count: 2044
list:
- accessibility: Open access
  additionDate: '2019-09-28T12:06:10Z'
  biotoolsCURIE: biotools:AMICI
  biotoolsID: AMICI
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: fabian_froehlich@hms.harvard.edu
    fundrefid: null
    gridid: null
    name: Fabian Fröhlich
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    - Developer
    url: null
  - email: jan.hasenauer@uni-bonn.de
    fundrefid: null
    gridid: null
    name: Jan Hasenauer
    note: null
    orcidid: https://orcid.org/0000-0002-4935-3312
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: daniel.weindl@uni-bonn.de
    fundrefid: null
    gridid: null
    name: Daniel Weindl
    note: null
    orcidid: https://orcid.org/0000-0001-9963-6057
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    - Developer
    url: null
  description: 'AMICI provides a multi-language (Python, C++, Matlab) interface for
    the SUNDIALS solvers CVODES (for ordinary differential equations) and IDAS (for
    algebraic differential equations). AMICI allows the user to read differential
    equation models specified as SBML or PySB and automatically compiles such models
    into .mex simulation files (Matlab), C++ executables or Python modules.


    Beyond forward integration, the compiled simulation file also allows for forward
    sensitivity analysis, steady state sensitivity analysis and adjoint sensitivity
    analysis for likelihood-based output functions.


    The interface was designed to provide routines for efficient gradient computation
    in parameter estimation of biochemical reaction models but it is also applicable
    to a wider range of differential equation constrained optimization problems.'
  documentation:
  - note: null
    type:
    - User manual
    url: https://amici.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: PEtab
        uri: http://edamontology.org/format_4015
    note: null
    operation:
    - term: Network simulation
      uri: http://edamontology.org/operation_3562
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output:
    - data:
        term: Trajectory data
        uri: http://edamontology.org/data_3870
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://github.com/AMICI-dev/AMICI
  homepage_status: 0
  language:
  - MATLAB
  - C++
  - Python
  lastUpdate: '2025-05-08T07:28:52.817175Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/AMICI-dev/AMICI/issues
  maturity: Mature
  name: AMICI
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: dweindl
  publication:
  - doi: 10.1093/bioinformatics/btab227
    metadata:
      abstract: Ordinary differential equation models facilitate the understanding
        of cellular signal transduction and other biological processes. However, for
        large and comprehensive models, the computational cost of simulating or calibrating
        can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB
        that provides efficient simulation and sensitivity analysis routines tailored
        for scalable, gradient-based parameter estimation and uncertainty quantification.
      authors:
      - name: Frohlich F.
      - name: Weindl D.
      - name: Schalte Y.
      - name: Pathirana D.
      - name: Paszkowski L.
      - name: Lines G.T.
      - name: Stapor P.
      - name: Hasenauer J.
      citationCount: 37
      date: '2021-10-15T00:00:00Z'
      journal: Bioinformatics
      title: 'AMICI: high-performance sensitivity analysis for large ordinary differential
        equation models'
    note: null
    pmcid: PMC8545331
    pmid: '33821950'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  validated: 1
  version: []
- accessibility: null
  additionDate: '2024-12-21T15:24:55.662209Z'
  biotoolsCURIE: biotools:stripepy
  biotoolsID: stripepy
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: StripePy recognizes architectural stripes in 3C and Hi-C contact maps
    using geometric reasoning
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/paulsengroup/StripePy/blob/main/README.md
  download:
  - note: null
    type: Source code
    url: https://github.com/paulsengroup/StripePy/releases
    version: null
  - note: null
    type: Container file
    url: https://hub.docker.com/r/paulsengroup/stripepy
    version: null
  - note: null
    type: Test data
    url: https://doi.org/10.5281/zenodo.14283921
    version: null
  - note: null
    type: Binaries
    url: https://pypi.org/project/stripepy-hic/
    version: null
  - note: null
    type: Binaries
    url: https://anaconda.org/bioconda/stripepy-hic
    version: null
  editPermission:
    authors:
    - rea1991
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/paulsengroup/StripePy
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-05-02T11:01:00.332596Z'
  license: MIT
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/paulsengroup/StripePy/issues
  - note: null
    type:
    - Discussion forum
    url: https://github.com/paulsengroup/StripePy/discussions
  - note: null
    type:
    - Repository
    url: https://github.com/paulsengroup/StripePy.git
  maturity: null
  name: StripePy
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: robomics
  publication:
  - doi: 10.1101/2024.12.20.629789
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 0.0.1
  - 0.0.2
  - 1.0.0
  - 1.1.0
- accessibility: Open access
  additionDate: '2019-08-09T13:16:33Z'
  biotoolsCURIE: biotools:pyOpenMS
  biotoolsID: pyOpenMS
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: hannes.rost@utoronto.ca
    fundrefid: null
    gridid: null
    name: Hannes L. Rost
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Python library for mass spectrometry, specifically for the analysis
    of proteomics and metabolomics data.
  documentation:
  - note: null
    type:
    - User manual
    url: https://pyopenms.readthedocs.io
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/OpenMS/pyopenms-extra/blob/master/docs/source/index.rst
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-04-30T11:39:29.549782Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/OpenMS/pyopenms-extra
  - note: null
    type:
    - Issue tracker
    url: https://github.com/OpenMS/pyopenms-extra/issues
  maturity: Mature
  name: pyOpenMS
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: samwein
  publication:
  - doi: 10.7287/PEERJ.PREPRINTS.27736
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-08-20T15:17:20Z'
  biotoolsCURIE: biotools:bracken
  biotoolsID: bracken
  collectionID:
  - Animal and Crop Genomics
  confidence_flag: null
  cost: null
  credit:
  - email: jlu26@jhmi.edu
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Statistical method that computes the abundance of species in DNA sequences
    from a metagenomics sample.
  documentation:
  - note: null
    type:
    - User manual
    url: https://ccb.jhu.edu/software/bracken/index.shtml?t=manual
  download: []
  editPermission:
    authors:
    - animalandcropgenomics
    - ELIXIR-CZ
    - bebatut
    - vashokan
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Statistical calculation
      uri: http://edamontology.org/operation_2238
    output: []
  homepage: https://ccb.jhu.edu/software/bracken/
  homepage_status: 0
  language:
  - Perl
  - Python
  lastUpdate: '2025-04-23T07:43:18.805990Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/jenniferlu717/Bracken
  - note: null
    type:
    - Issue tracker
    url: https://github.com/jenniferlu717/Bracken/issues
  maturity: null
  name: Bracken
  operatingSystem:
  - Linux
  otherID: []
  owner: admin
  publication:
  - doi: 10.7717/peerj-cs.104
    metadata:
      abstract: Metagenomic experiments attempt to characterize microbial communities
        using high-throughput DNA sequencing. Identification of the microorganisms
        in a sample provides information about the genetic profile, population structure,
        and role of microorganisms within an environment. Until recently, most metagenomics
        studies focused on high-level characterization at the level of phyla, or alternatively
        sequenced the 16S ribosomalRNAgene that is present in bacterial species. As
        the cost of sequencing has fallen, though, metagenomics experiments have increasingly
        used unbiased shotgun sequencing to capture all the organisms in a sample.
        This approach requires a method for estimating abundance directly from the
        raw read data. Here we describe a fast, accurate new method that computes
        the abundance at the species level using the reads collected in a metagenomics
        experiment. Bracken (Bayesian Reestimation of Abundance after Classification
        with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level
        classifier, along with information about the genomes themselves to estimate
        abundance at the species level, the genus level, or above. We demonstrate
        that Bracken can produce accurate species- and genus-level abundance estimates
        even when a sample contains multiple near-identical species.
      authors:
      - name: Lu J.
      - name: Breitwieser F.P.
      - name: Thielen P.
      - name: Salzberg S.L.
      citationCount: 900
      date: '2017-01-01T00:00:00Z'
      journal: PeerJ Computer Science
      title: 'Bracken: Estimating species abundance in metagenomics data'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  validated: 1
  version: []
- accessibility: Open access (with restrictions)
  additionDate: '2017-04-22T17:35:18Z'
  biotoolsCURIE: biotools:gemma
  biotoolsID: gemma
  collectionID: []
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: pavlab-support@msl.ubc.ca
    fundrefid: null
    gridid: null
    name: Pavlidis Lab Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Support
    url: https://pavlab.msl.ubc.ca/
  description: Gemma provides data, experimental design annotations, and differential
    expression analysis results for thousands of microarray and RNA-seq experiments.
    We re-analyze raw data from public sources (primarily NCBI GEO), annotate experimental
    conditions, conduct quality control and compute differential expression using
    standardized procedures. We have especially good coverage of experiments relevant
    to the nervous system.
  documentation:
  - note: null
    type:
    - General
    url: https://pavlidislab.github.io/Gemma/
  - note: Documentation for the Gemma REST API.
    type:
    - API documentation
    url: https://gemma.msl.ubc.ca/resources/restapidocs/
  download:
  - note: null
    type: Downloads page
    url: https://github.com/PavlidisLab/Gemma/releases
    version: null
  editPermission:
    authors:
    - artrymix
    - paulpavlidis
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      format: []
    - data:
        term: Raw microarray data
        uri: http://edamontology.org/data_3110
      format: []
    note: null
    operation:
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    output:
    - data:
        term: Heat map
        uri: http://edamontology.org/data_1636
      format: []
    - data:
        term: Quality control report
        uri: http://edamontology.org/data_3914
      format: []
  homepage: https://gemma.msl.ubc.ca/
  homepage_status: 0
  language:
  - Java
  - JavaScript
  lastUpdate: '2025-04-22T22:09:03.811075Z'
  license: CC-BY-NC-4.0
  link:
  - note: null
    type:
    - Repository
    - Issue tracker
    url: https://github.com/PavlidisLab/Gemma
  maturity: Mature
  name: Gemma
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: artrymix
  publication:
  - doi: 10.1093/bioinformatics/btp259
    metadata:
      abstract: 'Motivation: Many microarray datasets are available online with formalized
        standards describing the probe sequences and expression values. Unfortunately,
        the description, conditions and parameters of the experiments are less commonly
        formalized and often occur as natural language text. This hinders searching,
        high-throughput analysis, organization and integration of the datasets. Results:
        We use the lexical resources and software tools from the Unified Medical Language
        System (UMLS) to extract concepts from text. We then link the UMLS concepts
        to classes in open biomedical ontologies. The result is accessible and clear
        semantic annotations of gene expression experiments. We applied the method
        to 595 expression experiments from Gemma, a resource for re-use and meta-analysis
        of gene expression profiling data. We evaluated and corrected all stages of
        the annotation process. The majority of missed annotations were due to a lack
        of cross-references. The most error-prone stage was the extraction of concepts
        from phrases. Final review of the annotations in context of the experiments
        revealed 89% precision. A naive system, lacking the phrase to concept corrections
        is 68% precise. We have integrated this annotation pipeline into Gemma. ©
        2009 The Author(s).'
      authors:
      - name: French L.
      - name: Lane S.
      - name: Law T.
      - name: Xu L.
      - name: Pavlidis P.
      citationCount: 9
      date: '2009-06-09T00:00:00Z'
      journal: Bioinformatics
      title: Application and evaluation of automated semantic annotation of gene expression
        experiments
    note: null
    pmcid: PMC2687992
    pmid: '19376825'
    type:
    - Primary
    version: null
  - doi: 10.1093/database/baab006
    metadata:
      abstract: 'Vast amounts of transcriptomic data reside in public repositories,
        but effective reuse remains challenging. Issues include unstructured dataset
        metadata, inconsistent data processing and quality control, and inconsistent
        probe-gene mappings across microarray technologies. Thus, extensive curation
        and data reprocessing are necessary prior to any reuse. The Gemma bioinformatics
        system was created to help address these issues. Gemma consists of a database
        of curated transcriptomic datasets, analytical software, a web interface and
        web services. Here we present an update on Gemma''s holdings, data processing
        and analysis pipelines, our curation guidelines, and software features. As
        of June 2020, Gemma contains 10 811 manually curated datasets (primarily human,
        mouse and rat), over 395 000 samples and hundreds of curated transcriptomic
        platforms (both microarray and RNA sequencing). Dataset topics were represented
        with 10 215 distinct terms from 12 ontologies, for a total of 54 316 topic
        annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of
        conditions and tissues, it captures a large majority of available brain-related
        datasets, accounting for 34% of its holdings. Users can access the curated
        data and differential expression analyses through the Gemma website, RESTful
        service and an R package. Database URL: https://gemma.msl.ubc.ca/home.html'
      authors:
      - name: Lim N.
      - name: Tesar S.
      - name: Belmadani M.
      - name: Poirier-Morency G.
      - name: Mancarci B.O.
      - name: Sicherman J.
      - name: Jacobson M.
      - name: Leong J.
      - name: Tan P.
      - name: Pavlidis P.
      citationCount: 21
      date: '2021-01-01T00:00:00Z'
      journal: Database
      title: Curation of over 10 000 transcriptomic studies to enable data reuse
    note: null
    pmcid: PMC7904053
    pmid: '33599246'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: rsem
    type: uses
  - biotoolsID: multiqc
    type: uses
  - biotoolsID: cutadapt
    type: uses
  - biotoolsID: star
    type: uses
  - biotoolsID: ncbi_geo
    type: uses
  - biotoolsID: sradb
    type: uses
  - biotoolsID: ncbi_gene
    type: uses
  toolType:
  - Web application
  - Web API
  topic:
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  - term: Microarray experiment
    uri: http://edamontology.org/topic_3518
  - term: Data curation and archival
    uri: http://edamontology.org/topic_0219
  validated: 1
  version:
  - 1.31.13
- accessibility: null
  additionDate: '2024-04-18T08:31:46.459964Z'
  biotoolsCURIE: biotools:hictkpy
  biotoolsID: hictkpy
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: 'Python bindings for hictk: read and write .cool and .hic files directly
    from Python'
  documentation:
  - note: null
    type:
    - General
    url: https://hictkpy.readthedocs.io/en/stable/
  - note: null
    type:
    - Installation instructions
    url: https://hictkpy.readthedocs.io/en/stable/installation.html
  - note: null
    type:
    - Quick start guide
    url: https://hictkpy.readthedocs.io/en/stable/quickstart.html
  - note: null
    type:
    - API documentation
    url: https://hictkpy.readthedocs.io/en/stable/api/index.html
  download:
  - note: null
    type: Downloads page
    url: https://github.com/paulsengroup/hictkpy/releases
    version: null
  - note: null
    type: Binaries
    url: https://pypi.org/project/hictkpy/
    version: null
  - note: null
    type: Binaries
    url: https://anaconda.org/bioconda/hictkpy
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/paulsengroup/hictkpy
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-04-14T22:54:26.266145Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/paulsengroup/hictkpy
  - note: null
    type:
    - Issue tracker
    url: https://github.com/paulsengroup/hictkpy/issues
  - note: null
    type:
    - Helpdesk
    url: https://github.com/paulsengroup/hictkpy/discussions
  maturity: Mature
  name: hictkpy
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: robomics
  publication:
  - doi: 10.1093/bioinformatics/btae408
    metadata:
      abstract: 'Motivation: Hi-C is gaining prominence as a method for mapping genome
        organization. With declining sequencing costs and a growing demand for higher-resolution
        data, efficient tools for processing Hi-C datasets at different resolutions
        are crucial. Over the past decade, the. hic and Cooler file formats have become
        the de-facto standard to store interaction matrices produced by Hi-C experiments
        in binary format. Interoperability issues make it unnecessarily difficult
        to convert between the two formats and to develop applications that can process
        each format natively. Results: We developed hictk, a toolkit that can transparently
        operate on. hic and. cool files with excellent performance. The toolkit is
        written in C++ and consists of a C++ library with Python and R bindings as
        well as CLI tools to perform common operations directly from the shell, including
        converting between. hic and. mcool formats. We benchmark the performance of
        hictk and compare it with other popular tools and libraries. We conclude that
        hictk significantly outperforms existing tools while providing the flexibility
        of natively working with both file formats without code duplication.'
      authors:
      - name: Rossini R.
      - name: Paulsen J.
      citationCount: 0
      date: '2024-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'hictk: blazing fast toolkit to work with. hic and. cool files'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: hictk
    type: uses
  toolType:
  - Library
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - 0.0.1
  - 0.0.2
  - 0.0.3
  - 0.0.4
  - 0.0.5
  - 1.0.0
  - 1.1.0
  - 1.2.0
- accessibility: null
  additionDate: '2024-02-02T14:42:46.030561Z'
  biotoolsCURIE: biotools:hictk
  biotoolsID: hictk
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Blazing fast toolkit to work with .hic and .cool files
  documentation:
  - note: null
    type:
    - User manual
    url: https://hictk.readthedocs.io/en/stable/index.html
  download:
  - note: null
    type: Downloads page
    url: https://github.com/paulsengroup/hictk/releases
    version: null
  - note: null
    type: Binaries
    url: https://anaconda.org/bioconda/hictk
    version: null
  - note: null
    type: Container file
    url: https://github.com/paulsengroup/hictk/pkgs/container/hictk
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/paulsengroup/hictk
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-14T22:54:16.319235Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/paulsengroup/hictk
  - note: null
    type:
    - Issue tracker
    url: https://github.com/paulsengroup/hictk/issues
  - note: null
    type:
    - Helpdesk
    url: https://github.com/paulsengroup/hictk/discussions
  maturity: Mature
  name: hictk
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: robomics
  publication:
  - doi: 10.1093/bioinformatics/btae408
    metadata:
      abstract: 'Motivation: Hi-C is gaining prominence as a method for mapping genome
        organization. With declining sequencing costs and a growing demand for higher-resolution
        data, efficient tools for processing Hi-C datasets at different resolutions
        are crucial. Over the past decade, the. hic and Cooler file formats have become
        the de-facto standard to store interaction matrices produced by Hi-C experiments
        in binary format. Interoperability issues make it unnecessarily difficult
        to convert between the two formats and to develop applications that can process
        each format natively. Results: We developed hictk, a toolkit that can transparently
        operate on. hic and. cool files with excellent performance. The toolkit is
        written in C++ and consists of a C++ library with Python and R bindings as
        well as CLI tools to perform common operations directly from the shell, including
        converting between. hic and. mcool formats. We benchmark the performance of
        hictk and compare it with other popular tools and libraries. We conclude that
        hictk significantly outperforms existing tools while providing the flexibility
        of natively working with both file formats without code duplication.'
      authors:
      - name: Rossini R.
      - name: Paulsen J.
      citationCount: 0
      date: '2024-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'hictk: blazing fast toolkit to work with. hic and. cool files'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: hictkpy
    type: usedBy
  - biotoolsID: hictkr
    type: usedBy
  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - 0.0.1
  - 0.0.2
  - 0.0.3
  - 0.0.4
  - 0.0.5
  - 0.0.6
  - 0.0.7
  - 0.0.8
  - 0.0.9
  - 0.0.10
  - 0.0.11
  - 0.0.12
  - 1.0.0
  - 2.0.0
  - 2.0.1
  - 2.0.2
  - 2.1.0
  - 2.1.1
- accessibility: Open access
  additionDate: '2019-08-26T14:34:55Z'
  biotoolsCURIE: biotools:MINERVA_Platform
  biotoolsID: MINERVA_Platform
  collectionID:
  - ELIXIR-LU
  - LCSB
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: 'The MINERVA (Molecular Interaction NEtwoRk VisuAlization) platform
    is a standalone webserver for visualization, exploration and management of molecular
    networks encoded in SBGN-compliant format, including files produced using CellDesigner
    or SBGN editors. Visualization of uploaded networks generated by the platform
    is accessible via a web browser to all viewers with the weblink to the resource.


    The MINERVA Platform is a webservice using the Java Server Faces 2 technology.
    The server side, including data parsing, integration, annotation and verification,
    is implemented in Java. The platform uses the Postgres SQL database for data storage
    and the Hibernate framework as a middle layer between web server and database.
    The user web-interface is generated using React.js. The displayed content is visualized
    by OpenLayers API, dedicated JavaScript and CSS.'
  documentation:
  - note: null
    type:
    - Quick start guide
    - Release notes
    - User manual
    - API documentation
    - Citation instructions
    - Terms of use
    url: https://minerva.uni.lu
  download:
  - note: Installation instructions, including debian package, virtual machine images
      and docker containers.
    type: Other
    url: https://minerva.pages.uni.lu/doc/install/
    version: 13.1.3 - 18.1.1
  editPermission:
    authors:
    - sascha.herzinger
    type: group
  elixirCommunity: []
  elixirNode:
  - Luxembourg
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Pathway visualisation
      uri: http://edamontology.org/operation_3926
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Expression data visualisation
      uri: http://edamontology.org/operation_0571
    output: []
  homepage: https://minerva.uni.lu
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-14T09:01:54.711218Z'
  license: AGPL-3.0
  link:
  - note: GiLab repository for core functionalities (data and format handling, service
      stability, API access)
    type:
    - Repository
    url: https://gitlab.lcsb.uni.lu/minerva/core/
  - note: Issue tracker for core functionalities (data and format handling, service
      stability, API access)
    type:
    - Issue tracker
    url: https://gitlab.lcsb.uni.lu/minerva/core/-/issues
  - note: GiLab repository for frontend functionalities
    type:
    - Repository
    url: https://gitlab.lcsb.uni.lu/minerva/frontend
  - note: Issue tracker for frontend functionalities
    type:
    - Issue tracker
    url: https://gitlab.lcsb.uni.lu/minerva/frontend/-/issues
  maturity: Mature
  name: The MINERVA Platform
  operatingSystem: []
  otherID: []
  owner: mjostaszewski
  publication:
  - doi: 10.1038/npjsba.2016.20
    metadata:
      abstract: Our growing knowledge about various molecular mechanisms is becoming
        increasingly more structured and accessible. Different repositories of molecular
        interactions and available literature enable construction of focused and high-quality
        molecular interaction networks. Novel tools for curation and exploration of
        such networks are needed, in order to foster the development of a systems
        biology environment. In particular, solutions for visualization, annotation
        and data cross-linking will facilitate usage of network-encoded knowledge
        in biomedical research. To this end we developed the MINERVA (Molecular Interaction
        NEtwoRks VisuAlization) platform, a standalone webservice supporting curation,
        annotation and visualization of molecular interaction networks in Systems
        Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated
        content annotation and verification for improved quality control. The end
        users can explore and interact with hosted networks, and provide direct feedback
        to content curators. MINERVA enables mapping drug targets or overlaying experimental
        data on the visualized networks. Extensive export functions enable downloading
        areas of the visualized networks as SBGN-compliant models for efficient reuse
        of hosted networks. The software is available under Affero GPL 3.0 as a Virtual
        Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/.
        We believe that MINERVA is an important contribution to systems biology community,
        as its architecture enables set-up of locally or globally accessible SBGN-oriented
        repositories of molecular interaction networks. Its functionalities allow
        overlay of multiple information layers, facilitating exploration of content
        and interpretation of data. Moreover, annotation and verification workflows
        of MINERVA improve the efficiency of curation of networks, allowing life-science
        researchers to better engage in development and use of biomedical knowledge
        repositories.
      authors:
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Satagopam V.
      - name: Gebel S.
      - name: Mazein A.
      - name: Kuzma M.
      - name: Zorzan S.
      - name: McGee F.
      - name: Otjacques B.
      - name: Balling R.
      - name: Schneider R.
      citationCount: 60
      date: '2016-01-01T00:00:00Z'
      journal: npj Systems Biology and Applications
      title: MINERVA—A platform for visualization and curation of molecular interaction
        networks
    note: null
    pmcid: PMC5516855
    pmid: '28725475'
    type:
    - Primary
    version: '10.0'
  - doi: 10.1093/bioinformatics/btz286
    metadata:
      abstract: 'Summary: The complexity of molecular networks makes them difficult
        to navigate and interpret, creating a need for specialized software. MINERVA
        is a web platform for visualization, exploration and management of molecular
        networks. Here, we introduce an extension to MINERVA architecture that greatly
        facilitates the access and use of the stored molecular network data. It allows
        to incorporate such data in analytical pipelines via a programmatic access
        interface, and to extend the platform''s visual exploration and analytics
        functionality via plugin architecture. This is possible for any molecular
        network hosted by the MINERVA platform encoded in well-recognized systems
        biology formats. To showcase the possibilities of the plugin architecture,
        we have developed several plugins extending the MINERVA core functionalities.
        In the article, we demonstrate the plugins for interactive tree traversal
        of molecular networks, for enrichment analysis and for mapping and visualization
        of known disease variants or known adverse drug reactions to molecules in
        the network. Availability and implementation: Plugins developed and maintained
        by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/.
        The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.'
      authors:
      - name: Hoksza D.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Smula E.
      - name: Schneider R.
      citationCount: 24
      date: '2019-11-01T00:00:00Z'
      journal: Bioinformatics
      title: 'MINERVA API and plugins: Opening molecular network analysis and visualization
        to the community'
    note: null
    pmcid: PMC6821317
    pmid: '31074494'
    type:
    - Primary
    version: 12.2.3
  - doi: 10.1093/bib/bbz067
    metadata:
      abstract: The understanding of complex biological networks often relies on both
        a dedicated layout and a topology. Currently, there are three major competing
        layout-aware systems biology formats, but there are no software tools or software
        libraries supporting all of them. This complicates the management of molecular
        network layouts and hinders their reuse and extension. In this paper, we present
        a high-level overview of the layout formats in systems biology, focusing on
        their commonalities and differences, review their support in existing software
        tools, libraries and repositories and finally introduce a new conversion module
        within the MINERVA platform. The module is available via a REST API and offers,
        besides the ability to convert between layout-aware systems biology formats,
        the possibility to export layouts into several graphical formats. The module
        enables conversion of very large networks with thousands of elements, such
        as disease maps or metabolic reconstructions, rendering it widely applicable
        in systems biology.
      authors:
      - name: Hoksza D.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Hasenauer J.
      - name: Schneider R.
      citationCount: 15
      date: '2019-07-10T00:00:00Z'
      journal: Briefings in Bioinformatics
      title: Closing the gap between formats for storing layout information in systems
        biology
    note: null
    pmcid: PMC7373180
    pmid: '31273380'
    type:
    - Primary
    version: 13.1.1
  - doi: 10.1089/big.2015.0057
    metadata:
      abstract: Translational medicine is a domain turning results of basic life science
        research into new tools and methods in a clinical environment, for example,
        as new diagnostics or therapies. Nowadays, the process of translation is supported
        by large amounts of heterogeneous data ranging from medical data to a whole
        range of -omics data. It is not only a great opportunity but also a great
        challenge, as translational medicine big data is difficult to integrate and
        analyze, and requires the involvement of biomedical experts for the data processing.
        We show here that visualization and interoperable workflows, combining multiple
        complex steps, can address at least parts of the challenge. In this article,
        we present an integrated workflow for exploring, analysis, and interpretation
        of translational medicine data in the context of human health. Three Web services
        - tranSMART, a Galaxy Server, and a MINERVA platform - are combined into one
        big data pipeline. Native visualization capabilities enable the biomedical
        experts to get a comprehensive overview and control over separate steps of
        the workflow. The capabilities of tranSMART enable a flexible filtering of
        multidimensional integrated data sets to create subsets suitable for downstream
        processing. A Galaxy Server offers visually aided construction of analytical
        pipelines, with the use of existing or custom components. A MINERVA platform
        supports the exploration of health and disease-related mechanisms in a contextualized
        analytical visualization system. We demonstrate the utility of our workflow
        by illustrating its subsequent steps using an existing data set, for which
        we propose a filtering scheme, an analytical pipeline, and a corresponding
        visualization of analytical results. The workflow is available as a sandbox
        environment, where readers can work with the described setup themselves. Overall,
        our work shows how visualization and interfacing of big data processing services
        facilitate exploration, analysis, and interpretation of translational medicine
        data.
      authors:
      - name: Satagopam V.
      - name: Gu W.
      - name: Eifes S.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Gebel S.
      - name: Barbosa-Silva A.
      - name: Balling R.
      - name: Schneider R.
      citationCount: 38
      date: '2016-06-01T00:00:00Z'
      journal: Big Data
      title: Integration and Visualization of Translational Medicine Data for Better
        Understanding of Human Diseases
    note: null
    pmcid: PMC4932659
    pmid: '27441714'
    type:
    - Usage
    version: '10.0'
  - doi: 10.1016/j.envpol.2019.04.005
    metadata:
      abstract: 'Polychlorinated biphenyls (PCBs) are a class of widespread environmental
        pollutants, commonly found in human blood, that have been suggested to be
        linked to the occurrence of sporadic Parkinson''s disease (PD). It has been
        reported that some non-coplanar PCBs accumulate in the brains of female PD
        patients. To improve our understanding of the association between PCB exposure
        and PD risk we have applied whole transcriptome gene expression analysis in
        blood cells from 594 PCB-exposed subjects (369 female, 225 male). Interestingly,
        we observe that in females, blood levels of non-coplanar PCBs appear to be
        associated with expression levels of PD-specific genes. However, no such association
        was detected in males. Among the 131 PD-specific genes affected, 39 have been
        shown to display similar changes in expression levels in the substantia nigra
        of deceased PD patients. Especially among the down-regulated genes, transcripts
        of genes involved in neurotransmitter vesicle-related functions were predominant.
        Capsule: Plasma PCB levels are associated with gene expression changes in
        females only, resulting in brain-related genes changing in blood cells of
        healthy individuals exposed to PCBs.'
      authors:
      - name: Bohler S.
      - name: Krauskopf J.
      - name: Espin-Perez A.
      - name: Gebel S.
      - name: Palli D.
      - name: Rantakokko P.
      - name: Kiviranta H.
      - name: Kyrtopoulos S.A.
      - name: Balling R.
      - name: Kleinjans J.
      citationCount: 5
      date: '2019-07-01T00:00:00Z'
      journal: Environmental Pollution
      title: Genes associated with Parkinson's disease respond to increasing polychlorinated
        biphenyl levels in the blood of healthy females
    note: null
    pmcid: null
    pmid: '30991279'
    type: []
    version: 13.1.1
  relation:
  - biotoolsID: pathvisio
    type: uses
  - biotoolsID: sbgn
    type: uses
  - biotoolsID: libsbml
    type: uses
  toolType: []
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Omics
    uri: http://edamontology.org/topic_3391
  - term: Translational medicine
    uri: http://edamontology.org/topic_3342
  validated: 0
  version:
  - 13.1.3
  - 13.2.0
  - 14.0.13
  - 15.0.3
  - 16.4.0
  - 17.1.3
  - 18.1.1
- accessibility: Open access
  additionDate: '2025-04-11T13:56:02.037873Z'
  biotoolsCURIE: biotools:pr2-reference
  biotoolsID: pr2-reference
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: The PR2 reference sequence database was initiated in 2010 in the frame
    of the BioMarks project from work that had developed in the previous ten years
    in the Plankton Group of the Station Biologique of Roscoff. It aims to provide
    a reference database of carefully annotated 18S rRNA sequences using nine unique
    taxonomic fields (from domain to species). At present, it contains over 240,000
    sequences. Although it focuses on protists, it also contains sequences from metazoa,
    fungi and plants as well a limited set of 16S sequences from plastids and bacteria.
    Several metadata fields are available for many sequences, including geo-localisation,
    whether it originates from a culture or a natural sample, host type etc… The annotation
    of PR2 is performed by experts from each taxonomic groups.
  documentation:
  - note: null
    type:
    - User manual
    url: https://pr2database.github.io/pr2database/articles/pr2database.html
  download:
  - note: null
    type: Downloads page
    url: https://github.com/pr2database/pr2database/releases
    version: 5.1.0
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://app.pr2-database.org/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-04-11T14:01:09.249274Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/pr2database/pr2database/releases
  - note: null
    type:
    - Issue tracker
    url: https://github.com/pr2database/pr2database/issues
  - note: null
    type:
    - Other
    url: https://pr2-database.org/
  maturity: Mature
  name: Protists Ribosomal Reference Database
  operatingSystem: []
  otherID: []
  owner: vaulot
  publication:
  - doi: 10.1093/nar/gks1160
    metadata:
      abstract: The interrogation of genetic markers in environmental meta-barcoding
        studies is currently seriously hindered by the lack of taxonomically curated
        reference data sets for the targeted genes. The Protist Ribosomal Reference
        database (PR2, http://ssu-rrna.org/) provides a unique access to eukaryotic
        small sub-unit (SSU) ribosomal RNA and DNA sequences, with curated taxonomy.
        The database mainly consists of nuclear-encoded protistan sequences. However,
        metazoans, land plants, macrosporic fungi and eukaryotic organelles (mitochondrion,
        plastid and others) are also included because they are useful for the analysis
        of high-troughput sequencing data sets. Introns and putative chimeric sequences
        have been also carefully checked. Taxonomic assignation of sequences consists
        of eight unique taxonomic fields. In total, 136866 sequences are nuclear encoded,
        45708 (36 501 mitochondrial and 9657 chloroplastic) are from organelles, the
        remaining being putative chimeric sequences. The website allows the users
        to download sequences from the entire and partial databases (including representative
        sequences after clustering at a given level of similarity). Different web
        tools also allow searches by sequence similarity. The presence of both rRNA
        and rDNA sequences, taking into account introns (crucial for eukaryotic sequences),
        a normalized eight terms ranked-taxonomy and updates of new GenBank releases
        were made possible by a long-term collaboration between experts in taxonomy
        and computer scientists. © The Author(s) 2012.
      authors:
      - name: Guillou L.
      - name: Bachar D.
      - name: Audic S.
      - name: Bass D.
      - name: Berney C.
      - name: Bittner L.
      - name: Boutte C.
      - name: Burgaud G.
      - name: De Vargas C.
      - name: Decelle J.
      - name: Del Campo J.
      - name: Dolan J.R.
      - name: Dunthorn M.
      - name: Edvardsen B.
      - name: Holzmann M.
      - name: Kooistra W.H.C.F.
      - name: Lara E.
      - name: Le Bescot N.
      - name: Logares R.
      - name: Mahe F.
      - name: Massana R.
      - name: Montresor M.
      - name: Morard R.
      - name: Not F.
      - name: Pawlowski J.
      - name: Probert I.
      - name: Sauvadet A.-L.
      - name: Siano R.
      - name: Stoeck T.
      - name: Vaulot D.
      - name: Zimmermann P.
      - name: Christen R.
      citationCount: 1456
      date: '2013-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The Protist Ribosomal Reference database (PR2): A catalog of unicellular
        eukaryote Small Sub-Unit rRNA sequences with curated taxonomy'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Database portal
  - Web application
  topic:
  - term: Metabarcoding
    uri: http://edamontology.org/topic_4038
  validated: 0
  version:
  - 5.1.0
- accessibility: Open access
  additionDate: '2025-02-28T15:04:33.594183Z'
  biotoolsCURIE: biotools:prolfquapp
  biotoolsID: prolfquapp
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: A command-line tool for differential expression analysis in quantitative
    proteomics
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/prolfqua/prolfquapp/blob/master/README.md
  download:
  - note: null
    type: Downloads page
    url: https://github.com/prolfqua/prolfquapp/releases/tag/0.1.6
    version: 0.1.6
  editPermission:
    authors:
    - thatmariia
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: './prolfqua_dea.sh -i data_dir/ -d annotation.xlsx -y config.yaml -w NameOfAnalysis
      -s DIANN

      # and again you run the version within the docker container with

      # ./prolfquapp_docker.sh prolfqua_dea.sh -i data_dir/ -d annotation.xlsx -y
      config.yaml -w NameOfAnalysis -s DIANN'
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Differential protein expression profiling
      uri: http://edamontology.org/operation_3741
    output:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: xlsx
        uri: http://edamontology.org/format_3620
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: xlsx
        uri: http://edamontology.org/format_3620
    note: null
    operation:
    - term: Validation
      uri: http://edamontology.org/operation_2428
    output:
    - data:
        term: Quality control report
        uri: http://edamontology.org/data_3914
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Expression data visualisation
      uri: http://edamontology.org/operation_0571
    output:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: PDF
        uri: http://edamontology.org/format_3508
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://github.com/prolfqua/prolfquapp
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-28T10:18:25.715685Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/prolfqua/prolfquapp
  - note: null
    type:
    - Issue tracker
    url: https://github.com/prolfqua/prolfquapp/issues
  maturity: Emerging
  name: prolfquapp
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.1021/acs.jproteome.4c00911
    metadata:
      abstract: Mass spectrometry is a cornerstone of quantitative proteomics, enabling
        relative protein quantification and differential expression analysis (DEA)
        of proteins. As experiments grow in complexity, involving more samples, groups,
        and identified proteins, interactive differential expression analysis tools
        become impractical. The prolfquapp addresses this challenge by providing a
        command-line interface that simplifies DEA, making it accessible to nonprogrammers
        and seamlessly integrating it into workflow management systems. Prolfquapp
        streamlines data processing and result visualization by generating dynamic
        HTML reports that facilitate the exploration of differential expression results.
        These reports allow for investigating complex experiments, such as those involving
        repeated measurements or multiple explanatory variables. Additionally, prolfquapp
        supports various output formats, including XLSX files, SummarizedExperiment
        objects and rank files, for further interactive analysis using spreadsheet
        software, the exploreDE Shiny application, or gene set enrichment analysis
        software, respectively. By leveraging advanced statistical models from the
        prolfqua R package, prolfquapp offers a user-friendly, integrated solution
        for large-scale quantitative proteomics studies, combining efficient data
        processing with insightful, publication-ready outputs.
      authors:
      - name: Wolski W.E.
      - name: Grossmann J.
      - name: Schwarz L.
      - name: Leary P.
      - name: Turker C.
      - name: Nanni P.
      - name: Schlapbach R.
      - name: Panse C.
      citationCount: 0
      date: '2025-02-07T00:00:00Z'
      journal: Journal of Proteome Research
      title: prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential
        Expression Analysis in Quantitative Proteomics
    note: null
    pmcid: null
    pmid: null
    type: []
    version: 0.0.6
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version:
  - 0.1.6
- accessibility: Open access
  additionDate: '2025-03-25T09:59:44.309666Z'
  biotoolsCURIE: biotools:rocrate-validator
  biotoolsID: rocrate-validator
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: marcoenrico.piras@crs4.it
    fundrefid: null
    gridid: null
    name: Marco Enrico Piras
    note: null
    orcidid: https://orcid.org/0000-0002-5207-0030
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    - Primary contact
    - Developer
    url: null
  - email: simone.leo@crs4.it
    fundrefid: null
    gridid: null
    name: Simone Leo
    note: null
    orcidid: https://orcid.org/0000-0001-8271-5429
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    - Developer
    url: null
  - email: luca.pireddu@crs4.it
    fundrefid: null
    gridid: null
    name: Luca Pireddu
    note: null
    orcidid: https://orcid.org/0000-0002-4663-5613
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: CRS4
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole: []
    url: https://www.crs4.it
  description: rocrate-validator is a tool and Python package to validate RO-Crates
    against different profiles, including the base RO-Crate profile and various extensions.
  documentation:
  - note: null
    type:
    - API documentation
    - Command-line options
    - Installation instructions
    - Quick start guide
    url: https://rocrate-validator.readthedocs.io/
  download:
  - note: null
    type: Downloads page
    url: https://github.com/crs4/rocrate-validator/releases
    version: null
  - note: null
    type: Downloads page
    url: https://pypi.org/project/roc-validator/#files
    version: null
  editPermission:
    authors:
    - mep
    - ilveroluca
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: '`rocrate-validator validate <path_to_rocrate>`'
    input: []
    note: Validate compliance of RO-Crate objects with specifications
    operation:
    - term: Format validation
      uri: http://edamontology.org/operation_0336
    output: []
  homepage: https://rocrate-validator.readthedocs.io/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-03-25T12:19:18.196376Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/crs4/rocrate-validator
  - note: null
    type:
    - Issue tracker
    url: https://github.com/crs4/rocrate-validator/issues
  maturity: Emerging
  name: rocrate-validator
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: mep
  publication: []
  relation:
  - biotoolsID: lifemonitor
    type: usedBy
  toolType:
  - Command-line tool
  - Library
  topic:
  - term: Data quality management
    uri: http://edamontology.org/topic_3572
  - term: FAIR data
    uri: http://edamontology.org/topic_4012
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Data management
    uri: http://edamontology.org/topic_3071
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-25T12:17:42.278603Z'
  biotoolsCURIE: biotools:lifemonitor
  biotoolsID: lifemonitor
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: marcoenrico.piras@crs4.it
    fundrefid: null
    gridid: null
    name: Marco Enrico Piras
    note: null
    orcidid: https://orcid.org/0000-0002-5207-0030
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: null
  - email: simone.leo@crs4.it
    fundrefid: null
    gridid: null
    name: Simone Leo
    note: null
    orcidid: https://orcid.org/0000-0001-8271-5429
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    - Developer
    url: null
  - email: luca.pireddu@crs4.it
    fundrefid: null
    gridid: null
    name: Luca Pireddu
    note: null
    orcidid: https://orcid.org/0000-0002-4663-5613
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: CRS4
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole: []
    url: https://www.crs4.it
  - email: null
    fundrefid: null
    gridid: null
    name: EOSC-Life
    note: null
    orcidid: null
    rorid: null
    typeEntity: Consortium
    typeRole: []
    url: https://www.eosc-life.eu/
  description: 'LifeMonitor is a service to support the sustainability and reusability
    of published computational workflows. It provides:

    * periodic testing;

    * test monitoring and notification;

    * automated checks and templates for workflow best practices.'
  documentation:
  - note: null
    type:
    - User manual
    url: https://lifemonitor.eu
  - note: null
    type:
    - API documentation
    url: https://api.lifemonitor.eu/openapi.html
  download: []
  editPermission:
    authors:
    - mep
    - ilveroluca
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://lifemonitor.eu
  homepage_status: 0
  language:
  - Python
  - JavaScript
  lastUpdate: '2025-03-25T12:17:42.289116Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/crs4/life_monitor
  - note: REST API (production instance)
    type:
    - Service
    url: https://api.lifemonitor.eu
  - note: REST API (dev instance)
    type:
    - Service
    url: https://api.dev/lifemonitor.eu
  - note: Web App (production instance)
    type:
    - Service
    url: https://app.lifemonitor.eu
  - note: Web App (dev instance)
    type:
    - Service
    url: https://app.dev.lifemonitor.eu
  - note: null
    type:
    - Issue tracker
    url: https://github.com/crs4/life_monitor/issues
  - note: GitHub App
    type:
    - Service
    url: https://github.com/apps/lifemonitor
  maturity: Mature
  name: LifeMonitor
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: mep
  publication: []
  relation:
  - biotoolsID: rocrate-validator
    type: uses
  toolType:
  - Web application
  - Web API
  - Web service
  topic:
  - term: Software engineering
    uri: http://edamontology.org/topic_3372
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Open science
    uri: http://edamontology.org/topic_4010
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-12T15:38:32.218141Z'
  biotoolsCURIE: biotools:gtestimate
  biotoolsID: gtestimate
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: GTestimate is a scRNA-seq normalization method. In contrast to other
    methods it uses the Simple Good-Turing estimator for the per cell relative gene
    expression estimation.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/Martin-Fahrenberger/GTestimate
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: GTestimate()
    input:
    - data:
        term: Count matrix
        uri: http://edamontology.org/data_3917
      format: []
    note: GTestimate is provided as an R-package containing the GTestimate() function.
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output:
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      format: []
  homepage: https://github.com/Martin-Fahrenberger/GTestimate
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-12T15:38:32.221353Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    - Repository
    url: https://github.com/Martin-Fahrenberger/GTestimate
  maturity: Mature
  name: GTestimate
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: martin_fahrenberger
  publication:
  - doi: 10.1101/2024.07.02.601501
    metadata: null
    note: preprint version 2
    pmcid: null
    pmid: null
    type:
    - Method
    version: '2'
  relation: []
  toolType:
  - Library
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-03T14:20:54.340271Z'
  biotoolsCURIE: biotools:coxmos
  biotoolsID: coxmos
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: pedrosalguerog@gmail.com
    fundrefid: null
    gridid: null
    name: Pedro Salguero García
    note: null
    orcidid: https://orcid.org/0000-0002-1879-3374
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    - Developer
    url: null
  description: The Coxmos (Cox MultiBlock Survival) R package is an end-to-end pipeline
    designed for the study of survival analysis for high dimensional data. Updating
    classical methods and adding new ones based on sPLS technologies. Furthermore,
    includes multiblock functions to work with multiple sets of information to improve
    survival accuracy.
  documentation:
  - note: null
    type:
    - User manual
    url: https://cran.r-project.org/web/packages/Coxmos/Coxmos.pdf
  - note: Single-Omic Analysis
    type:
    - Quick start guide
    url: https://cran.r-project.org/web/packages/Coxmos/vignettes/Coxmos-pipeline.html
  - note: Multi-Omic Analysis
    type:
    - Quick start guide
    url: https://cran.r-project.org/web/packages/Coxmos/vignettes/Coxmos-MO-pipeline.html
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://CRAN.R-project.org/package=Coxmos
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-04T15:20:02.818403Z'
  license: CC-BY-4.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/BiostatOmics/Coxmos/issues
  - note: null
    type:
    - Repository
    url: https://github.com/BiostatOmics/Coxmos
  maturity: Emerging
  name: Coxmos
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: biostatomics1
  publication: []
  relation: []
  toolType:
  - Library
  topic:
  - term: Multiomics
    uri: http://edamontology.org/topic_4021
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 1.1.1
- accessibility: Open access
  additionDate: '2025-03-03T15:06:10.867996Z'
  biotoolsCURIE: biotools:multipower
  biotoolsID: multipower
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: sotacam@eio.upv.es
    fundrefid: null
    gridid: null
    name: Sonia Tarazona
    note: null
    orcidid: https://orcid.org/0000-0001-5346-1407
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    url: null
  description: The MultiPower R method performs statistical power studies for multi-omics
    experiments, and is designed to assist users in experimental design as well as
    in the evaluation of already-generated multi-omics datasets.
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/ConesaLab/MultiPower/blob/master/MultiPowerUsersGuide_v2.pdf
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Power test
      uri: http://edamontology.org/operation_4031
    output: []
  homepage: https://github.com/ConesaLab/MultiPower
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-04T15:16:47.048391Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/ConesaLab/MultiPower
  - note: null
    type:
    - Issue tracker
    url: https://github.com/ConesaLab/MultiPower/issues
  maturity: Emerging
  name: MultiPower
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: biostatomics1
  publication:
  - doi: 10.1038/s41467-020-16937-8
    metadata:
      abstract: Multi-omic studies combine measurements at different molecular levels
        to build comprehensive models of cellular systems. The success of a multi-omic
        data analysis strategy depends largely on the adoption of adequate experimental
        designs, and on the quality of the measurements provided by the different
        omic platforms. However, the field lacks a comparative description of performance
        parameters across omic technologies and a formulation for experimental design
        in multi-omic data scenarios. Here, we propose a set of harmonized Figures
        of Merit (FoM) as quality descriptors applicable to different omic data types.
        Employing this information, we formulate the MultiPower method to estimate
        and assess the optimal sample size in a multi-omics experiment. MultiPower
        supports different experimental settings, data types and sample sizes, and
        includes graphical for experimental design decision-making. MultiPower is
        complemented with MultiML, an algorithm to estimate sample size for machine
        learning classification problems based on multi-omic data.
      authors:
      - name: Tarazona S.
      - name: Balzano-Nogueira L.
      - name: Gomez-Cabrero D.
      - name: Schmidt A.
      - name: Imhof A.
      - name: Hankemeier T.
      - name: Tegner J.
      - name: Westerhuis J.A.
      - name: Conesa A.
      citationCount: 54
      date: '2020-12-01T00:00:00Z'
      journal: Nature Communications
      title: Harmonization of quality metrics and power calculation in multi-omic
        studies
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Multiomics
    uri: http://edamontology.org/topic_4021
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2025-03-03T13:46:56.613465Z'
  biotoolsCURIE: biotools:mosim
  biotoolsID: mosim
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: sotacam@eio.upv.es
    fundrefid: null
    gridid: null
    name: Sonia Tarazona
    note: null
    orcidid: https://orcid.org/0000-0001-5346-1407
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    url: null
  description: MOSim (Multi-Omics Simulation) package simulates multi-omic experiments
    that mimic regulatory mechanisms within the cell, allowing flexible experimental
    design including time course and multiple groups.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output: []
  homepage: https://bioconductor.org/packages/release/bioc/html/MOSim.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-04T15:12:34.696650Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/ConesaLab/MOSim
  - note: null
    type:
    - Issue tracker
    url: https://github.com/ConesaLab/MOSim/issues
  maturity: Emerging
  name: MOSim
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: biostatomics1
  publication:
  - doi: 10.1101/421834
    metadata: null
    note: This is a preprint accesible at bioRxiv. When the paper is published it
      will be updated
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Multiomics
    uri: http://edamontology.org/topic_4021
  - term: Simulation experiment
    uri: http://edamontology.org/topic_3524
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 2.2.0
- accessibility: Open access
  additionDate: '2017-01-17T15:07:47Z'
  biotoolsCURIE: biotools:copasi
  biotoolsID: copasi
  collectionID:
  - de.NBI
  - EBI Training Tools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://copasi.org/About/Team/
  - email: frank.bergmann@bioquant.uni-heidelberg.de
    fundrefid: null
    gridid: null
    name: Frank T. Bergmann
    note: null
    orcidid: https://orcid.org/0000-0001-5553-4702
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Open-source software application for creating and solving mathematical
    models of biological processes such as metabolic networks, cell-signaling pathways,
    regulatory networks, infectious diseases, and many others. It includes features
    to define models of biological processes, simulate and analyze these models, generate
    analysis reports, and import/export models in SBML format.
  documentation:
  - note: null
    type:
    - User manual
    url: http://copasi.org/Support/User_Manual/
  download:
  - note: Source and binary packages are available for download.
    type: Binaries
    url: http://copasi.org/Download/
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: CopasiML
        uri: http://edamontology.org/format_3239
      - term: SED-ML
        uri: http://edamontology.org/format_3685
      - term: COMBINE OMEX
        uri: http://edamontology.org/format_3686
    note: null
    operation:
    - term: Network simulation
      uri: http://edamontology.org/operation_3562
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    - term: Metabolic network modelling
      uri: http://edamontology.org/operation_3660
    - term: Pathway visualisation
      uri: http://edamontology.org/operation_3926
    output:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: CopasiML
        uri: http://edamontology.org/format_3239
      - term: SED-ML
        uri: http://edamontology.org/format_3685
      - term: COMBINE OMEX
        uri: http://edamontology.org/format_3686
  homepage: http://copasi.org/
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2025-02-26T13:59:11.356816Z'
  license: Artistic-2.0
  link:
  - note: User Forum
    type:
    - Discussion forum
    url: https://groups.google.com/g/copasi-user-forum
  - note: Issue tracker
    type:
    - Issue tracker
    url: http://tracker.copasi.org/
  - note: Github Repo
    type:
    - Repository
    url: https://github.com/copasi/COPASI
  - note: null
    type:
    - Social media
    url: https://fosstodon.org/@copasi
  maturity: Mature
  name: COPASI
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: frankbergmann
  publication:
  - doi: 10.1093/bioinformatics/btl485
    metadata:
      abstract: 'Motivation: Simulation and modeling is becoming a standard approach
        to understand complex biochemical processes. Therefore, there is a big need
        for software tools that allow access to diverse simulation and modeling methods
        as well as support for the usage of these methods. Results: Here, we present
        COPASI, a platform-independent and user-friendly biochemical simulator that
        offers several unique features. We discuss numerical issues with these features;
        in particular, the criteria to switch between stochastic and deterministic
        simulation methods, hybrid deterministic-stochastic methods, and the importance
        of random number generator numerical resolution in stochastic simulation.
        © 2006 Oxford University Press.'
      authors:
      - name: Hoops S.
      - name: Gauges R.
      - name: Lee C.
      - name: Pahle J.
      - name: Simus N.
      - name: Singhal M.
      - name: Xu L.
      - name: Mendes P.
      - name: Kummer U.
      citationCount: 1960
      date: '2006-12-15T00:00:00Z'
      journal: Bioinformatics
      title: COPASI - A COmplex PAthway SImulator
    note: null
    pmcid: null
    pmid: '17032683'
    type: []
    version: null
  - doi: 10.1007/978-1-59745-525-1_2
    metadata:
      abstract: Computational modeling and simulation of biochemical networks is at
        the core of systems biology and this includes many types of analyses that
        can aid understanding of how these systems work. COPASI is a generic software
        package for modeling and simulation of biochemical networks which provides
        many of these analyses in convenient ways that do not require the user to
        program or to have deep knowledge of the numerical algorithms. Here we provide
        a description of how these modeling techniques can be applied to biochemical
        models using COPASI. The focus is both on practical aspects of software usage
        as well as on the utility of these analyses in aiding biological understanding.
        Practical examples are described for steady-state and time-course simulations,
        stoichiometric analyses, parameter scanning, sensitivity analysis (including
        metabolic control analysis), global optimization, parameter estimation, and
        stochastic simulation. The examples used are all published models that are
        available in the BioModels database in SBML format. © 2009 Humana Press.
      authors:
      - name: Mendes P.
      - name: Hoops S.
      - name: Sahle S.
      - name: Gauges R.
      - name: Dada J.
      - name: Kummer U.
      citationCount: 168
      date: '2009-12-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: Computational modeling of biochemical networks using COPASI
    note: null
    pmcid: null
    pmid: '19399433'
    type: []
    version: null
  - doi: 10.1016/j.jbiotec.2017.06.1200
    metadata:
      abstract: COPASI is software used for the creation, modification, simulation
        and computational analysis of kinetic models in various fields. It is open-source,
        available for all major platforms and provides a user-friendly graphical user
        interface, but is also controllable via the command line and scripting languages.
        These are likely reasons for its wide acceptance. We begin this review with
        a short introduction describing the general approaches and techniques used
        in computational modeling in the biosciences. Next we introduce the COPASI
        package, and its capabilities, before looking at typical applications of COPASI
        in biotechnology.
      authors:
      - name: Bergmann F.T.
      - name: Hoops S.
      - name: Klahn B.
      - name: Kummer U.
      - name: Mendes P.
      - name: Pahle J.
      - name: Sahle S.
      citationCount: 75
      date: '2017-11-10T00:00:00Z'
      journal: Journal of Biotechnology
      title: COPASI and its applications in biotechnology
    note: null
    pmcid: PMC5623632
    pmid: '28655634'
    type: []
    version: null
  relation:
  - biotoolsID: corc
    type: usedBy
  - biotoolsID: pycotools
    type: usedBy
  - biotoolsID: biosimulations
    type: includedIn
  - biotoolsID: sbmlwebapp
    type: usedBy
  toolType:
  - Command-line tool
  - Library
  - Desktop application
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2021-05-08T17:25:21Z'
  biotoolsCURIE: biotools:bakta
  biotoolsID: bakta
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: oliver.schwengers@cb.jlug.de
    fundrefid: null
    gridid: null
    name: Oliver Schwengers
    note: null
    orcidid: https://orcid.org/0000-0003-4216-2721
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: https://github.com/oschwengers
  - email: null
    fundrefid: null
    gridid: null
    name: Justus Liebig University Giessen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.uni-giessen.de
  description: Rapid & standardized annotation of bacterial genomes, MAGs & plasmids
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/oschwengers/bakta/blob/main/README.md
  - note: null
    type:
    - Contributions policy
    url: https://github.com/oschwengers/bakta/blob/main/CONTRIBUTION.md
  - note: null
    type:
    - Code of conduct
    url: https://github.com/oschwengers/bakta/blob/main/CODE_OF_CONDUCT.md
  - note: null
    type:
    - User manual
    url: https://bakta.readthedocs.io/
  download:
  - note: Mandatory annotation database
    type: Other
    url: https://zenodo.org/records/7669534
    version: v5.1
  editPermission:
    authors:
    - ELIXIR-CZ
    - bebatut
    type: group
  elixirCommunity: []
  elixirNode:
  - Germany
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: bakta --db <db-path> --prefix <prefix> --output <output-path> genome.fasta
    input:
    - data:
        term: Sequence assembly
        uri: http://edamontology.org/data_0925
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Sequence features metadata
        uri: http://edamontology.org/data_2914
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Sequence coordinates
        uri: http://edamontology.org/data_2012
      format:
      - term: GFF3
        uri: http://edamontology.org/format_1975
    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Hidden Markov model
        uri: http://edamontology.org/data_1364
      format:
      - term: HMMER3
        uri: http://edamontology.org/format_3329
    note: null
    operation:
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    output:
    - data:
        term: Feature table
        uri: http://edamontology.org/data_1270
      format:
      - term: GenBank format
        uri: http://edamontology.org/format_1936
      - term: EMBL format
        uri: http://edamontology.org/format_1927
      - term: GFF3
        uri: http://edamontology.org/format_1975
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Nucleic acid sequence record
        uri: http://edamontology.org/data_2887
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Score
        uri: http://edamontology.org/data_1772
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: https://github.com/oschwengers/bakta
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-12-23T21:48:50.049892Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/oschwengers/bakta
  - note: null
    type:
    - Issue tracker
    url: https://github.com/oschwengers/bakta/issues
  - note: null
    type:
    - Other
    url: https://bioconda.github.io/recipes/bakta/README.html
  - note: null
    type:
    - Service
    url: https://bakta.computational.bio
  maturity: Mature
  name: Bakta
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: oschwengers
  publication:
  - doi: 10.1099/mgen.0.000685
    metadata:
      abstract: Command-line annotation software tools have continuously gained popularity
        compared to centralized online services due to the worldwide increase of sequenced
        bacterial genomes. However, results of existing command-line software pipelines
        heavily depend on taxon-specific databases or sufficiently well annotated
        reference genomes. Here, we introduce Bakta, a new command-line software tool
        for the robust, taxon-independent, thorough and, nonetheless, fast annotation
        of bacterial genomes. Bakta conducts a comprehensive annotation workflow including
        the detection of small proteins taking into account replicon metadata. The
        annotation of coding sequences is accelerated via an alignment-free sequence
        identification approach that in addition facilitates the precise assignment
        of public database cross-references. Annotation results are exported in GFF3
        and International Nucleotide Sequence Database Collaboration (INSDC)-compliant
        flat files, as well as comprehensive JSON files, facilitating automated downstream
        analysis. We compared Bakta to other rapid contemporary command-line annotation
        software tools in both targeted and taxonomically broad benchmarks including
        isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms
        other tools in terms of functional annotations, the assignment of functional
        categories and database cross-references, whilst providing comparable wall-clock
        runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems.
        It is freely available under a GPLv3 license at https://​github.​com/​oschwengers/​bakta.
        An accompanying web version is available at https://​bakta.​computational.​bio.
      authors:
      - name: Schwengers O.
      - name: Jelonek L.
      - name: Dieckmann M.A.
      - name: Beyvers S.
      - name: Blom J.
      - name: Goesmann A.
      citationCount: 323
      date: '2021-01-01T00:00:00Z'
      journal: Microbial Genomics
      title: 'Bakta: Rapid and standardized annotation of bacterial genomes via alignment-free
        sequence identification'
    note: null
    pmcid: PMC8743544
    pmid: '34739369'
    type:
    - Primary
    version: '1.1'
  relation:
  - biotoolsID: diamond
    type: uses
  - biotoolsID: hmmer3
    type: uses
  - biotoolsID: infernal
    type: uses
  - biotoolsID: trnascan-se
    type: uses
  - biotoolsID: blast
    type: uses
  - biotoolsID: aragorn
    type: uses
  - biotoolsID: pilercr
    type: uses
  - biotoolsID: deepsig
    type: uses
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - v1.10.3
- accessibility: Open access
  additionDate: '2021-05-26T20:42:18Z'
  biotoolsCURIE: biotools:maldi_quant
  biotoolsID: maldi_quant
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: mail@sebastiangibb.de
    fundrefid: null
    gridid: null
    name: Sebastian Gibb
    note: null
    orcidid: https://orcid.org/0000-0001-7406-4443
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Korbinian Strimmer
    note: null
    orcidid: https://orcid.org/0000-0001-7917-2056
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: MALDIquant is a complete analysis pipeline for matrix-assisted laser
    desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass
    spectrometry data. In addition to commonly used plotting and processing methods
    it includes distinctive features, namely baseline subtraction methods such as
    morphological filters (TopHat) or the statistics-sensitive non-linear iterative
    peak-clipping algorithm (SNIP), peak alignment using warping functions, handling
    of replicated measurements as well as allowing spectra with different resolutions.
  documentation:
  - note: null
    type:
    - General
    url: https://cran.r-project.org/web/packages/MALDIquant/MALDIquant.pdf
  download: []
  editPermission:
    authors:
    - sizhengZhao
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    note: null
    operation:
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Spectrum calculation
      uri: http://edamontology.org/operation_3860
    - term: Peak detection
      uri: http://edamontology.org/operation_3215
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output:
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    note: null
    operation:
    - term: Mass spectra calibration
      uri: http://edamontology.org/operation_3627
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
  homepage: https://cran.r-project.org/package=MALDIquant
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-12-15T19:31:00.871136Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/sgibb/MALDIquant/
  - note: null
    type:
    - Issue tracker
    url: https://github.com/sgibb/MALDIquant/issues
  - note: null
    type:
    - Other
    url: http://strimmerlab.org/software/maldiquant/
  maturity: null
  name: MALDIquant
  operatingSystem: []
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1093/bioinformatics/bts447
    metadata:
      abstract: MALDIquant is an R package providing a complete and modular analysis
        pipeline for quantitative analysis of mass spectrometry data. MALDIquant is
        specifically designed with application in clinical diagnostics in mind and
        implements sophisticated routines for importing raw data, preprocessing, non-linear
        peak alignment and calibration. It also handles technical replicates as well
        as spectra with unequal resolution. © The Author 2012. Published by Oxford
        University Press. All rights reserved.
      authors:
      - name: Gibb S.
      - name: Strimmer K.
      citationCount: 476
      date: '2012-09-01T00:00:00Z'
      journal: Bioinformatics
      title: 'Maldiquant: A versatile R package for the analysis of mass spectrometry
        data'
    note: null
    pmcid: null
    pmid: '22796955'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  - Workflow
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-18T08:54:15.428034Z'
  biotoolsCURIE: biotools:hictkr
  biotoolsID: hictkr
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: 'R bindings for hictk: read .cool and .hic files directly from R'
  documentation:
  - note: null
    type:
    - General
    url: https://paulsengroup.github.io/hictkR/
  - note: null
    type:
    - Quick start guide
    url: https://paulsengroup.github.io/hictkR/articles/hictkR-vignette.html
  - note: null
    type:
    - API documentation
    url: https://paulsengroup.github.io/hictkR/reference/index.html
  download:
  - note: null
    type: Downloads page
    url: https://github.com/paulsengroup/hictkR/releases
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/paulsengroup/hictkR
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-12-04T21:53:14.841716Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/paulsengroup/hictkR
  - note: null
    type:
    - Issue tracker
    url: https://github.com/paulsengroup/hictkR/issues
  maturity: null
  name: hictkR
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: robomics
  publication:
  - doi: 10.1093/bioinformatics/btae408
    metadata:
      abstract: 'Motivation: Hi-C is gaining prominence as a method for mapping genome
        organization. With declining sequencing costs and a growing demand for higher-resolution
        data, efficient tools for processing Hi-C datasets at different resolutions
        are crucial. Over the past decade, the. hic and Cooler file formats have become
        the de-facto standard to store interaction matrices produced by Hi-C experiments
        in binary format. Interoperability issues make it unnecessarily difficult
        to convert between the two formats and to develop applications that can process
        each format natively. Results: We developed hictk, a toolkit that can transparently
        operate on. hic and. cool files with excellent performance. The toolkit is
        written in C++ and consists of a C++ library with Python and R bindings as
        well as CLI tools to perform common operations directly from the shell, including
        converting between. hic and. mcool formats. We benchmark the performance of
        hictk and compare it with other popular tools and libraries. We conclude that
        hictk significantly outperforms existing tools while providing the flexibility
        of natively working with both file formats without code duplication.'
      authors:
      - name: Rossini R.
      - name: Paulsen J.
      citationCount: 0
      date: '2024-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'hictk: blazing fast toolkit to work with. hic and. cool files'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: hictk
    type: uses
  toolType:
  - Library
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - v0.0.1
- accessibility: Open access
  additionDate: '2024-09-06T08:11:29.614214Z'
  biotoolsCURIE: biotools:enrichdo
  biotoolsID: enrichdo
  collectionID:
  - enrichdo
  confidence_flag: null
  cost: null
  credit: []
  description: A Global Weighted Model for Disease Ontology Enrichment Analysis. EnrichDO
    was based on the latest annotations of the human genome with DO terms, and double
    weighted the annotated genes. On one hand, to reinforce the saliency of direct
    gene-DO annotations, different initial weights were assigned to directly annotated
    genes and indirectly annotated genes, respectively. On the other hand, to detect
    locally most significant node between the parent and its children, less significant
    nodes were dynamically down-weighted.
  documentation:
  - note: null
    type:
    - Quick start guide
    url: https://github.com/liangcheng-hrbmu/EnrichDO/blob/devel/vignettes/EnrichDO.Rmd
  - note: null
    type:
    - Installation instructions
    url: https://www.bioconductor.org/packages/devel/bioc/html/EnrichDO.html
  download:
  - note: null
    type: Source code
    url: https://github.com/liangcheng-hrbmu/EnrichDO
    version: 1.1.1
  - note: R package download
    type: Downloads page
    url: https://www.bioconductor.org/packages/devel/bioc/html/EnrichDO.html
    version: 1.1.1
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene ID (NCBI)
        uri: http://edamontology.org/data_1027
      format:
      - term: Document format
        uri: http://edamontology.org/format_3507
    note: null
    operation:
    - term: Enrichment analysis
      uri: http://edamontology.org/operation_3501
    output:
    - data:
        term: Ontology data
        uri: http://edamontology.org/data_2353
      format:
      - term: Document format
        uri: http://edamontology.org/format_3507
  homepage: https://github.com/liangcheng-hrbmu/EnrichDO
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-12-01T01:14:37.834760Z'
  license: MIT
  link:
  - note: Please use the issue tracker for reporting bugs and making features requests.
    type:
    - Software catalogue
    url: https://github.com/liangcheng-hrbmu/EnrichDO
  maturity: null
  name: EnrichDO
  operatingSystem: []
  otherID: []
  owner: LiangCheng
  publication: []
  relation: []
  toolType:
  - Library
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 1.1.1
- accessibility: Open access
  additionDate: '2019-02-13T17:01:40Z'
  biotoolsCURIE: biotools:Jalview
  biotoolsID: Jalview
  collectionID:
  - ELIXIR-UK
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Jim Procter
    note: null
    orcidid: https://orcid.org/0000-0002-7865-7382
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.lifesci.dundee.ac.uk/people/jim-procter
  - email: null
    fundrefid: null
    gridid: null
    name: Geoff Barton
    note: null
    orcidid: https://orcid.org/0000-0002-9014-5355
    rorid: null
    typeEntity: null
    typeRole: []
    url: https://www.lifesci.dundee.ac.uk/people/geoff-barton
  description: Jalview is a free program for multiple sequence alignment editing,
    visualisation and analysis. Use it to view and edit sequence alignments, analyse
    them with phylogenetic trees and principal components analysis (PCA) plots and
    explore molecular structures and annotation.
  documentation:
  - note: null
    type:
    - Citation instructions
    url: https://www.jalview.org/about/citation
  - note: Hands-on exercises, Training courses and Training videos
    type:
    - Training material
    url: https://www.jalview.org/training/
  - note: null
    type:
    - FAQ
    url: https://www.jalview.org/help/faq
  - note: null
    type:
    - User manual
    url: https://www.jalview.org/help/documentation/
  download:
  - note: null
    type: Downloads page
    url: https://www.jalview.org/download
    version: null
  - note: null
    type: Source code
    url: https://www.jalview.org/download/source/
    version: null
  - note: Binaries for all platforms
    type: Binaries
    url: https://www.jalview.org/download/?os=all
    version: null
  - note: null
    type: Icon
    url: https://www.jalview.org/favicon.svg
    version: null
  - note: Executable JAR file
    type: Binaries
    url: https://www.jalview.org/download/other/jar/
    version: null
  editPermission:
    authors:
    - ben_s
    - jimprocter
    type: group
  elixirCommunity: []
  elixirNode:
  - UK
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence alignment
        uri: http://edamontology.org/data_0863
      format:
      - term: GFF3-seq
        uri: http://edamontology.org/format_1939
      - term: ClustalW format
        uri: http://edamontology.org/format_1982
      - term: Stockholm format
        uri: http://edamontology.org/format_1961
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
      - term: GFF2-seq
        uri: http://edamontology.org/format_1938
      - term: FASTA
        uri: http://edamontology.org/format_1929
      - term: nbrf/pir
        uri: http://edamontology.org/format_1948
      - term: BioJSON (Jalview)
        uri: http://edamontology.org/format_3774
      - term: PHYLIP format
        uri: http://edamontology.org/format_1997
      - term: BLC
        uri: http://edamontology.org/format_3313
      - term: RNAML
        uri: http://edamontology.org/format_3311
      - term: GCG MSF
        uri: http://edamontology.org/format_1947
      - term: Pileup
        uri: http://edamontology.org/format_3015
      - term: mmCIF
        uri: http://edamontology.org/format_1477
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: Format
        uri: http://edamontology.org/format_1915
    - data:
        term: Structure alignment
        uri: http://edamontology.org/data_0886
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    note: 'Other Input formats:

      AMSA (.amsa);

      JnetFile (.concise, .jnet);

      PFAM (.pfam);

      Substitution matrix (.matrix);

      Jalview Project File (.jvp);

      Jalview Feature File (.features, .jvfeatures);

      Jalview Annotations File (.annotations, .jvannotations);

      Predicted Aligned Error (PAE) Matrix File (.json)

      ...

      Other Output formats:

      PFAM (.pfam);

      BioJS (.biojs) (interactive HTML/Javascript);

      Jalview Project File (.jvp);'
    operation:
    - term: Sequence visualisation
      uri: http://edamontology.org/operation_0564
    - term: Phylogenetic tree analysis
      uri: http://edamontology.org/operation_0324
    - term: Sequence alignment editing
      uri: http://edamontology.org/operation_3081
    output:
    - data:
        term: Sequence alignment
        uri: http://edamontology.org/data_0863
      format:
      - term: nbrf/pir
        uri: http://edamontology.org/format_1948
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: Stockholm format
        uri: http://edamontology.org/format_1961
      - term: FASTA
        uri: http://edamontology.org/format_1929
      - term: PHYLIP format
        uri: http://edamontology.org/format_1997
      - term: BLC
        uri: http://edamontology.org/format_3313
      - term: BioJSON (Jalview)
        uri: http://edamontology.org/format_3774
      - term: GCG MSF
        uri: http://edamontology.org/format_1947
      - term: Pileup
        uri: http://edamontology.org/format_3015
      - term: ClustalW format
        uri: http://edamontology.org/format_1982
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: EPS
        uri: http://edamontology.org/format_3466
      - term: SVG
        uri: http://edamontology.org/format_3604
      - term: Format
        uri: http://edamontology.org/format_1915
  homepage: https://www.jalview.org/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-28T10:07:42.658684Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Discussion forum
    url: https://discourse.jalview.org/
  - note: null
    type:
    - Issue tracker
    url: https://issues.jalview.org/
  - note: Latest development version
    type:
    - Other
    url: https://www.jalview.org/development/jalview_develop/
  - note: null
    type:
    - Repository
    url: https://source.jalview.org/crucible/browse/jalview
  - note: Twitter feed
    type:
    - Social media
    url: https://twitter.com/Jalview
  - note: YouTube training videos
    type:
    - Social media
    url: https://www.youtube.com/channel/UCIjpnvZB770yz7ftbrJ0tfw
  maturity: Mature
  name: Jalview
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ben_s
  publication:
  - doi: 10.1093/bioinformatics/btp033
    metadata: null
    note: null
    pmcid: PMC2672624
    pmid: '19151095'
    type: []
    version: null
  relation:
  - biotoolsID: jabaws
    type: uses
  - biotoolsID: chimera
    type: uses
  - biotoolsID: chimerax
    type: uses
  - biotoolsID: pymol
    type: uses
  - biotoolsID: bioconda
    type: includedIn
  - biotoolsID: 3d-beacons
    type: uses
  - biotoolsID: uniprot
    type: uses
  - biotoolsID: pfam
    type: uses
  - biotoolsID: ensembl
    type: uses
  - biotoolsID: pdb
    type: uses
  - biotoolsID: rfam
    type: uses
  toolType:
  - Desktop application
  - Command-line tool
  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  validated: 1
  version:
  - 2.11.4.1
- accessibility: Open access
  additionDate: '2019-07-03T16:03:27Z'
  biotoolsCURIE: biotools:MMseqs2
  biotoolsID: MMseqs2
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: 'MMseqs2 (Many-against-Many sequence searching) is a software suite
    to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open
    source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta
    version, via cygwin) Windows. The software is designed to run on multiple cores
    and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster
    than BLAST. At 100 times its speed it achieves almost the same sensitivity. It
    can perform profile searches with the same sensitivity as PSI-BLAST at over 400
    times its speed.


    MMseqs2 includes Linclust, the first clustering algorithm whose runtime scales
    linearly With Linclust we clustered 1.6 billion metagenomic sequence fragments
    in 10 h on a single server to 50% sequence identity.'
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/soedinglab/MMseqs2/blob/master/README.md
  - note: null
    type:
    - User manual
    url: https://github.com/soedinglab/mmseqs2/wiki
  - note: Tutorial material
    type:
    - Training material
    url: https://github.com/soedinglab/MMseqs2/wiki/Tutorials
  download:
  - note: null
    type: Source code
    url: https://github.com/soedinglab/MMseqs2/releases
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: mmseqs easy-search query.fasta target.fasta output.m8 tmp
    input:
    - data:
        term: Sequence set
        uri: http://edamontology.org/data_0850
      format:
      - term: FASTQ-like format (text)
        uri: http://edamontology.org/format_2182
      - term: FASTA-like (text)
        uri: http://edamontology.org/format_2200
    note: null
    operation:
    - term: Sequence similarity search
      uri: http://edamontology.org/operation_0346
    - term: Sequence alignment
      uri: http://edamontology.org/operation_0292
    output:
    - data:
        term: Sequence search results
        uri: http://edamontology.org/data_0857
      format:
      - term: BLAST results
        uri: http://edamontology.org/format_1333
  - cmd: 'mmseqs easy-cluster input.fasta output tmp

      mmseqs easy-linclust input.fasta output tmp'
    input:
    - data:
        term: Sequence set
        uri: http://edamontology.org/data_0850
      format:
      - term: FASTQ-like format (text)
        uri: http://edamontology.org/format_2182
      - term: FASTA-like (text)
        uri: http://edamontology.org/format_2200
    note: null
    operation:
    - term: Sequence clustering
      uri: http://edamontology.org/operation_0291
    output:
    - data:
        term: Sequence cluster
        uri: http://edamontology.org/data_1235
      format: []
  - cmd: mmseqs easy-taxonomy query.fasta targetDB output tmp
    input:
    - data:
        term: Sequence set
        uri: http://edamontology.org/data_0850
      format:
      - term: FASTQ-like format (text)
        uri: http://edamontology.org/format_2182
      - term: FASTA-like (text)
        uri: http://edamontology.org/format_2200
    note: null
    operation:
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output:
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format: []
  homepage: https://mmseqs.com
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2024-11-26T16:25:05.809769Z'
  license: MIT
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/soedinglab/mmseqs2/issues
  - note: null
    type:
    - Repository
    url: https://github.com/soedinglab/mmseqs2
  maturity: Mature
  name: MMseqs2
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: milot-mirdita
  publication:
  - doi: 10.1038/nbt.3988
    metadata: null
    note: null
    pmcid: null
    pmid: '29035372'
    type: []
    version: null
  - doi: 10.1101/079681
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1038/s41467-018-04964-5
    metadata: null
    note: null
    pmcid: PMC6026198
    pmid: '29959318'
    type:
    - Method
    version: null
  - doi: 10.1093/bioinformatics/bty1057
    metadata: null
    note: null
    pmcid: PMC6691333
    pmid: '30615063'
    type:
    - Method
    version: null
  - doi: 10.1101/2020.11.27.401018
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1093/bioinformatics/btab184
    metadata: null
    note: MMseqs2 Taxonomy
    pmcid: PMC8479651
    pmid: '33734313'
    type: []
    version: null
  - doi: 10.1101/2024.11.13.623350v1
    metadata: null
    note: MMseqs2-GPU
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: mmseqs
    type: isNewVersionOf
  - biotoolsID: plass
    type: usedBy
  - biotoolsID: linclust
    type: includes
  - biotoolsID: metaeuk
    type: usedBy
  - biotoolsID: conterminator
    type: usedBy
  - biotoolsID: spacepharer
    type: usedBy
  toolType:
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Nucleic acids
    uri: http://edamontology.org/topic_0077
  - term: Gene and protein families
    uri: http://edamontology.org/topic_0623
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  validated: 1
  version: []
- accessibility: null
  additionDate: '2018-02-03T11:08:18Z'
  biotoolsCURIE: biotools:mdanalysis
  biotoolsID: mdanalysis
  collectionID:
  - BioExcel
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: MDAnalysis mainling list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://groups.google.com/group/mdnalysis-discussion
  description: MDAnalysis is an object-oriented python toolkit to analyze molecular
    dynamics trajectories generated by CHARMM, Gromacs, NAMD, LAMMPS, Amber or DL_POLY;
    it also reads other formats (e.g. PDB files and XYZ format trajectories; see the
    supported coordinate formats for the full list). It can write most of these formats,
    too, together with atom selections for use in Gromacs, CHARMM, VMD and PyMol
  documentation:
  - note: null
    type:
    - General
    url: https://www.mdanalysis.org/docs/
  download:
  - note: null
    type: Software package
    url: https://github.com/MDAnalysis/mdanalysis
    version: null
  - note: null
    type: Software package
    url: http://pypi.python.org/pypi/MDAnalysis
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Molecular dynamics
      uri: http://edamontology.org/operation_2476
    output: []
  homepage: https://www.mdanalysis.org
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-11-25T16:03:28.831718Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/MDAnalysis/mdanalysis/issues
  - note: null
    type:
    - Mailing list
    url: https://groups.google.com/group/mdnalysis-discussion
  - note: null
    type:
    - Repository
    url: https://github.com/MDAnalysis/mdanalysis
  - note: null
    type:
    - Mailing list
    url: https://groups.google.com/group/mdnalysis-devel
  maturity: Mature
  name: MDAnalysis
  operatingSystem:
  - Linux
  otherID: []
  owner: gelpi@ub.edu
  publication:
  - doi: 10.1002/jcc.21787
    metadata: null
    note: null
    pmcid: PMC3144279
    pmid: '21500218'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Molecular dynamics
    uri: http://edamontology.org/topic_0176
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-02-27T12:05:22Z'
  biotoolsCURIE: biotools:igdiscover
  biotoolsID: igdiscover
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Gunilla Karlsson Hedestam
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: http://ki.se/en/mtc/gunilla-karlsson-hedestam-group
  - email: igdiscover@googlegroups.com
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://igdiscover.se/
  description: Analyzes antibody repertoires and discovers new V genes from high-throughput
    sequencing reads. Heavy chains, kappa and lambda light chains are supported (to
    discover VH, VK and VL genes).
  documentation:
  - note: null
    type:
    - User manual
    url: http://docs.igdiscover.se/
  download:
  - note: null
    type: Software package
    url: https://pypi.python.org/pypi/igdiscover
    version: null
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: RNA sequence (raw)
        uri: http://edamontology.org/data_3496
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Homology-based gene prediction
      uri: http://edamontology.org/operation_3663
    - term: Gene expression profiling
      uri: http://edamontology.org/operation_0314
    output:
    - data:
        term: Gene expression profile
        uri: http://edamontology.org/data_0928
      format: []
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_2977
      format: []
  homepage: http://igdiscover.se/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-11-25T14:56:36.524326Z'
  license: MIT
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/NBISweden/IgDiscover
  maturity: Mature
  name: IgDiscover
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: marcelm
  publication:
  - doi: 10.1038/ncomms13642
    metadata: null
    note: null
    pmcid: PMC5187446
    pmid: '27995928'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Nucleic acid structure analysis
    uri: http://edamontology.org/topic_0097
  validated: 1
  version:
  - 0.6.0
- accessibility: null
  additionDate: '2018-07-17T14:46:40Z'
  biotoolsCURIE: biotools:consensusov
  biotoolsID: consensusov
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: benjamin.haibe.kains@utoronto.ca
    fundrefid: null
    gridid: null
    name: Benjamin Haibe-Kains
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Implementation of  four major subtype classifiers for high-grade serous
    (HGS) ovarian cancer. In addition, it implements a consensus classifier, which
    consolidates and improves on the robustness of the proposed subtype classifiers,
    thereby providing reliable stratification of patients with HGS ovarian tumors
    of clearly defined subtype.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/consensusOV/man/consensusOV.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/consensusOV_1.2.0.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Classification
      uri: http://edamontology.org/operation_2990
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Clustering
      uri: http://edamontology.org/operation_3432
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Differential gene expression analysis
      uri: http://edamontology.org/operation_3223
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/consensusOV.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:35:19.593337Z'
  license: Artistic-2.0
  link:
  - note: null
    type:
    - Mirror
    url: http://www.pmgenomics.ca/bhklab/software/consensusOV
  - note: null
    type:
    - Issue tracker
    url: https://github.com/bhklab/consensusOV/issues
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/consensusOV/
  - note: null
    type:
    - Repository
    url: https://git.bioconductor.org/packages/consensusOV
  maturity: null
  name: consensusOV
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1158/1078-0432.ccr-18-0784
    metadata:
      abstract: 'Purpose: The majority of ovarian carcinomas are of high-grade serous
        histology, which is associated with poor prognosis. Surgery and chemotherapy
        are the mainstay of treatment, and molecular characterization is necessary
        to lead the way to targeted therapeutic options. To this end, various computational
        methods for gene expression-based subtyping of high-grade serous ovarian carcinoma
        (HGSOC) have been proposed, but their overlap and robustness remain unknown.
        Experimental Design: We assess three major subtype classifiers by meta-analysis
        of publicly available expression data, and assess statistical criteria of
        subtype robustness and classifier concordance. We develop a consensus classifier
        that represents the subtype classifications of tumors based on the consensus
        of multiple methods, and outputs a confidence score. Using our compendium
        of expression data, we examine the possibility that a subset of tumors is
        unclassifiable based on currently proposed subtypes. Results: HGSOC subtyping
        classifiers exhibit moderate pairwise concordance across our data compendium
        (58.9%-70.9%; P < 10-5) and are associated with overall survival in a meta-analysis
        across datasets (P < 10-5). Current subtypes do not meet statistical criteria
        for robustness to reclustering across multiple datasets (prediction strength
        < 0.6). A new subtype classifier is trained on concordantly classified samples
        to yield a consensus classification of patient tumors that correlates with
        patient age, survival, tumor purity, and lymphocyte infiltration. Conclusions:
        A new consensus ovarian subtype classifier represents the consensus of methods
        and demonstrates the importance of classification approaches for cancer that
        do not require all tumors to be assigned to a distinct subtype.'
      authors:
      - name: Chen G.M.
      - name: Kannan L.
      - name: Geistlinger L.
      - name: Kofia V.
      - name: Safikhani Z.
      - name: Gendoo D.M.A.
      - name: Parmigiani G.
      - name: Birrer M.
      - name: Haibe-Kains B.
      - name: Waldron L.
      citationCount: 85
      date: '2018-10-15T00:00:00Z'
      journal: Clinical Cancer Research
      title: Consensus on molecular subtypes of high-grade serous ovarian carcinoma
    note: null
    pmcid: PMC6207081
    pmid: '30084834'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Microarray experiment
    uri: http://edamontology.org/topic_3518
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 1
  version:
  - 1.2.0
- accessibility: null
  additionDate: '2018-07-17T13:51:36Z'
  biotoolsCURIE: biotools:chromswitch
  biotoolsID: chromswitch
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: selinjessa@gmail.com
    fundrefid: null
    gridid: null
    name: Selin Jessa
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Flexible method to detect chromatin state switches between samples
    in two biological conditions in a specific genomic region of interest given peaks
    or chromatin state calls from ChIP-seq data.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/chromswitch/man/chromswitch.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/chromswitch_1.2.1.tar.gz
    version: null
  - note: null
    type: Source code
    url: http://bioconductor.org/packages/release/bioc/vignettes/chromswitch/inst/doc/chromswitch_intro.R
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Clustering
      uri: http://edamontology.org/operation_3432
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Comparison
      uri: http://edamontology.org/operation_2424
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/chromswitch.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:35:18.381844Z'
  license: MIT
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/sjessa/chromswitch
  - note: null
    type:
    - Issue tracker
    url: https://github.com/sjessa/chromswitch/issues
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/chromswitch/
  - note: null
    type:
    - Repository
    url: https://git.bioconductor.org/packages/chromswitch
  maturity: null
  name: chromswitch
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1093/bioinformatics/bty075
    metadata:
      abstract: Chromatin state plays amajor role in controlling gene expression,
        and comparative analysis of ChIP-seq data is key to understanding epigenetic
        regulation. We present chromswitch, an R/ Bioconductor package to integrate
        epigenomic data in a defined window of interest to detect an overall switch
        in chromatin state. Chromswitch accurately classifies a benchmarking dataset,
        and when applied genome-wide, the tool successfully detects chromatin changes
        that result in brain-specific expression.
      authors:
      - name: Jessa S.
      - name: Kleinman C.L.
      citationCount: 9
      date: '2018-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'Chromswitch: A flexible method to detect chromatin state switches'
    note: null
    pmcid: PMC6022667
    pmid: '29438498'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Epigenetics
    uri: http://edamontology.org/topic_3295
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  validated: 1
  version:
  - 1.2.1
- accessibility: null
  additionDate: '2018-06-11T17:34:32Z'
  biotoolsCURIE: biotools:chimeraviz
  biotoolsID: chimeraviz
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: rolf.i.skotheim@rr-research.no
    fundrefid: null
    gridid: null
    name: Rolf I. Skotheim
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Visualization tools for gene fusions.
  documentation:
  - note: null
    type:
    - User manual
    url: https://bioconductor.org/packages/release/bioc/manuals/chimeraviz/man/chimeraviz.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/chimeraviz_1.6.0.tar.gz
    version: null
  - note: null
    type: Source code
    url: http://bioconductor.org/packages/release/bioc/vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence visualisation
      uri: http://edamontology.org/operation_0564
    output: []
  homepage: https://bioconductor.org/packages/release/bioc/html/chimeraviz.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:35:17.124576Z'
  license: Artistic-2.0
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/stianlagstad/chimeraviz
  - note: null
    type:
    - Issue tracker
    url: https://github.com/stianlagstad/chimeraviz/issues
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/chimeraviz/
  maturity: null
  name: chimeraviz
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: d.gabrielaitis
  publication:
  - doi: 10.1093/bioinformatics/btx329
    metadata:
      abstract: 'Summary: Advances in high-throughput RNA sequencing have enabled
        more efficient detection of fusion transcripts, but the technology and associated
        software used for fusion detection from sequencing data often yield a high
        false discovery rate. Good prioritization of the results is important, and
        this can be helped by a visualization framework that automatically integrates
        RNA data with known genomic features. Here we present chimeraviz, a Bioconductor
        package that automates the creation of chimeric RNA visualizations. The package
        supports input from nine different fusion-finder tools: deFuse, EricScript,
        InFusion, JAFFA, FusionCatcher, FusionMap, PRADA, SOAPfuse and STAR-FUSION.
        Availability and implementation: chimeraviz is an R package available via
        Bioconductor (https://bio conductor.org/packages/release/bioc/html/chimeraviz.html)
        under Artistic-2.0. Source code and support is available at GitHub (https://github.com/stianlagstad/chimeraviz).'
      authors:
      - name: Lagstad S.
      - name: Zhao S.
      - name: Hoff A.M.
      - name: Johannessen B.
      - name: Lingjaerde O.C.
      - name: Skotheim R.I.
      citationCount: 22
      date: '2017-09-15T00:00:00Z'
      journal: Bioinformatics
      title: 'Chimeraviz: A tool for visualizing chimeric RNA'
    note: null
    pmcid: PMC5870674
    pmid: '28525538'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  validated: 1
  version:
  - 1.6.0
- accessibility: null
  additionDate: '2018-07-14T20:21:43Z'
  biotoolsCURIE: biotools:rcistarget
  biotoolsID: rcistarget
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: null
  credit:
  - email: sara.aibar@kuleuven.vib.be
    fundrefid: null
    gridid: null
    name: Sara Aibar
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: It identifies transcription factor binding motifs (TFBS) over-represented
    on a gene list. In a first step, it selects DNA motifs that are significantly
    over-represented in the surroundings of the transcription start site (TSS) of
    the genes in the gene-set. This is achieved by using a database that contains
    genome-wide cross-species rankings for each motif.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/RcisTarget/man/RcisTarget.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/RcisTarget_1.0.2.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene-set enrichment analysis
      uri: http://edamontology.org/operation_2436
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Transcription factor binding site prediction
      uri: http://edamontology.org/operation_0445
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/RcisTarget.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:35:08.425464Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Mirror
    url: http://scenic.aertslab.org/
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/RcisTarget/
  - note: null
    type:
    - Issue tracker
    url: https://github.com/aertslab/RcisTarget/issues
  - note: null
    type:
    - Repository
    url: https://git.bioconductor.org/packages/RcisTarget
  maturity: null
  name: RcisTarget
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1038/nmeth.4463
    metadata:
      abstract: We present SCENIC, a computational method for simultaneous gene regulatory
        network reconstruction and cell-state identification from single-cell RNA-seq
        data (http://scenic.aertslab.org). On a compendium of single-cell data from
        tumors and brain, we demonstrate that cis-regulatory analysis can be exploited
        to guide the identification of transcription factors and cell states. SCENIC
        provides critical biological insights into the mechanisms driving cellular
        heterogeneity.
      authors:
      - name: Aibar S.
      - name: Gonzalez-Blas C.B.
      - name: Moerman T.
      - name: Huynh-Thu V.A.
      - name: Imrichova H.
      - name: Hulselmans G.
      - name: Rambow F.
      - name: Marine J.-C.
      - name: Geurts P.
      - name: Aerts J.
      - name: Van Den Oord J.
      - name: Atak Z.K.
      - name: Wouters J.
      - name: Aerts S.
      citationCount: 2732
      date: '2017-10-31T00:00:00Z'
      journal: Nature Methods
      title: 'SCENIC: Single-cell regulatory network inference and clustering'
    note: null
    pmcid: PMC5937676
    pmid: '28991892'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Gene regulation
    uri: http://edamontology.org/topic_0204
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 1
  version:
  - 1.0.2
- accessibility: null
  additionDate: '2018-07-13T09:52:43Z'
  biotoolsCURIE: biotools:rjmcmcnucleosomes
  biotoolsID: rjmcmcnucleosomes
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: null
  credit:
  - email: adeschen@hotmail.com
    fundrefid: null
    gridid: null
    name: Astrid Deschênes
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: This package does nucleosome positioning using informative Multinomial-Dirichlet
    prior in a t-mixture with reversible jump estimation of nucleosome positions for
    genome-wide profiling.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/RJMCMCNucleosomes/man/RJMCMCNucleosomes.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/RJMCMCNucleosomes_1.4.0.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene expression profiling
      uri: http://edamontology.org/operation_0314
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/RJMCMCNucleosomes.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:35:00.529400Z'
  license: Artistic-2.0
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/RJMCMCNucleosomes/
  - note: null
    type:
    - Issue tracker
    url: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues
  - note: null
    type:
    - Repository
    url: https://git.bioconductor.org/packages/RJMCMCNucleosomes
  maturity: null
  name: RJMCMCNucleosomes
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1515/sagmb-2014-0098
    metadata:
      abstract: Genome-wide mapping of nucleosomes has revealed a great deal about
        the relationships between chromatin structure and control of gene expression.
        Recent next generation CHIP-chip and CHIP-Seq technologies have accelerated
        our understanding of basic principles of chromatin organization. These technologies
        have taught us that nucleosomes play a crucial role in gene regulation by
        allowing physical access to transcription factors. Recent methods and experimental
        advancements allow the determination of nucleosome positions for a given genome
        area. However, most of these methods estimate the number of nucleosomes either
        by an EM algorithm using a BIC criterion or an effective heuristic strategy.
        Here, we introduce a Bayesian method for identifying nucleosome positions.
        The proposed model is based on a Multinomial-Dirichlet classification and
        a hierarchical mixture distributions. The number and the positions of nucleosomes
        are estimated using a reversible jump Markov chain Monte Carlo simulation
        technique. We compare the performance of our method on simulated data and
        MNase-Seq data from Saccharomyces cerevisiae against PING and NOrMAL methods.
      authors:
      - name: Samb R.
      - name: Khadraoui K.
      - name: Belleau P.
      - name: Deschenes A.
      - name: Lakhal-Chaieb L.
      - name: Droit A.
      citationCount: 10
      date: '2015-12-01T00:00:00Z'
      journal: Statistical Applications in Genetics and Molecular Biology
      title: Using informative Multinomial-Dirichlet prior in a t-mixture with reversible
        jump estimation of nucleosome positions for genome-wide profiling
    note: null
    pmcid: null
    pmid: '26656614'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: ChIP-seq
    uri: http://edamontology.org/topic_3169
  - term: DNA packaging
    uri: http://edamontology.org/topic_3176
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  validated: 1
  version:
  - 1.4.0
- accessibility: null
  additionDate: '2018-07-13T08:49:55Z'
  biotoolsCURIE: biotools:remp
  biotoolsID: remp
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: null
  credit:
  - email: y-zheng@northwestern.edu
    fundrefid: null
    gridid: null
    name: Yinan Zheng
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Machine learning-based tools to predict DNA methylation of locus-specific
    repetitive elements (RE) by learning surrounding genetic and epigenetic information.
    These tools provide genomewide and single-base resolution of DNA methylation prediction
    on RE that are difficult to measure using array-based or sequencing-based platforms,
    which enables epigenome-wide association study (EWAS) and differentially methylated
    region (DMR) analysis on RE.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/REMP/man/REMP.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/REMP_1.4.0.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Alignment
      uri: http://edamontology.org/operation_2928
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Differentially-methylated region identification
      uri: http://edamontology.org/operation_3809
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene prediction
      uri: http://edamontology.org/operation_2454
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Validation
      uri: http://edamontology.org/operation_2428
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/REMP.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:34:59.156002Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/REMP/
  - note: null
    type:
    - Mirror
    url: https://github.com/YinanZheng/REMP
  - note: null
    type:
    - Issue tracker
    url: https://github.com/YinanZheng/REMP/issues
  - note: null
    type:
    - Repository
    url: https://git.bioconductor.org/packages/REMP
  maturity: null
  name: REMP
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1093/nar/gkx587
    metadata:
      abstract: DNA methylation in repetitive elements (RE) suppresses their mobility
        and maintains genomic stability, and decreases in it are frequently observed
        in tumor and/or surrogate tissues. Averaging methylation across RE in genome
        is widely used to quantify globalmethylation. However, methylation may vary
        in specific RE and play diverse roles in disease development, thus averaging
        methylation across RE may lose significant biological information. The ambiguous
        mapping of short reads by and high cost of current bisulfite sequencing platforms
        make them impractical for quantifying locus-specific RE methylation. Although
        microarray-based approaches (particularly Illumina's Infinium methylation
        arrays) provide cost-effective and robust genome-wide methylation quantification,
        the number of interrogated CpGs in RE remains limited.We report a random forest-based
        algorithm (and corresponding R package, REMP) that can accurately predict
        genome-wide locus-specific RE methylation based on Infinium array profiling
        data. We validated its prediction performance using alternative sequencing
        and microarray data. Testing its clinical utility with The Cancer Genome Atlas
        data demonstrated that our algorithm offers more comprehensively extended
        locus-specific RE methylation information that can be readily applied to large
        human studies in a cost-effective manner. Our work has the potential to improve
        our understanding of the role of global methylation in human diseases, especially
        cancer.
      authors:
      - name: Zheng Y.
      - name: Joyce B.T.
      - name: Liu L.
      - name: Zhang Z.
      - name: Kibbe W.A.
      - name: Zhang W.
      - name: Hou L.
      citationCount: 72
      date: '2017-09-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: Prediction of genome-wide DNA methylation in repetitive elements
    note: null
    pmcid: PMC5587781
    pmid: '28911103'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Epigenetics
    uri: http://edamontology.org/topic_3295
  - term: Microarray experiment
    uri: http://edamontology.org/topic_3518
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  validated: 1
  version:
  - 1.4.0
- accessibility: null
  additionDate: '2018-06-25T07:11:57Z'
  biotoolsCURIE: biotools:basics
  biotoolsID: basics
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: null
  credit:
  - email: cnvallej@uc.cl
    fundrefid: null
    gridid: null
    name: Catalina A. Vallejos
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: eling@ebi.ac.uk
    fundrefid: null
    gridid: null
    name: Nils Eling
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Bayesian Analysis of Single-Cell Sequencing data is an integrated Bayesian
    hierarchical model to perform statistical analyses of single-cell RNA sequencing
    datasets in the context of supervised experiments. It could perform built-in data
    normalisation and technical noise quantification. BASiCS provides an intuitive
    detection criterion for highly variable genes within a single group of cells.
    Also, it can compare gene expression patterns between two or more pre-specified
    groups of cells.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/BASiCS/man/BASiCS.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/BASiCS_1.2.1.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Differential gene expression analysis
      uri: http://edamontology.org/operation_3223
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/BASiCS.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:34:37.430997Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/catavallejos/BASiCS
  - note: null
    type:
    - Issue tracker
    url: https://github.com/catavallejos/BASiCS/issues
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/BASiCS/
  maturity: null
  name: BASiCS
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1371/journal.pcbi.1004333
    metadata:
      abstract: 'Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity
        in gene expression levels in seemingly homogeneous populations of cells. However,
        these experiments are prone to high levels of unexplained technical noise,
        creating new challenges for identifying genes that show genuine heterogeneous
        expression within the population of cells under study. BASiCS (Bayesian Analysis
        of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model
        where: (i) cell-specific normalisation constants are estimated as part of
        the model parameters, (ii) technical variability is quantified based on spike-in
        genes that are artificially introduced to each analysed cell’s lysate and
        (iii) the total variability of the expression counts is decomposed into technical
        and biological components. BASiCS also provides an intuitive detection criterion
        for highly (or lowly) variable genes within the population of cells under
        study. This is formalised by means of tail posterior probabilities associated
        to high (or low) biological cell-to-cell variance contributions, quantities
        that can be easily interpreted by users. We demonstrate our method using gene
        expression measurements from mouse Embryonic Stem Cells. Cross-validation
        and meaningful enrichment of gene ontology categories within genes classified
        as highly (or lowly) variable supports the efficacy of our approach.'
      authors:
      - name: Vallejos C.A.
      - name: Marioni J.C.
      - name: Richardson S.
      citationCount: 203
      date: '2015-06-24T00:00:00Z'
      journal: PLoS Computational Biology
      title: 'BASiCS: Bayesian Analysis of Single-Cell Sequencing Data'
    note: null
    pmcid: PMC4480965
    pmid: '26107944'
    type:
    - Primary
    version: null
  - doi: 10.1186/s13059-016-0930-3
    metadata:
      abstract: Traditional differential expression tools are limited to detecting
        changes in overall expression, and fail to uncover the rich information provided
        by single-cell level data sets. We present a Bayesian hierarchical model that
        builds upon BASiCS to study changes that lie beyond comparisons of means,
        incorporating built-in normalization and quantifying technical artifacts by
        borrowing information from spike-in genes. Using a probabilistic approach,
        we highlight genes undergoing changes in cell-to-cell heterogeneity but whose
        overall expression remains unchanged. Control experiments validate our method's
        performance and a case study suggests that novel biological insights can be
        revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS.
      authors:
      - name: Vallejos C.A.
      - name: Richardson S.
      - name: Marioni J.C.
      citationCount: 73
      date: '2016-04-15T00:00:00Z'
      journal: Genome Biology
      title: 'Beyond comparisons of means: Understanding changes in gene expression
        at the single-cell level'
    note: null
    pmcid: PMC4832562
    pmid: '27083558'
    type:
    - Other
    version: null
  - doi: 10.1101/237214
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 1
  version:
  - 1.2.1
- accessibility: null
  additionDate: '2018-07-26T17:19:05Z'
  biotoolsCURIE: biotools:splatter
  biotoolsID: splatter
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: luke.zappia@mcri.edu.au
    fundrefid: null
    gridid: null
    name: Luke Zappia
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Simulate single-cell RNA sequencing count data. It provides a simple
    interface for creating complex simulations that are reproducible and well-documented.
    Parameters can be estimated from real data and functions are provided for comparing
    real and simulated datasets.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/splatter/man/splatter.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/splatter_1.4.1.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Comparison
      uri: http://edamontology.org/operation_2424
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/splatter.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:34:32.146110Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/Oshlack/splatter
  - note: null
    type:
    - Issue tracker
    url: https://github.com/Oshlack/splatter/issues
  maturity: null
  name: splatter
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1186/s13059-017-1305-0
    metadata:
      abstract: As single-cell RNA sequencing (scRNA-seq) technologies have rapidly
        developed, so have analysis methods. Many methods have been tested, developed,
        and validated using simulated datasets. Unfortunately, current simulations
        are often poorly documented, their similarity to real data is not demonstrated,
        or reproducible code is not available. Here, we present the Splatter Bioconductor
        package for simple, reproducible, and well-documented simulation of scRNA-seq
        data. Splatter provides an interface to multiple simulation methods including
        Splat, our own simulation, based on a gamma-Poisson distribution. Splat can
        simulate single populations of cells, populations with multiple cell types,
        or differentiation paths.
      authors:
      - name: Zappia L.
      - name: Phipson B.
      - name: Oshlack A.
      citationCount: 493
      date: '2017-09-12T00:00:00Z'
      journal: Genome Biology
      title: 'Splatter: Simulation of single-cell RNA sequencing data'
    note: null
    pmcid: PMC5596896
    pmid: '28899397'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 1
  version:
  - 1.4.1
- accessibility: null
  additionDate: '2018-07-26T11:04:28Z'
  biotoolsCURIE: biotools:scdd
  biotoolsID: scdd
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: keegan@jimmy.harvard.edu
    fundrefid: null
    gridid: null
    name: Keegan Korthauer
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: This package implements a method to analyze single-cell RNA- seq Data
    utilizing flexible Dirichlet Process mixture models. Genes with differential distributions
    of expression are classified into several interesting patterns of differences
    between two conditions. The package also includes functions for simulating data
    with these patterns from negative binomial distributions.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/scDD/man/scDD.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/scDD_1.4.0.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Clustering
      uri: http://edamontology.org/operation_3432
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Differential gene expression analysis
      uri: http://edamontology.org/operation_3223
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Comparison
      uri: http://edamontology.org/operation_2424
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/scDD.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:34:28.003998Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/kdkorthauer/scDD
  - note: null
    type:
    - Issue tracker
    url: https://github.com/kdkorthauer/scDD/issues
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/scDD/
  maturity: null
  name: scDD
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1186/s13059-016-1077-y
    metadata:
      abstract: The ability to quantify cellular heterogeneity is a major advantage
        of single-cell technologies. However, statistical methods often treat cellular
        heterogeneity as a nuisance. We present a novel method to characterize differences
        in expression in the presence of distinct expression states within and among
        biological conditions. We demonstrate that this framework can detect differential
        expression patterns under a wide range of settings. Compared to existing approaches,
        this method has higher power to detect subtle differences in gene expression
        distributions that are more complex than a mean shift, and can characterize
        those differences. The freely available R package scDD implements the approach.
      authors:
      - name: Korthauer K.D.
      - name: Chu L.-F.
      - name: Newton M.A.
      - name: Li Y.
      - name: Thomson J.
      - name: Stewart R.
      - name: Kendziorski C.
      citationCount: 154
      date: '2016-10-25T00:00:00Z'
      journal: Genome Biology
      title: A statistical approach for identifying differential distributions in
        single-cell RNA-seq experiments
    note: null
    pmcid: PMC5080738
    pmid: '27782827'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  validated: 1
  version:
  - 1.4.0
- accessibility: null
  additionDate: '2018-07-25T15:56:51Z'
  biotoolsCURIE: biotools:runibic
  biotoolsID: runibic
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: patryk.orzechowski@gmail.com
    fundrefid: null
    gridid: null
    name: Patryk Orzechowski
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: This package implements UbiBic algorithm in R. This biclustering algorithm
    for analysis of gene expression data was introduced by Zhenjia Wang et al. in
    2016. It is currently considered the most promising biclustering method for identification
    of meaningful structures in complex and noisy data.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/runibic/man/runibic.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/runibic_1.2.3.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Clustering
      uri: http://edamontology.org/operation_3432
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/runibic.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:34:25.941521Z'
  license: MIT
  link:
  - note: null
    type:
    - Mirror
    url: http://github.com/athril/runibic
  - note: null
    type:
    - Issue tracker
    url: http://github.com/athril/runibic/issues
  maturity: null
  name: runibic
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1038/srep23466
    metadata:
      abstract: Biclustering algorithms, which aim to provide an effective and efficient
        way to analyze gene expression data by finding a group of genes with trend-preserving
        expression patterns under certain conditions, have been widely developed since
        Morgan et al. pioneered a work about partitioning a data matrix into submatrices
        with approximately constant values. However, the identification of general
        trend-preserving biclusters which are the most meaningful substructures hidden
        in gene expression data remains a highly challenging problem. We found an
        elementary method by which biologically meaningful trend-preserving biclusters
        can be readily identified from noisy and complex large data. The basic idea
        is to apply the longest common subsequence (LCS) framework to selected pairs
        of rows in an index matrix derived from an input data matrix to locate a seed
        for each bicluster to be identified. We tested it on synthetic and real datasets
        and compared its performance with currently competitive biclustering tools.
        We found that the new algorithm, named UniBic, outperformed all previous biclustering
        algorithms in terms of commonly used evaluation scenarios except for BicSPAM
        on narrow biclusters. The latter was somewhat better at finding narrow biclusters,
        the task for which it was specifically designed.
      authors:
      - name: Wang Z.
      - name: Li G.
      - name: Robinson R.W.
      - name: Huang X.
      citationCount: 51
      date: '2016-03-22T00:00:00Z'
      journal: Scientific Reports
      title: 'UniBic: Sequential row-based biclustering algorithm for analysis of
        gene expression data'
    note: null
    pmcid: PMC4802312
    pmid: '27001340'
    type:
    - Primary
    version: null
  - doi: 10.1101/210682
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Microarray experiment
    uri: http://edamontology.org/topic_3518
  validated: 1
  version:
  - 1.2.3
- accessibility: null
  additionDate: '2018-07-25T15:03:49Z'
  biotoolsCURIE: biotools:recount_bioconductor
  biotoolsID: recount_bioconductor
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: lcollado@jhu.edu
    fundrefid: null
    gridid: null
    name: Leonardo Collado-Torres
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Using this package, you can download RangedSummarizedExperiment objects
    at the gene, exon or exon-exon junctions level, the raw counts, the phenotype
    metadata used, the urls to the sample coverage bigWig files or the mean coverage
    bigWig file for a particular study. The RangedSummarizedExperiment objects can
    be used by different packages for performing differential expression analysis.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/recount/man/recount.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/recount_1.6.2.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Differential gene expression analysis
      uri: http://edamontology.org/operation_3223
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/recount.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:34:20.043372Z'
  license: BSD-2-Clause
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/leekgroup/recount
  - note: null
    type:
    - Issue tracker
    url: https://support.bioconductor.org/t/recount/
  maturity: null
  name: recount_Bioconductor
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1038/nbt.3838
    metadata:
      abstract: ''
      authors:
      - name: Collado-Torres L.
      - name: Nellore A.
      - name: Kammers K.
      - name: Ellis S.E.
      - name: Taub M.A.
      - name: Hansen K.D.
      - name: Jaffe A.E.
      - name: Langmead B.
      - name: Leek J.T.
      citationCount: 232
      date: '2017-04-11T00:00:00Z'
      journal: Nature Biotechnology
      title: Reproducible RNA-seq analysis using recount2
    note: null
    pmcid: PMC6742427
    pmid: '28398307'
    type:
    - Primary
    version: null
  - doi: 10.12688/f1000research.12223.1
    metadata:
      abstract: The recount2 resource is composed of over 70,000 uniformly processed
        human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed
        data can be accessed via the recount2 website and the recount Bioconductor
        package. This workflow explains in detail how to use the recount package and
        how to integrate it with other Bioconductor packages for several analyses
        that can be carried out with the recount2 resource. In particular, we describe
        how the coverage count matrices were computed in recount2 as well as different
        ways of obtaining public metadata, which can facilitate downstream analyses.
        Step-by-step directions show how to do a gene-level differential expression
        analysis, visualize base-level genome coverage data, and perform an analyses
        at multiple feature levels. This workflow thus provides further information
        to understand the data in recount2 and a compendium of R code to use the data.
      authors:
      - name: Collado-Torres L.
      - name: Nellore A.
      - name: Jaffe A.E.
      citationCount: 29
      date: '2017-01-01T00:00:00Z'
      journal: F1000Research
      title: 'recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor'
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gky102
    metadata:
      abstract: Publicly available genomic data are a valuable resource for studying
        normal human variation and disease, but these data are often not well labeled
        or annotated. The lack of phenotype information for public genomic data severely
        limits their utility for addressing targeted biological questions. We develop
        an in silico phenotyping approach for predicting critical missing annotation
        directly from genomic measurements using well-annotated genomic and phe-notypic
        data produced by consortia like TCGA and GTEx as training data. We apply in
        silico phenotyping to a set of 70 000 RNA-seq samples we recently processed
        on a common pipeline as part of the recount2 project. We use gene expression
        data to build and evaluate predictors for both biological phenotypes (sex,
        tissue, sample source) and experimental conditions (sequencing strategy).
        We demonstrate how these predictions can be used to study cross-sample properties
        of public genomic data, select genomic projects with specific characteristics,
        and perform downstream analyses using predicted phenotypes. The methods to
        perform phenotype prediction are available in the phenopredict R package and
        the predictions for recount2 are available from the recount R package. With
        data and phenotype information available for 70,000 human samples, expression
        data is available for use on a scale that was not previously feasible.
      authors:
      - name: Ellis S.E.
      - name: Collado-Torres L.
      - name: Jaffe A.
      - name: Leek J.T.
      citationCount: 27
      date: '2018-05-18T00:00:00Z'
      journal: Nucleic Acids Research
      title: Improving the value of public RNA-seq expression data by phenotype prediction
    note: null
    pmcid: PMC5961118
    pmid: '29514223'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  validated: 1
  version:
  - 1.6.2
- accessibility: null
  additionDate: '2018-06-30T17:49:04Z'
  biotoolsCURIE: biotools:ramwas
  biotoolsID: ramwas
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: andrey.shabalin@gmail.com
    fundrefid: null
    gridid: null
    name: Andrey A Shabalin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: A complete toolset for methylome-wide association studies (MWAS). It
    is specifically designed for data from enrichment based methylation assays, but
    can be applied to other data as well.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/ramwas/man/ramwas.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/ramwas_1.4.0.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Enrichment analysis
      uri: http://edamontology.org/operation_3501
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/ramwas.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:34:18.705494Z'
  license: LGPL-3.0
  link:
  - note: null
    type:
    - Mirror
    url: https://bioconductor.org/packages/ramwas/
  - note: null
    type:
    - Issue tracker
    url: https://github.com/andreyshabalin/ramwas/issues
  maturity: null
  name: RaMWAS
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: d.gabrielaitis
  publication:
  - doi: 10.1093/bioinformatics/bty069
    metadata:
      abstract: 'Motivation: Enrichment-based technologies can provide measurements
        of DNA methylation at tens of millions of CpGs for thousands of samples. Existing
        tools for methylome-wide association studies cannot analyze datasets of this
        size and lack important features like principal component analysis, combined
        analysis with SNP data and outcome predictions that are based on all informative
        methylation sites. Results: We present a Bioconductor R package called RaMWAS
        with a full set of tools for largescale methylome-wide association studies.
        It is free, cross-platform, open source, memory efficient and fast.'
      authors:
      - name: Shabalin A.A.
      - name: Hattab M.W.
      - name: Clark S.L.
      - name: Chan R.F.
      - name: Kumar G.
      - name: Aberg K.A.
      - name: Van Den Oord E.J.C.G.
      citationCount: 35
      date: '2018-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'RaMWAS: Fast methylome-wide association study pipeline for enrichment
        platforms'
    note: null
    pmcid: PMC6022807
    pmid: '29447401'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: GWAS study
    uri: http://edamontology.org/topic_3517
  - term: DNA methylation
    uri: http://edamontology.org/topic_3295
  validated: 1
  version:
  - 1.4.0
- accessibility: null
  additionDate: '2018-07-20T19:33:53Z'
  biotoolsCURIE: biotools:iterclust
  biotoolsID: iterclust
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: hd2326@columbia.edu
    fundrefid: null
    gridid: null
    name: Hongxu Ding
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: A framework for performing clustering analysis iteratively.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/iterClust/man/iterClust.pdf
  - note: null
    type:
    - General
    url: http://bioconductor.org/packages/release/bioc/vignettes/iterClust/inst/doc/introduction.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/iterClust_1.2.0.tar.gzc
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Clustering
      uri: http://edamontology.org/operation_3432
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/iterClust.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:33:47.574352Z'
  license: Other
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/hd2326/iterClust
  - note: null
    type:
    - Issue tracker
    url: https://github.com/hd2326/iterClust/issues
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/iterClust/
  maturity: null
  name: iterClust
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1093/bioinformatics/bty176
    metadata:
      abstract: 'Motivation: In a scenario where populations A, B1 and B2 (subpopulations
        of B) exist, pronounced differences between A and B may mask subtle differences
        between B1 and B2. Results: Here we present iterClust, an iterative clustering
        framework, which can separate more pronounced differences (e.g. A and B) in
        starting iterations, followed by relatively subtle differences (e.g. B1 and
        B2), providing a comprehensive clustering trajectory.'
      authors:
      - name: Ding H.
      - name: Wang W.
      - name: Califano A.
      citationCount: 7
      date: '2018-08-15T00:00:00Z'
      journal: Bioinformatics
      title: 'iterClust: a statistical framework for iterative clustering analysis'
    note: null
    pmcid: PMC6084607
    pmid: '29579153'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 1
  version:
  - 1.2.0
- accessibility: null
  additionDate: '2018-07-01T13:07:46Z'
  biotoolsCURIE: biotools:ddpcrclust
  biotoolsID: ddpcrclust
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: null
  credit:
  - email: bbrink@cebitec.uni-bielefeld.de
    fundrefid: null
    gridid: null
    name: Benedikt G Brink
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Automated quantification of non-orthogonal, multiplexed ddPCR data.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/bgbrink/ddPCRclust
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/ddPCRclust_1.0.1.tar.gz
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene expression analysis
      uri: http://edamontology.org/operation_2495
    output: []
  homepage: https://github.com/bgbrink/ddPCRclust
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:33:37.196482Z'
  license: Artistic-2.0
  link:
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/ddPCRclust/
  - note: null
    type:
    - Issue tracker
    url: https://github.com/bgbrink/ddPCRclust/issues
  maturity: null
  name: ddPCRclust
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: d.gabrielaitis
  publication:
  - doi: 10.1093/bioinformatics/bty136
    metadata:
      abstract: 'Motivation: Droplet digital PCR (ddPCR) is an emerging technology
        for quantifying DNA. By partitioning the target DNA into 20 000 droplets,
        each serving as its own PCR reaction compartment, a very high sensitivity
        of DNA quantification can be achieved. However, manual analysis of the data
        is time consuming and algorithms for automated analysis of non-orthogonal,
        multiplexed ddPCR data are unavailable, presenting a major bottleneck for
        the advancement of ddPCR transitioning from low-throughput to high-throughput.
        Results: ddPCRclust is an R package for automated analysis of data from Bio-Rad''s
        droplet digital PCR systems (QX100 and QX200). It can automatically analyze
        and visualize multiplexed ddPCR experiments with up to four targets per reaction.
        Results are on par with manual analysis, but only take minutes to compute
        instead of hours. The accompanying Shiny app ddPCRvis provides easy access
        to the functionalities of ddPCRclust through a web-browser based GUI.'
      authors:
      - name: Brink B.G.
      - name: Meskas J.
      - name: Brinkman R.R.
      citationCount: 15
      date: '2018-08-01T00:00:00Z'
      journal: Bioinformatics
      title: 'DdPCRclust: An R package and Shiny app for automated analysis of multiplexed
        ddPCR data'
    note: null
    pmcid: PMC6061851
    pmid: '29534153'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  - Web application
  topic:
  - term: PCR experiment
    uri: http://edamontology.org/topic_3519
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Probes and primers
    uri: http://edamontology.org/topic_0632
  - term: Biomarkers
    uri: http://edamontology.org/topic_3360
  validated: 1
  version:
  - 1.0.1
- accessibility: null
  additionDate: '2018-08-06T04:34:05Z'
  biotoolsCURIE: biotools:ccb
  biotoolsID: ccb
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: jeremy_purvis@med.unc.edu
    fundrefid: null
    gridid: null
    name: Jeremy E. Purvis
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: The Human Cell Cycle Browser (hCCB) is an interactive web tool for
    visualizing and simulating the human cell cycle to explore the timeline of molecular
    events during cell cycle progression.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/RENCI/CellCycleBrowser/wiki
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    output: []
  homepage: https://cellcycle.renci.org/
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2024-11-25T14:27:51.380681Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/RENCI/CellCycleBrowser/issues
  maturity: null
  name: Cell Cycle Browser (CCB)
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: daniel_stefan
  publication:
  - doi: 10.1016/j.cels.2018.06.004
    metadata:
      abstract: The cell cycle is driven by precise temporal coordination among many
        molecular activities. To understand and explore this process, we developed
        the Cell Cycle Browser (CCB), an interactive web interface based on real-time
        reporter data collected in proliferating human cells. This tool facilitates
        visualizing, organizing, simulating, and predicting the outcomes of perturbing
        cell-cycle parameters. Time-series traces from individual cells can be combined
        to build a multi-layered timeline of molecular activities. Users can simulate
        the cell cycle using computational models that capture the dynamics of molecular
        activities and phase transitions. By adjusting individual expression levels
        and strengths of molecular relationships, users can predict effects on the
        cell cycle. Virtual assays, such as growth curves and flow cytometry, provide
        familiar outputs to compare cell-cycle behaviors for data and simulations.
        The CCB serves to unify our understanding of cell-cycle dynamics and provides
        a platform for generating hypotheses through virtual experiments. Borland,
        Yi, and colleagues develop an interactive website offering visualization,
        simulation, and virtual experiments to study cell-cycle progression at the
        molecular level.
      authors:
      - name: Borland D.
      - name: Yi H.
      - name: Grant G.D.
      - name: Kedziora K.M.
      - name: Chao H.X.
      - name: Haggerty R.A.
      - name: Kumar J.
      - name: Wolff S.C.
      - name: Cook J.G.
      - name: Purvis J.E.
      citationCount: 4
      date: '2018-08-22T00:00:00Z'
      journal: Cell Systems
      title: 'The Cell Cycle Browser: An Interactive Tool for Visualizing, Simulating,
        and Perturbing Cell-Cycle Progression'
    note: null
    pmcid: PMC6214685
    pmid: '30077635'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Molecular biology
    uri: http://edamontology.org/topic_3047
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2018-08-06T00:20:44Z'
  biotoolsCURIE: biotools:inspiired
  biotoolsID: inspiired
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: bushman@mail.med.upenn.edu
    fundrefid: null
    gridid: null
    name: Frederic D. Bushman
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: INSPIIRED is a software suite designed to study viral integration sites
    and the longitudinal outcomes of gene therapy patients.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/BushmanLab/INSPIIRED/blob/master/README.md
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output: []
  homepage: https://github.com/BushmanLab/INSPIIRED
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:27:50.030219Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/BushmanLab/INSPIIRED/issues
  maturity: null
  name: INSPIIRED
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: daniel_stefan
  publication:
  - doi: 10.1016/j.omtm.2016.11.003
    metadata:
      abstract: Analysis of sites of newly integrated DNA in cellular genomes is important
        to several fields, but methods for analyzing and visualizing these datasets
        are still under development. Here, we describe tools for data analysis and
        visualization that take as input integration site data from our INSPIIRED
        pipeline. Paired-end sequencing allows inference of the numbers of transduced
        cells as well as the distributions of integration sites in target genomes.
        We present interactive heatmaps that allow comparison of distributions of
        integration sites to genomic features and that support numerous user-defined
        statistical tests. To summarize integration site data from human gene therapy
        samples, we developed a reproducible report format that catalogs sample population
        structure, longitudinal dynamics, and integration frequency near cancer-associated
        genes. We also introduce a novel summary statistic, the UC50 (unique cell
        progenitors contributing the most expanded 50% of progeny cell clones), which
        provides a single number summarizing possible clonal expansion. Using these
        tools, we characterize ongoing longitudinal characterization of a patient
        from the first trial to treat severe combined immunodeficiency-X1 (SCID-X1),
        showing successful reconstitution for 15 years accompanied by persistence
        of a cell clone with an integration site near the cancer-associated gene CCND2.
        Software is available at https://github.com/BushmanLab/INSPIIRED.
      authors:
      - name: Berry C.C.
      - name: Nobles C.
      - name: Six E.
      - name: Wu Y.
      - name: Malani N.
      - name: Sherman E.
      - name: Dryga A.
      - name: Everett J.K.
      - name: Male F.
      - name: Bailey A.
      - name: Bittinger K.
      - name: Drake M.J.
      - name: Caccavelli L.
      - name: Bates P.
      - name: Hacein-Bey-Abina S.
      - name: Cavazzana M.
      - name: Bushman F.D.
      citationCount: 57
      date: '2017-03-17T00:00:00Z'
      journal: Molecular Therapy Methods and Clinical Development
      title: 'INSPIIRED: Quantification and Visualization Tools for Analyzing Integration
        Site Distributions'
    note: null
    pmcid: PMC5363318
    pmid: '28344988'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Suite
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Epigenetics
    uri: http://edamontology.org/topic_3295
  - term: DNA replication and recombination
    uri: http://edamontology.org/topic_3127
  validated: 1
  version: []
- accessibility: null
  additionDate: '2018-07-15T21:52:58Z'
  biotoolsCURIE: biotools:tnt
  biotoolsID: tnt
  collectionID:
  - BioConductor
  confidence_flag: null
  cost: null
  credit:
  - email: marlin@gmx.cn
    fundrefid: null
    gridid: null
    name: Jialin Ma
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: A R interface to the TnT javascript library (https://github.com/ tntvis)
    to provide interactive and flexible visualization of track-based genomic data.
  documentation:
  - note: null
    type:
    - User manual
    url: http://bioconductor.org/packages/release/bioc/manuals/TnT/man/TnT.pdf
  download:
  - note: null
    type: Software package
    url: http://bioconductor.org/packages/release/bioc/src/contrib/TnT_1.2.0.tar.gz
    version: null
  - note: null
    type: Source code
    url: http://bioconductor.org/packages/release/bioc/vignettes/TnT/inst/doc/introduction.R
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: http://bioconductor.org/packages/release/bioc/html/TnT.html
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:27:16.454532Z'
  license: AGPL-3.0
  link:
  - note: null
    type:
    - Mirror
    url: https://github.com/Marlin-Na/TnT
  - note: null
    type:
    - Issue tracker
    url: https://github.com/Marlin-Na/TnT/issues
  - note: null
    type:
    - Mirror
    url: http://bioconductor.org/packages/TnT/
  - note: null
    type:
    - Repository
    url: https://git.bioconductor.org/packages/TnT
  maturity: null
  name: TnT
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: shadi.m
  publication:
  - doi: 10.1093/bioinformatics/btw210
    metadata:
      abstract: 'Summary: There is an increasing need for rich and dynamic biological
        data visualizations in bioinformatic web applications. New standards in web
        technologies, like SVG or Canvas, are now supported by most modern web browsers
        allowing the blossoming of powerful visualizations in biological data analysis.
        The exploration of different ways to visualize genomic data is still challenging
        due to the lack of flexible tools to develop them. Here, I present a set of
        libraries aimed at creating powerful tree- and track-based visualizations
        for the web. Its modularity and rich API facilitate the development of many
        different visualizations ranging from simple species trees to complex visualizations
        comprising per-node data annotations or even simple genome browsers.'
      authors:
      - name: Pignatelli M.
      citationCount: 4
      date: '2016-08-15T00:00:00Z'
      journal: Bioinformatics
      title: 'TnT: A set of libraries for visualizing trees and track-based annotations
        for the web'
    note: null
    pmcid: PMC4978938
    pmid: '27153646'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 1
  version:
  - 1.2.0
- accessibility: null
  additionDate: '2018-07-18T09:31:39Z'
  biotoolsCURIE: biotools:raven_toolbox
  biotoolsID: raven_toolbox
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://gitter.im/SysBioChalmers/RAVEN?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
  - email: hao.wang@chalmers.se
    fundrefid: null
    gridid: null
    name: Hao Wang
    note: null
    orcidid: http://orcid.org/0000-0001-7475-0136
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: The RAVEN (Reconstruction, Analysis and Visualization of Metabolic
    Networks) Toolbox 2 is a software suite for Matlab that allows for semi-automated
    reconstruction of genome-scale models (GEMs). It makes use of published models
    and/or KEGG, MetaCyc databases, coupled with extensive gap-filling and quality
    control features. The software suite also contains methods for visualizing simulation
    results and omics data, as well as a range of methods for performing simulations
    and analyzing the results. The software is a useful tool for system-wide data
    analysis in a metabolic context and for streamlined reconstruction of metabolic
    networks based on protein homology.
  documentation:
  - note: GitHub Wiki
    type:
    - General
    url: https://github.com/SysBioChalmers/RAVEN/wiki
  download:
  - note: Stabile releases
    type: Binaries
    url: https://github.com/SysBioChalmers/RAVEN/releases
    version: null
  - note: Source code
    type: Source code
    url: https://github.com/SysBioChalmers/RAVEN
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: protein
        uri: http://edamontology.org/format_1208
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: xlsx
        uri: http://edamontology.org/format_3620
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Pathway or network prediction
      uri: http://edamontology.org/operation_3439
    - term: Metabolic network modelling
      uri: http://edamontology.org/operation_3660
    - term: Pathway or network analysis
      uri: http://edamontology.org/operation_2497
    output:
    - data:
        term: Pathway or network
        uri: http://edamontology.org/data_2600
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: xlsx
        uri: http://edamontology.org/format_3620
      - term: YAML
        uri: http://edamontology.org/format_3750
    - data:
        term: Physiology parameter
        uri: http://edamontology.org/data_3722
      format:
      - term: raw
        uri: http://edamontology.org/format_1957
  homepage: https://github.com/SysBioChalmers/RAVEN
  homepage_status: 0
  language:
  - MATLAB
  lastUpdate: '2024-11-25T14:23:23.622737Z'
  license: GPL-3.0
  link:
  - note: Gitter chat room
    type:
    - Mailing list
    url: https://gitter.im/SysBioChalmers/RAVEN
  - note: GitHub Issues
    type:
    - Issue tracker
    url: https://github.com/SysBioChalmers/RAVEN/issues
  - note: GitHub Repository
    type:
    - Repository
    url: https://github.com/SysBioChalmers/RAVEN
  - note: GitHub Wiki
    type:
    - Helpdesk
    url: https://github.com/SysBioChalmers/RAVEN/wiki
  maturity: Mature
  name: RAVEN Toolbox
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: eduardk
  publication:
  - doi: 10.1101/321067
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Script
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Bioengineering
    uri: http://edamontology.org/topic_3398
  validated: 1
  version:
  - '2.0'
- accessibility: null
  additionDate: '2018-06-02T07:25:33Z'
  biotoolsCURIE: biotools:gbm.auto
  biotoolsID: gbm.auto
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: simondedman@gmail.com
    fundrefid: null
    gridid: null
    name: Simon Dedman
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Process and predict mapping of species abundance data. The package
    automates and greatly simplifies Boosted Regression Tree spatial modelling and
    makes this powerful statistical modelling technique more accessible to potential
    users in the ecological and modelling communities.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/SimonDedman/gbm.auto/blob/master/README.md
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Map drawing
      uri: http://edamontology.org/operation_0573
    output: []
  homepage: https://github.com/SimonDedman/gbm.auto
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-11-25T14:22:47.664052Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/SimonDedman/gbm.auto/issues
  maturity: null
  name: gbm.auto
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: migle_sur
  publication:
  - doi: 10.1371/journal.pone.0192520
    metadata:
      abstract: 'The subheading “Worked examplePre-run parameter scoping with gbm.bfcheck”
        under the Guide to software functions section is incorrect. The subheadings
        should be “Worked example” and “Pre-run parameter scoping with gbm.bfcheck”.
        The following information is missing from the eighth paragraph of the Guide
        to software functions section under the subheading “Worked example”: To demonstrate
        gbm.auto we will run a simple example in the sections below; here we load
        the required functions and data. Please see Supplementary Material D1 for
        all details of each function and more fully worked examples. install.packages("devtools")
        library("devtools") install_github(’SimonDedman/gbm.auto’) library("gbm.auto")
        mygrids <- gbm.auto::grids # load grids mysamples <- gbm.auto::samples # load
        samples'
      authors: []
      citationCount: 0
      date: '2018-02-01T00:00:00Z'
      journal: PLoS ONE
      title: 'Correction: Gbm.auto: A software tool to simplify spatial modelling
        and Marine Protected Area planning (PLoS ONE (2017) 12:12 (e0188955) DOI:
        10.1371/journal.pone.0188955)'
    note: null
    pmcid: PMC5796701
    pmid: '29394292'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Library
  topic:
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Marine biology
    uri: http://edamontology.org/topic_3387
  validated: 1
  version: []
- accessibility: null
  additionDate: '2018-07-15T10:12:10Z'
  biotoolsCURIE: biotools:guidock-vnc
  biotoolsID: guidock-vnc
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: kayee@uw.edu
    fundrefid: null
    gridid: null
    name: Ka Yee Yeung
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Using a graphical desktop sharing system to provide a browser-based
    interface for containerized software.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/BioDepot/GUIdock-VNC
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Service composition
      uri: http://edamontology.org/operation_3762
    output: []
  homepage: https://github.com/BioDepot/GUIdock-VNC
  homepage_status: 0
  language:
  - JavaScript
  - Java
  - R
  - Python
  lastUpdate: '2024-11-25T14:22:37.744584Z'
  license: MIT
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/BioDepot/GUIdock-VNC/issues
  maturity: null
  name: GUIdock-VNC
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: d.gabrielaitis
  publication:
  - doi: 10.1093/gigascience/giw013
    metadata:
      abstract: 'Background: Software container technology such as Docker can be used
        to package and distribute bioinformatics workflows consisting of multiple
        software implementations and dependencies. However, Docker is a command line-based
        tool, and many bioinformatics pipelines consist of components that require
        a graphical user interface. Results: We present a container tool called GUIdock-VNC
        that uses a graphical desktop sharing system to provide a browser-based interface
        for containerized software. GUIdock-VNC uses the Virtual Network Computing
        protocol to render the graphics within most commonly used browsers. We also
        present a minimal image builder that can add our proposed graphical desktop
        sharing system to any Docker packages, with the end result that any Docker
        packages can be run using a graphical desktop within a browser. In addition,
        GUIdock-VNC uses the Oauth2 authentication protocols when deployed on the
        cloud. Conclusions: As a proof-of-concept, we demonstrated the utility of
        GUIdock-noVNC in gene network inference. We benchmarked our container implementation
        on various operating systems and showed that our solution creates minimal
        overhead.'
      authors:
      - name: Mittal V.
      - name: Hung L.-H.
      - name: Keswani J.
      - name: Kristiyanto D.
      - name: Lee S.B.
      - name: Yeung K.Y.
      citationCount: 6
      date: '2017-04-01T00:00:00Z'
      journal: GigaScience
      title: 'GUIdock-VNC: Using a graphical desktop sharing system to provide a browser-based
        interface for containerized software'
    note: null
    pmcid: PMC5530313
    pmid: '28327936'
    type: []
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Software engineering
    uri: http://edamontology.org/topic_3372
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Genetics
    uri: http://edamontology.org/topic_3053
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-08-03T18:47:58Z'
  biotoolsCURIE: biotools:lever
  biotoolsID: lever
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: andrew.r.cohen@drexel.edu
    fundrefid: null
    gridid: null
    name: Andrew R. Cohen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://bioimage.coe.drexel.edu/mp/contact/
  - email: null
    fundrefid: null
    gridid: null
    name: Mark R. Winter
    note: null
    orcidid: http://orcid.org/0000-0003-1180-1957
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  description: Collection of software tools for analyzing the development of proliferating
    (dividing) cells in time-lapse microscopy image sequences. It includes algorithms
    for segmentation, tracking and lineaging. It also includes a user interface that
    allows the segmentation, tracking and lineaging results to be validated, with
    any errors in the automated processing easily identified and quickly corrected.
  documentation:
  - note: null
    type:
    - General
    url: https://bioimage.coe.drexel.edu/mp/lever/index.php/sample-movies/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Image analysis
      uri: http://edamontology.org/operation_3443
    output: []
  homepage: https://bioimage.coe.drexel.edu/mp/lever/
  homepage_status: 0
  language:
  - MATLAB
  lastUpdate: '2024-11-25T14:22:31.353359Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Software catalogue
    url: http://www.mybiosoftware.com/lever-stem-cell-segmentation-tracking-and-lineaging.html
  - note: null
    type:
    - Issue tracker
    url: https://git-bioimage.coe.drexel.edu/opensource/lever/issues
  maturity: null
  name: LEVER
  operatingSystem:
  - Windows
  otherID: []
  owner: mbs_import
  publication:
  - doi: 10.1093/bioinformatics/btw406
    metadata:
      abstract: The analysis of time-lapse images showing cells dividing to produce
        clones of related cells is an important application in biological microscopy.
        Imaging at the temporal resolution required to establish accurate tracking
        for vertebrate stem or cancer cells often requires the use of transmitted
        light or phase-contrast microscopy. Processing these images requires automated
        segmentation, tracking and lineaging algorithms. There is also a need for
        any errors in the automated processing to be easily identified and quickly
        corrected. We have developed LEVER, an open source software tool that combines
        the automated image analysis for phase-contrast microscopy movies with an
        easy-to-use interface for validating the results and correcting any errors.
      authors:
      - name: Winter M.
      - name: Mankowski W.
      - name: Wait E.
      - name: Temple S.
      - name: Cohen A.R.
      citationCount: 39
      date: '2016-11-15T00:00:00Z'
      journal: Bioinformatics
      title: 'LEVER: Software tools for segmentation, tracking and lineaging of proliferating
        cells'
    note: null
    pmcid: PMC5181556
    pmid: '27423896'
    type: []
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Light microscopy
    uri: http://edamontology.org/topic_3385
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-08-03T19:02:53Z'
  biotoolsCURIE: biotools:transit
  biotoolsID: transit
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: michael.dejesus@tamu.edu
    fundrefid: null
    gridid: null
    name: Michael A. DeJesus
    note: null
    orcidid: http://orcid.org/0000-0003-3867-0299
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://students.cs.tamu.edu/mad/
  description: A tool for the analysis of Tn-Seq data. It provides an easy to use
    graphical interface and access to three different analysis methods that allow
    the user to determine essentiality in a single condition as well as between conditions.
  documentation:
  - note: null
    type:
    - General
    url: http://transit.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Transposon prediction
      uri: http://edamontology.org/operation_0427
    output: []
  homepage: http://saclab.tamu.edu/essentiality/transit/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-11-25T14:22:27.846591Z'
  license: null
  link:
  - note: null
    type:
    - Software catalogue
    url: http://www.mybiosoftware.com/transit-analysis-of-tn-seq-data.html
  - note: null
    type:
    - Repository
    url: https://github.com/mad-lab/transit
  - note: null
    type:
    - Issue tracker
    url: https://github.com/mad-lab/transit/issues
  maturity: null
  name: TRANSIT
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: mbs_import
  publication:
  - doi: 10.1371/journal.pcbi.1004401
    metadata:
      abstract: 'TnSeq has become a popular technique for determining the essentiality
        of genomic regions in bacterial organisms. Several methods have been developed
        to analyze the wealth of data that has been obtained through TnSeq experiments.
        We developed a tool for analyzing Himar1 TnSeq data called TRANSIT. TRANSIT
        provides a graphical interface to three different statistical methods for
        analyzing TnSeq data. These methods cover a variety of approaches capable
        of identifying essential genes in individual datasets as well as comparative
        analysis between conditions. We demonstrate the utility of this software by
        analyzing TnSeq datasets of M. tuberculosis grown on glycerol and cholesterol.
        We show that TRANSIT can be used to discover genes which have been previously
        implicated for growth on these carbon sources. TRANSIT is written in Python,
        and thus can be run on Windows, OSX and Linux platforms. The source code is
        distributed under the GNU GPL v3 license and can be obtained from the following
        GitHub repository: https://github.com/mad-lab/transit'
      authors:
      - name: DeJesus M.A.
      - name: Ambadipudi C.
      - name: Baker R.
      - name: Sassetti C.
      - name: Ioerger T.R.
      citationCount: 119
      date: '2015-01-01T00:00:00Z'
      journal: PLoS Computational Biology
      title: TRANSIT - A Software Tool for Himar1 TnSeq Analysis
    note: null
    pmcid: PMC4598096
    pmid: '26447887'
    type: []
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: DNA
    uri: http://edamontology.org/topic_0654
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Mobile genetic elements
    uri: http://edamontology.org/topic_0798
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-08-03T18:59:43Z'
  biotoolsCURIE: biotools:sina
  biotoolsID: sina
  collectionID:
  - SINA
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: contact@arb-silva.de
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://www.arb-silva.de/contact/
  description: 'Aligns and optionally taxonomically classifies your rRNA gene sequences.


    Reference based multiple sequence alignment'
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/epruesse/SINA
  download:
  - note: null
    type: Source code
    url: https://github.com/epruesse/SINA/releases
    version: null
  editPermission:
    authors:
    - epruesse
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence alignment analysis
      uri: http://edamontology.org/operation_0258
    output: []
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Multiple sequence alignment
      uri: http://edamontology.org/operation_0492
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    - term: Structure-based sequence alignment
      uri: http://edamontology.org/operation_0294
    output:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://sina.readthedocs.io
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2024-11-25T14:22:22.697934Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Software catalogue
    url: http://www.mybiosoftware.com/sina-high-throughput-multiple-sequence-alignment-of-ribosomal-rna-genes.html
  - note: null
    type:
    - Issue tracker
    url: https://github.com/epruesse/SINA/issues
  maturity: Mature
  name: SINA
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: epruesse
  publication:
  - doi: 10.1093/bioinformatics/bts252
    metadata:
      abstract: 'Motivation: In the analysis of homologous sequences, computation
        of multiple sequence alignments (MSAs) has become a bottleneck. This is especially
        troublesome for marker genes like the ribosomal RNA (rRNA) where already millions
        of sequences are publicly available and individual studies can easily produce
        hundreds of thousands of new sequences. Methods have been developed to cope
        with such numbers, but further improvements are needed to meet accuracy requirements.Results:
        In this study, we present the SILVA Incremental Aligner (SINA) used to align
        the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA
        uses a combination of k-mer searching and partial order alignment (POA) to
        maintain very high alignment accuracy while satisfying high throughput performance
        demands. SINA was evaluated in comparison with the commonly used high throughput
        MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could
        be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising
        38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference
        MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy
        than PyNAST and mothur in all performed benchmarks. © The Author(s) 2012.
        Published by Oxford University Press.'
      authors:
      - name: Pruesse E.
      - name: Peplies J.
      - name: Glockner F.O.
      citationCount: 2327
      date: '2012-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'SINA: Accurate high-throughput multiple sequence alignment of ribosomal
        RNA genes'
    note: null
    pmcid: PMC3389763
    pmid: '22556368'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: RNA
    uri: http://edamontology.org/topic_0099
  - term: Nucleic acid structure analysis
    uri: http://edamontology.org/topic_0097
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  validated: 1
  version:
  - 1.3.1
  - 1.5.0
- accessibility: null
  additionDate: '2018-02-22T19:13:08Z'
  biotoolsCURIE: biotools:arb
  biotoolsID: arb
  collectionID:
  - ARB
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: arb@arb-home.de
    fundrefid: null
    gridid: null
    name: ARB Users Mailing List
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: The ARB software is a graphically oriented package comprising various
    tools for sequence database handling and data analysis. A central database of
    processed (aligned) sequences and any type of additional data linked to the respective
    sequence entries is structured according to phylogeny or other user defined criteria
  documentation: []
  download:
  - note: null
    type: Binaries
    url: http://download.arb-home.de/release/latest/
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Sequence similarity search
      uri: http://edamontology.org/operation_0346
    - term: Base position variability plotting
      uri: http://edamontology.org/operation_0287
    - term: Sequence alignment conversion
      uri: http://edamontology.org/operation_0260
    - term: Structure-based sequence alignment
      uri: http://edamontology.org/operation_0294
    - term: Phylogenetic tree visualisation
      uri: http://edamontology.org/operation_0567
    - term: Sequence alignment editing
      uri: http://edamontology.org/operation_3081
    - term: Phylogenetic tree editing
      uri: http://edamontology.org/operation_0326
    - term: Sequence distance matrix generation
      uri: http://edamontology.org/operation_0289
    - term: RNA secondary structure prediction
      uri: http://edamontology.org/operation_0278
    - term: Multiple sequence alignment
      uri: http://edamontology.org/operation_0492
    - term: Sequence alignment comparison
      uri: http://edamontology.org/operation_0259
    - term: Sequence masking
      uri: http://edamontology.org/operation_0368
    - term: Sequence clustering
      uri: http://edamontology.org/operation_0291
    - term: Sequence database search (by property)
      uri: http://edamontology.org/operation_0349
    - term: Global alignment
      uri: http://edamontology.org/operation_0496
    - term: Phylogenetic tree annotation
      uri: http://edamontology.org/operation_0558
    - term: Structure editing
      uri: http://edamontology.org/operation_3080
    - term: DNA translation
      uri: http://edamontology.org/operation_0371
    output: []
  homepage: http://arb-home.de
  homepage_status: 0
  language:
  - Shell
  - C++
  - Perl
  lastUpdate: '2024-11-25T14:21:30.842496Z'
  license: Proprietary
  link:
  - note: null
    type:
    - Mailing list
    url: https://groups.yahoo.com/neo/groups/arb_users/info
  - note: null
    type:
    - Issue tracker
    url: http://bugs.arb-home.de/
  maturity: Mature
  name: ARB
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: epruesse
  publication:
  - doi: 10.1093/nar/gkh293
    metadata:
      abstract: The ARB (from Latin arbor, tree) project was initiated almost 10 years
        ago. The ARB program package comprises a variety of directly interacting software
        tools for sequence database maintenance and analysis which are controlled
        by a common graphical user interface. Although it was initially designed for
        ribosomal RNA data, it can be used for any nucleic and amino acid sequence
        data as well. A central database contains processed (aligned) primary structure
        data. Any additional descriptive data can be stored in database fields assigned
        to the individual sequences or linked via local or worldwide networks. A phylogenetic
        tree visualized in the main window can be used for data access and visualization.
        The package comprises additional tools for data import and export, sequence
        alignment, primary and secondary structure editing, profile and filter calculation,
        phylogenetic analyses, specific hybridization probe design and evaluation
        and other components for data analysis. Currently, the package is used by
        numerous working groups worldwide. © Oxford University Press 2004; all rights
        reserved.
      authors:
      - name: Ludwig W.
      - name: Strunk O.
      - name: Westram R.
      - name: Richter L.
      - name: Meier H.
      - name: Yadhukumar A.
      - name: Buchner A.
      - name: Lai T.
      - name: Steppi S.
      - name: Jacob G.
      - name: Forster W.
      - name: Brettske I.
      - name: Gerber S.
      - name: Ginhart A.W.
      - name: Gross O.
      - name: Grumann S.
      - name: Hermann S.
      - name: Jost R.
      - name: Konig A.
      - name: Liss T.
      - name: Lussbmann R.
      - name: May M.
      - name: Nonhoff B.
      - name: Reichel B.
      - name: Strehlow R.
      - name: Stamatakis A.
      - name: Stuckmann N.
      - name: Vilbig A.
      - name: Lenke M.
      - name: Ludwig T.
      - name: Bode A.
      - name: Schleifer K.-H.
      citationCount: 5442
      date: '2004-07-14T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ARB: A software environment for sequence data'
    note: null
    pmcid: PMC390282
    pmid: '14985472'
    type:
    - Primary
    version: null
  - doi: 10.1002/9781118010518.ch46
    metadata:
      abstract: ''
      authors:
      - name: Westram R.
      - name: Bader K.
      - name: Prusse E.
      - name: Kumar Y.
      - name: Meier H.
      - name: Glockner F.O.
      - name: Ludwig W.
      citationCount: 79
      date: '2011-05-03T00:00:00Z'
      journal: 'Handbook of Molecular Microbial Ecology I: Metagenomics and Complementary
        Approaches'
      title: 'ARB: A Software Environment for Sequence Data'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 1
  version:
  - 6.0.6
- accessibility: null
  additionDate: '2017-01-13T13:14:15Z'
  biotoolsCURIE: biotools:curtain
  biotoolsID: curtain
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: mhaimel.ebi@googlemail.com
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Curtain is a Java wrapper around next-generation assemblers such as
    Velvet which allows the incremental introduction of read-pair information into
    the assembly process. This enables the assembly of larger genomes than would otherwise
    be possible within existing memory constraints.
  documentation:
  - note: null
    type:
    - General
    url: https://code.google.com/archive/p/curtain/wikis
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Sequence assembly
      uri: http://edamontology.org/operation_0310
    output: []
  homepage: http://code.google.com/p/curtain/
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2024-11-25T14:20:16.624148Z'
  license: null
  link:
  - note: null
    type:
    - Software catalogue
    url: http://www.mybiosoftware.com/curtain-0-2-3-beta-assembling-large-genomes-short-read-sequences.html
  - note: Discussion forum
    type:
    - Issue tracker
    url: https://groups.google.com/forum/#!forum/curtain-assembler
  maturity: Emerging
  name: Curtain
  operatingSystem: []
  otherID: []
  owner: seqwiki_import
  publication:
  - doi: 10.1186/2047-217x-2-10
    metadata:
      abstract: 'Background: The process of generating raw genome sequence data continues
        to become cheaper, faster, and more accurate. However, assembly of such data
        into high-quality, finished genome sequences remains challenging. Many genome
        assembly tools are available, but they differ greatly in terms of their performance
        (speed, scalability, hardware requirements, acceptance of newer read technologies)
        and in their final output (composition of assembled sequence). More importantly,
        it remains largely unclear how to best assess the quality of assembled genome
        sequences. The Assemblathon competitions are intended to assess current state-of-the-art
        methods in genome assembly. Results: In Assemblathon 2, we provided a variety
        of sequence data to be assembled for three vertebrate species (a bird, a fish,
        and snake). This resulted in a total of 43 submitted assemblies from 21 participating
        teams. We evaluated these assemblies using a combination of optical map data,
        Fosmid sequences, and several statistical methods. From over 100 different
        metrics, we chose ten key measures by which to assess the overall quality
        of the assemblies. Conclusions: Many current genome assemblers produced useful
        assemblies, containing a significant representation of their genes and overall
        genome structure. However, the high degree of variability between the entries
        suggests that there is still much room for improvement in the field of genome
        assembly and that approaches which work well in assembling the genome of one
        species may not necessarily work well for another.'
      authors:
      - name: Bradnam K.R.
      - name: Fass J.N.
      - name: Alexandrov A.
      - name: Baranay P.
      - name: Bechner M.
      - name: Birol I.
      - name: Boisvert S.
      - name: Chapman J.A.
      - name: Chapuis G.
      - name: Chikhi R.
      - name: Chitsaz H.
      - name: Chou W.-C.
      - name: Corbeil J.
      - name: Fabbro C.D.
      - name: Docking T.R.
      - name: Durbin R.
      - name: Earl D.
      - name: Emrich S.
      - name: Fedotov P.
      - name: Fonseca N.A.
      - name: Ganapathy G.
      - name: Gibbs R.A.
      - name: Gnerre S.
      - name: Godzaridis E.
      - name: Goldstein S.
      - name: Haimel M.
      - name: Hall G.
      - name: Haussler D.
      - name: Hiatt J.B.
      - name: Ho I.Y.
      - name: Howard J.
      - name: Hunt M.
      - name: Jackman S.D.
      - name: Jaffe D.B.
      - name: Jarvis E.D.
      - name: Jiang H.
      - name: Kazakov S.
      - name: Kersey P.J.
      - name: Kitzman J.O.
      - name: Knight J.R.
      - name: Koren S.
      - name: Lam T.-W.
      - name: Lavenier D.
      - name: Laviolette F.
      - name: Li Y.
      - name: Li Z.
      - name: Liu B.
      - name: Liu Y.
      - name: Luo R.
      - name: MacCallum I.
      - name: MacManes M.D.
      - name: Maillet N.
      - name: Melnikov S.
      - name: Naquin D.
      - name: Ning Z.
      - name: Otto T.D.
      - name: Paten B.
      - name: Paulo O.S.
      - name: Phillippy A.M.
      - name: Pina-Martins F.
      - name: Place M.
      - name: Przybylski D.
      - name: Qin X.
      - name: Qu C.
      - name: Ribeiro F.J.
      - name: Richards S.
      - name: Rokhsar D.S.
      - name: Ruby J.G.
      - name: Scalabrin S.
      - name: Schatz M.C.
      - name: Schwartz D.C.
      - name: Sergushichev A.
      - name: Sharpe T.
      - name: Shaw T.I.
      - name: Shendure J.
      - name: Shi Y.
      - name: Simpson J.T.
      - name: Song H.
      - name: Tsarev F.
      - name: Vezzi F.
      - name: Vicedomini R.
      - name: Vieira B.M.
      - name: Wang J.
      - name: Worley K.C.
      - name: Yin S.
      - name: Yiu S.-M.
      - name: Yuan J.
      - name: Zhang G.
      - name: Zhang H.
      - name: Zhou S.
      - name: Korf I.F.
      citationCount: 481
      date: '2013-01-01T00:00:00Z'
      journal: GigaScience
      title: 'Assemblathon 2: Evaluating de novo methods of genome assembly in three
        vertebrate species'
    note: null
    pmcid: PMC3844414
    pmid: '23870653'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 1
  version: []
next: ?page=2
previous: null
