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        converting between. hic and. mcool formats. We benchmark the performance of
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      authors:
      - name: Rossini R.
      - name: Paulsen J.
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    name: Hakim Achterberg
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  description: Python package to interact with the XNAT REST API in a python data-science
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  additionDate: '2025-07-10T12:32:00.838316Z'
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  description: InMoose, the INtegrated Multi-Omic Open-Source environment, ports to
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    metadata:
      abstract: We introduce InMoose, an open-source Python environment aimed at omic
        data analysis. We illustrate its capabilities for bulk transcriptomic data
        analysis. Due to its wide adoption, Python has grown as a de facto standard
        in fields increasingly important for bioinformatic pipelines, such as data
        science, machine learning, or artificial intelligence (AI). As a general-purpose
        language, Python is also recognized for its versatility and scalability. InMoose
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        Python ecosystem. InMoose focuses on providing drop-in replacements for R
        tools, to ensure consistency and reproducibility between R-based and Python-based
        pipelines. The first development phase has focused on bulk transcriptomic
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      authors:
      - name: Colange M.
      - name: Appe G.
      - name: Meunier L.
      - name: Weill S.
      - name: Johnson W.E.
      - name: Nordor A.
      - name: Behdenna A.
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    note: null
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  - doi: 10.1186/s12859-023-05578-5
    metadata:
      abstract: 'Background: Variability in datasets is not only the product of biological
        processes: they are also the product of technical biases. ComBat and ComBat-Seq
        are among the most widely used tools for correcting those technical biases,
        called batch effects, in, respectively, microarray and RNA-Seq expression
        data. Results: In this technical note, we present a new Python implementation
        of ComBat and ComBat-Seq. While the mathematical framework is strictly the
        same, we show here that our implementations: (i) have similar results in terms
        of batch effects correction; (ii) are as fast or faster than the original
        implementations in R and; (iii) offer new tools for the bioinformatics community
        to participate in its development. pyComBat is implemented in the Python language
        and is distributed under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html)
        license as a module of the inmoose package. Source code is available at https://github.com/epigenelabs/inmoose
        and Python package at https://pypi.org/project/inmoose . Conclusions: We present
        a new Python implementation of state-of-the-art tools ComBat and ComBat-Seq
        for the correction of batch effects in microarray and RNA-Seq data. This new
        implementation, based on the same mathematical frameworks as ComBat and ComBat-Seq,
        offers similar power for batch effect correction, at reduced computational
        cost.'
      authors:
      - name: Behdenna A.
      - name: Colange M.
      - name: Haziza J.
      - name: Gema A.
      - name: Appe G.
      - name: Azencott C.-A.
      - name: Nordor A.
      citationCount: 30
      date: '2023-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: pyComBat, a Python tool for batch effects correction in high-throughput
        molecular data using empirical Bayes methods
    note: null
    pmcid: null
    pmid: null
    type:
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    - Method
    version: null
  - doi: 10.1186/s12859-025-06180-7
    metadata:
      abstract: 'Background: Differential gene expression analysis is a prominent
        technique for the analysis of biomolecular data to identify genetic features
        associated with phenotypes. Limma—for microarray data –, and edgeR and DESeq2—for
        RNA-Seq data–, are the most widely used tools for differential gene expression
        analysis of bulk transcriptomic data. Results: We present the differential
        expression features of InMoose, a Python implementation of R tools limma,
        edgeR, and DESeq2. We experimentally show that InMoose stands as a drop-in
        replacement for those tools, with nearly identical results. This ensures reproducibility
        when interfacing both languages in bioinformatic pipelines. InMoose is an
        open source software released under the GPL3 license, available at www.github.com/epigenelabs/inmoose
        and https://inmoose.readthedocs.io. Conclusions: We present a new Python implementation
        of state-of-the-art tools limma, edgeR, and DESeq2, to perform differential
        gene expression analysis of bulk transcriptomic data. This new implementation
        exhibits results nearly identical to the original tools, improving interoperability
        and reproducibility between Python and R bioinformatics pipelines.'
      authors:
      - name: Colange M.
      - name: Appe G.
      - name: Meunier L.
      - name: Weill S.
      - name: Nordor A.
      - name: Behdenna A.
      citationCount: 0
      date: '2025-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: Differential expression analysis with inmoose, the integrated multi-omic
        open-source environment in Python
    note: null
    pmcid: null
    pmid: null
    type:
    - Benchmarking study
    - Method
    version: null
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  - Library
  topic: []
  validated: 0
  version:
  - 0.8.0
- accessibility: Open access (with restrictions)
  additionDate: '2025-07-10T03:21:50.923918Z'
  biotoolsCURIE: biotools:colopola_dataset_for_colorectal_cancer_detection
  biotoolsID: colopola_dataset_for_colorectal_cancer_detection
  collectionID:
  - Dataset
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit: []
  description: The dataset consists of 572 slices (specimens) with 20,592 images.
    There are 284 cancer samples and 288 normal samples. The lists of samples for
    training (train+val) set and testing set are provided in the Zenodo repository.
    Source code in Github is to train and validate five deep learning models using
    ColoPola dataset.
  documentation: []
  download:
  - note: Python language
    type: Source code
    url: https://github.com/haile493/Colorectal-cancer-detection-using-ColoPola-dataset
    version: '1.0'
  - note: Dataset for running source code
    type: Test data
    url: https://zenodo.org/records/10554304
    version: '1.2'
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/haile493/Colorectal-cancer-detection-using-ColoPola-dataset
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-07-10T03:33:52.616431Z'
  license: CC0-1.0
  link:
  - note: Source code
    type:
    - Repository
    url: https://github.com/haile493/Colorectal-cancer-detection-using-ColoPola-dataset
  - note: null
    type:
    - Issue tracker
    url: https://github.com/haile493/Colorectal-cancer-detection-using-ColoPola-dataset/issues
  maturity: Mature
  name: ColoPola dataset for colorectal cancer detection
  operatingSystem:
  - Windows
  otherID: []
  owner: haile
  publication: []
  relation: []
  toolType:
  - Script
  topic:
  - term: Computer science
    uri: http://edamontology.org/topic_3316
  validated: 0
  version:
  - '1.0'
- 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: '2025-07-07T11:17:37.638447Z'
  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: 2004
      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: 171
      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: 81
      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
  - biotoolsID: libsbml
    type: uses
  toolType:
  - Command-line tool
  - Library
  - Desktop application
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2019-11-14T18:08:10Z'
  biotoolsCURIE: biotools:purge_dups
  biotoolsID: purge_dups
  collectionID:
  - ONTeater
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Dengfeng Guan
    note: null
    orcidid: https://orcid.org/0000-0002-6376-3940
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://www.chatlink.com.cn
  description: 'Identifying and removing haplotypic duplication in primary genome
    assemblies | haplotypic duplication identification tool | scripts/pd_config.py:
    script to generate a configuration file used by run_purge_dups.py | purge haplotigs
    and overlaps in an assembly based on read depth | Given a primary assembly pri_asm
    and an alternative assembly hap_asm (optional, if you have one), follow the steps
    shown below to build your own purge_dups pipeline, steps with same number can
    be run simultaneously. Among all the steps, although step 4 is optional, we highly
    recommend our users to do so, because assemblers may produce overrepresented seqeuences.
    In such a case, The final step 4 can be applied to remove those seqeuences'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - 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: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: Read binning
      uri: http://edamontology.org/operation_3798
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    output:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://github.com/dfguan/purge_dups
  homepage_status: 0
  language:
  - Python
  - C
  lastUpdate: '2025-06-30T15:34:51.626796Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/dfguan/purge_dups
  - note: null
    type:
    - Issue tracker
    url: https://github.com/dfguan/purge_dups/issues
  maturity: null
  name: purge_dups
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1101/729962
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 1
  version:
  - v.1.2.6
- accessibility: Open access
  additionDate: '2024-03-18T14:51:49.667412Z'
  biotoolsCURIE: biotools:compleasm
  biotoolsID: compleasm
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: neng@ds.dfci.harvard.edu
    fundrefid: null
    gridid: null
    name: Neng Huang
    note: null
    orcidid: https://orcid.org/0000-0001-7187-0749
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: 'Compleasm: a faster and more accurate reimplementation of BUSCO.

    It provides measures for quantitative assessment of genome assembly, gene set,
    and transcriptome completeness based on evolutionarily informed expectations of
    gene content from near-universal single-copy orthologs.'
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/huangnengCSU/compleasm/blob/0.2.6/README.md
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: compleasm run -l "$lineage" -a assembly.fa -o output_prefix
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA-like
        uri: http://edamontology.org/format_2546
    note: Runs compleasm using the BUSCO set corresponding to the lineage given.
    operation:
    - term: Sequence assembly validation
      uri: http://edamontology.org/operation_3180
    output:
    - data:
        term: Sequence report
        uri: http://edamontology.org/data_2955
      format: []
  homepage: https://github.com/huangnengCSU/compleasm
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-30T15:30:41.266812Z'
  license: Apache-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/huangnengCSU/compleasm
  - note: null
    type:
    - Issue tracker
    url: https://github.com/huangnengCSU/compleasm/issues
  - note: List of accepted lineages (taxonomic groups with curated BUSCO sets)
    type:
    - Other
    url: https://busco.ezlab.org/list_of_lineages.html
  maturity: Mature
  name: Compleasm
  operatingSystem: []
  otherID: []
  owner: rlibouban
  publication:
  - doi: 10.1093/bioinformatics/btad595
    metadata:
      abstract: 'Motivation: Evaluating the gene completeness is critical to measuring
        the quality of a genome assembly. An incomplete assembly can lead to errors
        in gene predictions, annotation, and other downstream analyses. Benchmarking
        Universal Single-Copy Orthologs (BUSCO) is a widely used tool for assessing
        the completeness of genome assembly by testing the presence of a set of single-copy
        orthologs conserved across a wide range of taxa. However, BUSCO is slow particularly
        for large genome assemblies. It is cumbersome to apply BUSCO to a large number
        of assemblies. Results: Here, we present compleasm, an efficient tool for
        assessing the completeness of genome assemblies. Compleasm utilizes the miniprot
        protein-to-genome aligner and the conserved orthologous genes from BUSCO.
        It is 14 times faster than BUSCO for human assemblies and reports a more accurate
        completeness of 99.6% than BUSCO''s 95.7%, which is in close agreement with
        the annotation completeness of 99.5% for T2T-CHM13.'
      authors:
      - name: Huang N.
      - name: Li H.
      citationCount: 95
      date: '2023-10-01T00:00:00Z'
      journal: Bioinformatics
      title: 'compleasm: a faster and more accurate reimplementation of BUSCO'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: busco
    type: isNewVersionOf
  toolType:
  - Command-line tool
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - v.0.2.5
- 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: Visualise systems biology diagrams online, on a standalone web server
    operation:
    - term: Pathway visualisation
      uri: http://edamontology.org/operation_3926
    output: []
  - cmd: null
    input: []
    note: Visualise omics data from multiple datasets on top of the diagrams
    operation:
    - term: Expression data visualisation
      uri: http://edamontology.org/operation_0571
    output: []
  - cmd: null
    input: []
    note: 'Convert between main systems biology layout formats: CellDeslgners SBML,
      SBML layout+render, SBGN-ML, GPML'
    operation:
    - term: Conversion
      uri: http://edamontology.org/operation_3434
    output: []
  - cmd: null
    input: []
    note: Use MINERVA API to access systems biology formats for modelling
    operation:
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output: []
  homepage: https://minerva.uni.lu
  homepage_status: 0
  language: []
  lastUpdate: '2025-06-26T13:15:59.673112Z'
  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: 65
      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: 25
      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: 17
      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: 39
      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
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  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 (with restrictions)
  additionDate: '2021-07-06T20:27:27Z'
  biotoolsCURIE: biotools:nanoplot
  biotoolsID: nanoplot
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Wouter De Coster
    note: null
    orcidid: https://orcid.org/0000-0002-5248-8197
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://gigabaseorgigabyte.wordpress.com/
  description: NanoPlot is a tool with various visualizations of sequencing data in
    bam, cram, fastq, fasta or platform-specific TSV summaries, mainly intended for
    long-read sequencing from Oxford Nanopore Technologies and Pacific Biosciences
  documentation:
  - note: null
    type:
    - Command-line options
    url: https://github.com/wdecoster/NanoPlot
  download:
  - note: null
    type: Command-line specification
    url: https://anaconda.org/bioconda/nanoplot
    version: null
  - note: null
    type: Command-line specification
    url: https://pypi.org/project/NanoPlot/
    version: null
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format:
      - term: FASTA-like
        uri: http://edamontology.org/format_2546
      - term: nucleotide
        uri: http://edamontology.org/format_1207
    note: null
    operation:
    - term: Scatter plot plotting
      uri: http://edamontology.org/operation_2940
    - term: Box-Whisker plot plotting
      uri: http://edamontology.org/operation_2943
    output: []
  homepage: https://github.com/wdecoster/NanoPlot
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-06-18T12:31:48.762608Z'
  license: GPL-3.0
  link:
  - note: Issue tracker and most up to date software version
    type:
    - Repository
    url: https://github.com/wdecoster/NanoPlot
  - note: Web service with more limited options compared to the command line tool
    type:
    - Service
    url: http://nanoplot.bioinf.be/
  maturity: Mature
  name: NanoPlot
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: wdecoster
  publication:
  - doi: 10.1093/bioinformatics/bty149
    metadata:
      abstract: 'Summary: Here we describe NanoPack, a set of tools developed for
        visualization and processing of long-read sequencing data from Oxford Nanopore
        Technologies and Pacific Biosciences. Availability and implementation: The
        NanoPack tools are written in Python3 and released under the GNU GPL3.0 License.
        The source code can be found at https://github.com/wdecoster/nanopack, together
        with links to separate scripts and their documentation. The scripts are compatible
        with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available
        as a graphical user interface, a web service at http://nanoplot.bioinf.be
        and command line tools.'
      authors:
      - name: De Coster W.
      - name: D'Hert S.
      - name: Schultz D.T.
      - name: Cruts M.
      - name: Van Broeckhoven C.
      citationCount: 1840
      date: '2018-08-01T00:00:00Z'
      journal: Bioinformatics
      title: 'NanoPack: Visualizing and processing long-read sequencing data'
    note: null
    pmcid: PMC6061794
    pmid: '29547981'
    type:
    - Method
    version: null
  relation:
  - biotoolsID: nanopack
    type: includedIn
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version:
  - v.1.42.0
- accessibility: Open access
  additionDate: '2019-09-13T12:51:16Z'
  biotoolsCURIE: biotools:kraken2
  biotoolsID: kraken2
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Derrick E. Wood
    note: null
    orcidid: http://orcid.org/0000-0002-7429-1854
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jennifer Lu
    note: null
    orcidid: http://orcid.org/0000-0001-9167-2002
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: langmea@cs.jhu.edu
    fundrefid: null
    gridid: null
    name: Ben Langmead
    note: null
    orcidid: http://orcid.org/0000-0003-2437-1976
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'Kraken 2 is the newest version of Kraken, a taxonomic classification
    system using exact k-mer matches to achieve high accuracy and fast classification
    speeds. This classifier matches each k-mer within a query sequence to the lowest
    common ancestor (LCA) of all genomes containing the given k-mer. The k-mer assignments
    inform the classification algorithm.

    Any assumption that Kraken’s raw read assignments can be directly translated into
    species or strain-level abundance estimates is flawed. Bracken (Bayesian Reestimation
    of Abundance after Classification with KrakEN), estimates species abundances in
    metagenomics samples by probabilistically re-distributing reads in the taxonomic
    tree. (Lu, Jennifer et al. “Bracken: estimating species abundance in metagenomics
    data.”)'
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/DerrickWood/kraken2/wiki/Manual
  - note: Links to multiple Kraken 2 and bracken databases and indexes
    type:
    - User manual
    url: https://benlangmead.github.io/aws-indexes/k2
  download:
  - note: null
    type: Source code
    url: https://github.com/DerrickWood/kraken2/archive/v2.0.8-beta.tar.gz
    version: 2.0.8-beta
  editPermission:
    authors:
    - vashokan
    - Keiler_Collier
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: '`kraken2 --db <kraken2_database> <input.fastq>`'
    input:
    - data:
        term: DNA sequence
        uri: http://edamontology.org/data_3494
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output:
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: https://ccb.jhu.edu/software/kraken2/
  homepage_status: 0
  language:
  - C++
  - Perl
  lastUpdate: '2025-06-18T12:24:26.657325Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/DerrickWood/kraken2
  - note: null
    type:
    - Issue tracker
    url: https://github.com/DerrickWood/kraken2/issues
  maturity: null
  name: kraken2
  operatingSystem: []
  otherID: []
  owner: anand-anshu
  publication:
  - doi: 10.1101/762302
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation:
  - biotoolsID: kraken
    type: isNewVersionOf
  - biotoolsID: bracken
    type: usedBy
  toolType:
  - Command-line tool
  topic:
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Microbiology
    uri: http://edamontology.org/topic_3301
  validated: 1
  version:
  - 2.0.8-beta
- accessibility: Open access
  additionDate: '2021-05-26T21:13:32Z'
  biotoolsCURIE: biotools:nextdenovo
  biotoolsID: nextdenovo
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: support@nextomics.org
    fundrefid: null
    gridid: null
    name: Nextomics
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Primary contact
    url: null
  description: NextDenovo is a string graph-based de novo assembler for long reads
    (CLR, HiFi and ONT). It uses a "correct-then-assemble" strategy similar to canu
    (no correction step for PacBio Hifi reads), but requires significantly less computing
    resources and storages.
  documentation:
  - note: null
    type:
    - User manual
    url: https://nextdenovo.readthedocs.io/en/latest/
  download:
  - note: null
    type: Source code
    url: https://github.com/Nextomics/NextDenovo/releases/tag/2.5.2
    version: null
  editPermission:
    authors:
    - jw
    - ELIXIR-CZ
    - Keiler_Collier
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    output:
    - data:
        term: Sequence assembly
        uri: http://edamontology.org/data_0925
      format:
      - term: Sequence assembly format (text)
        uri: http://edamontology.org/format_2561
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://github.com/Nextomics/NextDenovo
  homepage_status: 0
  language:
  - Python
  - C
  lastUpdate: '2025-06-18T12:20:23.607241Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/Nextomics/NextDenovo/issues
  maturity: Mature
  name: NextDenovo
  operatingSystem: []
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1101/2023.03.09.531669.
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 0
  version:
  - v.2.5.2
- accessibility: Open access
  additionDate: '2021-05-27T09:04:45Z'
  biotoolsCURIE: biotools:quickmerge
  biotoolsID: quickmerge
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Mahul Chakraborty
    note: null
    orcidid: https://orcid.org/0000-0003-2414-9187
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://mahulchakraborty.wordpress.com/
  description: Quickmerge is a program that uses complementary information from genomes
    assembled with long reads in order to improve contiguity, and works with assemblies
    derived from both Pacific Biosciences or Oxford Nanopore. Quickmerge will even
    work with hybrid assemblies made by combining long reads and Illumina short reads.
  documentation: []
  download: []
  editPermission:
    authors:
    - ELIXIR-CZ
    - Keiler_Collier
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: merge_wrapper.py -pre output_prefix assembly_1.fa assembly_2.fa
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: 'Runs whole merge process on an input assembly.

      Assembly 2 will be used to fill gaps in assembly 1.'
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    output:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://github.com/mahulchak/quickmerge
  homepage_status: 0
  language:
  - C++
  - C
  lastUpdate: '2025-06-18T12:17:20.983962Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/mahulchak/quickmerge/issues
  - note: null
    type:
    - Repository
    url: https://github.com/mahulchak/quickmerge
  maturity: null
  name: quickmerge
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1534/g3.118.200162
    metadata:
      abstract: Accurate and comprehensive characterization of genetic variation is
        essential for deciphering the genetic basis of diseases and other phenotypes.
        A vast amount of genetic variation stems from largescale sequence changes
        arising from the duplication, deletion, inversion, and translocation of sequences.
        In the past 10 years, high-throughput short reads have greatly expanded our
        ability to assay sequence variation due to single nucleotide polymorphisms.
        However, a recent de novo assembly of a second Drosophila melanogaster reference
        genome has revealed that short read genotyping methods miss hundreds of structural
        variants, including those affecting phenotypes. While genomes assembled using
        highcoverage long reads can achieve high levels of contiguity and completeness,
        concerns about cost, errors, and low yield have limited widespread adoption
        of such sequencing approaches. Here we resequenced the reference strain of
        D. melanogaster (ISO1) on a single Oxford Nanopore MinION flow cell run for
        24 hr. Using only reads longer than 1 kb or with at least 30x coverage, we
        assembled a highly contiguous de novo genome. The addition of inexpensive
        paired reads and subsequent scaffolding using an optical map technology achieved
        an assembly with completeness and contiguity comparable to the D. melanogaster
        reference assembly. Comparison of our assembly to the reference assembly of
        ISO1 uncovered a number of structural variants (SVs), including novel LTR
        transposable element insertions and duplications affecting genes with developmental,
        behavioral, and metabolic functions. Collectively, these SVs provide a snapshot
        of the dynamics of genome evolution. Furthermore, our assembly and comparison
        to the D. melanogaster reference genome demonstrates that high-quality de
        novo assembly of reference genomes and comprehensive variant discovery using
        such assemblies are now possible by a single lab for under $1,000 (USD).
      authors:
      - name: Solares E.A.
      - name: Chakraborty M.
      - name: Miller D.E.
      - name: Kalsow S.
      - name: Hall K.
      - name: Perera A.G.
      - name: Emerson J.J.
      - name: Scott Hawley R.
      citationCount: 70
      date: '2018-10-01T00:00:00Z'
      journal: 'G3: Genes, Genomes, Genetics'
      title: Rapid low-cost assembly of the drosophila melanogaster reference genome
        using low-coverage, long-read sequencing
    note: null
    pmcid: PMC6169397
    pmid: '30018084'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Structural variation
    uri: http://edamontology.org/topic_3175
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  validated: 0
  version:
  - v.0.3
- accessibility: Open access
  additionDate: '2017-01-13T13:16:01Z'
  biotoolsCURIE: biotools:quast
  biotoolsID: quast
  collectionID:
  - ONTeater
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: quast.support@cab.spbu.ru
    fundrefid: null
    gridid: null
    name: QUAST Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: "QUAST stands for QUality ASsessment Tool.  \nIt evaluates a quality\
    \ of genome assemblies by computing various metrics and providing nice reports."
  documentation:
  - note: null
    type:
    - General
    url: http://quast.bioinf.spbau.ru/
  download: []
  editPermission:
    authors:
    - ELIXIR-CZ
    - Keiler_Collier
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: quast -ek assembly.fa --out output_prefix
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: '# Running quast on a eukaryotic genome'
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Sequence assembly validation
      uri: http://edamontology.org/operation_3180
    output: []
  homepage: http://quast.sourceforge.net/quast
  homepage_status: 0
  language:
  - Perl
  - Python
  - C
  lastUpdate: '2025-06-18T11:53:52.861712Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/ablab/quast
  - note: null
    type:
    - Issue tracker
    url: https://github.com/ablab/quast/issues
  maturity: Mature
  name: QUAST
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: seqwiki_import
  publication:
  - doi: 10.1093/bioinformatics/btt086
    metadata:
      abstract: Limitations of genome sequencing techniques have led to dozens of
        assembly algorithms, none of which is perfect. A number of methods for comparing
        assemblers have been developed, but none is yet a recognized benchmark. Further,
        most existing methods for comparing assemblies are only applicable to new
        assemblies of finished genomes; the problem of evaluating assemblies of previously
        unsequenced species has not been adequately considered. Here, we present QUAST
        - a quality assessment tool for evaluating and comparing genome assemblies.
        This tool improves on leading assembly comparison software with new ideas
        and quality metrics. QUAST can evaluate assemblies both with a reference genome,
        as well as without a reference. QUAST produces many reports, summary tables
        and plots to help scientists in their research and in their publications.
        In this study, we used QUAST to compare several genome assemblers on three
        datasets. QUAST tables and plots for all of them are available in the Supplementary
        Material, and interactive versions of these reports are on the QUAST website.
        © 2013 The Author.
      authors:
      - name: Gurevich A.
      - name: Saveliev V.
      - name: Vyahhi N.
      - name: Tesler G.
      citationCount: 6872
      date: '2013-04-15T00:00:00Z'
      journal: Bioinformatics
      title: 'QUAST: Quality assessment tool for genome assemblies'
    note: null
    pmcid: PMC3624806
    pmid: '23422339'
    type: []
    version: null
  relation: []
  toolType:
  - Workflow
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 1
  version:
  - v.5.3.0
- accessibility: Open access
  additionDate: '2019-11-14T18:44:38Z'
  biotoolsCURIE: biotools:Racon
  biotoolsID: Racon
  collectionID:
  - ONTeater
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: cskang@korea.kr
    fundrefid: null
    gridid: null
    name: Chon-Sik Kang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: changsookim@cnu.ac.kr
    fundrefid: null
    gridid: null
    name: Changsoo Kim
    note: null
    orcidid: https://orcid.org/0000-0002-3596-2934
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'Consensus module for raw de novo DNA assembly of long uncorrected
    reads


    Racon is intended as a standalone consensus module to correct raw contigs generated
    by rapid assembly methods which do not include a consensus step. The goal of Racon
    is to generate genomic consensus which is of similar or better quality compared
    to the output generated by assembly methods which employ both error correction
    and consensus steps, while providing a speedup of several times compared to those
    methods. It supports data produced by both Pacific Biosciences and Oxford Nanopore
    Technologies.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: racon -u reads.fa mapped_reads.sam assembly.fa > assembly_racon.fa
    input:
    - data:
        term: Nucleic acid sequence alignment
        uri: http://edamontology.org/data_1383
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: SAM
        uri: http://edamontology.org/format_2573
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: '# The mapping file can be generated with any mapping program - eg, bwa-mem
      or minimap2.

      # The following is an example using minimap2 with ONT data

      minimap2 assembly.fa-ax map-ont reads.fa > mapped_reads.sam'
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: Mapping assembly
      uri: http://edamontology.org/operation_0523
    output:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://github.com/isovic/racon
  homepage_status: 0
  language:
  - C++
  - Python
  lastUpdate: '2025-06-18T11:42:42.378216Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/isovic/racon
  - note: null
    type:
    - Issue tracker
    url: https://github.com/isovic/racon/issues
  maturity: null
  name: Racon
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.3390/plants8080270
    metadata:
      abstract: 'The whole genome sequencing (WGS) has become a crucial tool in understanding
        genome structure and genetic variation. The MinION sequencing of Oxford Nanopore
        Technologies (ONT) is an excellent approach for performing WGS and it has
        advantages in comparison with other Next-Generation Sequencing (NGS): It is
        relatively inexpensive, portable, has simple library preparation, can be monitored
        in real-time, and has no theoretical limits on reading length. Sorghum bicolor
        (L.) Moench is diploid (2n = 2x = 20) with a genome size of about 730 Mb,
        and its genome sequence information is released in the Phytozome database.
        Therefore, sorghum can be used as a good reference. However, plant species
        have complex and large genomes when compared to animals or microorganisms.
        As a result, complete genome sequencing is difficult for plant species. MinION
        sequencing that produces long-reads can be an excellent tool for overcoming
        the weak assembly of short-reads generated from NGS by minimizing the generation
        of gaps or covering the repetitive sequence that appears on the plant genome.
        Here, we conducted the genome sequencing for S. bicolor cv. BTx623 while using
        the MinION platform and obtained 895,678 reads and 17.9 gigabytes (Gb) (ca.
        25× coverage of reference) from long-read sequence data. A total of 6124 contigs
        (covering 45.9%) were generated from Canu, and a total of 2661 contigs (covering
        50%) were generated from Minimap and Miniasm with a Racon through a de novo
        assembly using two different tools and mapped assembled contigs against the
        sorghum reference genome. Our results provide an optimal series of long-read
        sequencing analysis for plant species while using the MinION platform and
        a clue to determine the total sequencing scale for optimal coverage that is
        based on various genome sizes.'
      authors:
      - name: Lee Y.G.
      - name: Choi S.C.
      - name: Kang Y.
      - name: Kim K.M.
      - name: Kang C.-S.
      - name: Kim C.
      citationCount: 13
      date: '2019-08-01T00:00:00Z'
      journal: Plants
      title: 'Constructing a reference genome in a single lab: The possibility to
        use oxford nanopore technology'
    note: null
    pmcid: PMC6724115
    pmid: '31390788'
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2019-11-14T20:08:20Z'
  biotoolsCURIE: biotools:Flye
  biotoolsID: Flye
  collectionID:
  - ONTeater
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: fenderglass@gmail.com
    fundrefid: null
    gridid: null
    name: Mikhail Kolmogorov
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Support
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Yu Lin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jeffrey Yuan
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: 'Flye is a de novo assembler for single molecule sequencing reads,
    such as those produced by PacBio and Oxford Nanopore Technologies. It is designed
    for a wide range of datasets, from small bacterial projects to large mammalian-scale
    assemblies. The package represents a complete pipeline: it takes raw PB / ONT
    reads as input and outputs polished contigs.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Mapping assembly
      uri: http://edamontology.org/operation_0523
    - term: Cross-assembly
      uri: http://edamontology.org/operation_3730
    output:
    - data:
        term: Sequence set (nucleic acid)
        uri: http://edamontology.org/data_1234
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
  homepage: https://github.com/fenderglass/Flye
  homepage_status: 0
  language:
  - C++
  - Python
  - C
  lastUpdate: '2025-06-18T10:20:03.775603Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/fenderglass/Flye/issues
  - note: null
    type:
    - Repository
    url: https://github.com/mikolmogorov/Flye
  maturity: null
  name: Flye
  operatingSystem:
  - Mac
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1099/mgen.0.000294
    metadata:
      abstract: Illumina sequencing allows rapid, cheap and accurate whole genome
        bacterial analyses, but short reads (<300 bp) do not usually enable complete
        genome assembly. Long-read sequencing greatly assists with resolving complex
        bacterial genomes, particularly when combined with short-read Illumina data
        (hybrid assembly). However, it is not clear how different long-read sequencing
        methods affect hybrid assembly accuracy. Relative automation of the assembly
        process is also crucial to facilitating high-throughput complete bacterial
        genome reconstruction, avoiding multiple bespoke filtering and data manipulation
        steps. In this study, we compared hybrid assemblies for 20 bacterial isolates,
        including two reference strains, using Illumina sequencing and long reads
        from either Oxford Nanopore Technologies (ONT) or SMRT Pacific Biosciences
        (PacBio) sequencing platforms. We chose isolates from the family Enterobacteriaceae,
        as these frequently have highly plastic, repetitive genetic structures, and
        complete genome reconstruction for these species is relevant for a precise
        understanding of the epidemiology of antimicrobial resistance. We de novo
        assembled genomes using the hybrid assembler Unicycler and compared different
        read processing strategies, as well as comparing to long-read-only assembly
        with Flye followed by short-read polishing with Pilon. Hybrid assembly with
        either PacBio or ONT reads facilitated high-quality genome reconstruction,
        and was superior to the longread assembly and polishing approach evaluated
        with respect to accuracy and completeness. Combining ONT and Illumina reads
        fully resolved most genomes without additional manual steps, and at a lower
        consumables cost per isolate in our setting. Automated hybrid assembly is
        a powerful tool for complete and accurate bacterial genome assembly.
      authors:
      - name: De Maio N.
      - name: Shaw L.P.
      - name: Hubbard A.
      - name: George S.
      - name: Sanderson N.D.
      - name: Swann J.
      - name: Wick R.
      - name: Oun M.A.
      - name: Stubberfield E.
      - name: Hoosdally S.J.
      - name: Crook D.W.
      - name: Peto T.E.A.
      - name: Sheppard A.E.
      - name: Bailey M.J.
      - name: Read D.S.
      - name: Anjum M.F.
      - name: Sarah Walker A.
      - name: Stoesser N.
      citationCount: 166
      date: '2019-01-01T00:00:00Z'
      journal: Microbial Genomics
      title: Comparison of long-read sequencing technologies in the hybrid assembly
        of complex bacterial genomes
    note: null
    pmcid: PMC6807382
    pmid: '31483244'
    type:
    - Usage
    version: null
  - doi: 10.1038/s41587-019-0072-8
    metadata:
      abstract: Accurate genome assembly is hampered by repetitive regions. Although
        long single molecule sequencing reads are better able to resolve genomic repeats
        than short-read data, most long-read assembly algorithms do not provide the
        repeat characterization necessary for producing optimal assemblies. Here,
        we present Flye, a long-read assembly algorithm that generates arbitrary paths
        in an unknown repeat graph, called disjointigs, and constructs an accurate
        repeat graph from these error-riddled disjointigs. We benchmark Flye against
        five state-of-the-art assemblers and show that it generates better or comparable
        assemblies, while being an order of magnitude faster. Flye nearly doubled
        the contiguity of the human genome assembly (as measured by the NGA50 assembly
        quality metric) compared with existing assemblers.
      authors:
      - name: Kolmogorov M.
      - name: Yuan J.
      - name: Lin Y.
      - name: Pevzner P.A.
      citationCount: 3077
      date: '2019-05-01T00:00:00Z'
      journal: Nature Biotechnology
      title: Assembly of long, error-prone reads using repeat graphs
    note: null
    pmcid: null
    pmid: '30936562'
    type:
    - Primary
    version: null
  - doi: 10.1038/s41592-020-00971-x
    metadata:
      abstract: Long-read sequencing technologies have substantially improved the
        assemblies of many isolate bacterial genomes as compared to fragmented short-read
        assemblies. However, assembling complex metagenomic datasets remains difficult
        even for state-of-the-art long-read assemblers. Here we present metaFlye,
        which addresses important long-read metagenomic assembly challenges, such
        as uneven bacterial composition and intra-species heterogeneity. First, we
        benchmarked metaFlye using simulated and mock bacterial communities and show
        that it consistently produces assemblies with better completeness and contiguity
        than state-of-the-art long-read assemblers. Second, we performed long-read
        sequencing of the sheep microbiome and applied metaFlye to reconstruct 63
        complete or nearly complete bacterial genomes within single contigs. Finally,
        we show that long-read assembly of human microbiomes enables the discovery
        of full-length biosynthetic gene clusters that encode biomedically important
        natural products.
      authors:
      - name: Kolmogorov M.
      - name: Bickhart D.M.
      - name: Behsaz B.
      - name: Gurevich A.
      - name: Rayko M.
      - name: Shin S.B.
      - name: Kuhn K.
      - name: Yuan J.
      - name: Polevikov E.
      - name: Smith T.P.L.
      - name: Pevzner P.A.
      citationCount: 511
      date: '2020-11-01T00:00:00Z'
      journal: Nature Methods
      title: 'metaFlye: scalable long-read metagenome assembly using repeat graphs'
    note: null
    pmcid: PMC10699202
    pmid: '33020656'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Workflow
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 1
  version:
  - 2.9.6
- accessibility: Open access
  additionDate: '2020-06-18T05:28:02Z'
  biotoolsCURIE: biotools:biosimulations
  biotoolsID: biosimulations
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: karr@mssm.edu
    fundrefid: null
    gridid: null
    name: Jonathan Karr
    note: null
    orcidid: https://orcid.org/0000-0002-2605-5080
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://www.karrlab.org
  - email: null
    fundrefid: null
    gridid: null
    name: Bilal Shaikh
    note: null
    orcidid: https://orcid.org/0000-0001-5801-5510
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  description: BioSimulations is a web application for sharing and re-using biomodels,
    simulations, and visualizations of simulations results. BioSimulations supports
    a wide range of modeling frameworks (e.g., kinetic, constraint-based, and logical
    modeling), model formats (e.g., BNGL, CellML, SBML), and simulation tools (e.g.,
    COPASI, libRoadRunner/tellurium, NFSim, VCell). BioSimulations aims to help researchers
    discover published models that might be useful for their research and quickly
    try them via a simple web-based interface.
  documentation:
  - note: null
    type:
    - General
    url: https://docs.biosimulations.org
  - note: null
    type:
    - API documentation
    url: https://api.biosimulations.org/
  - note: null
    type:
    - Code of conduct
    url: https://docs.biosimulations.org/developers/conduct/
  - note: null
    type:
    - Terms of use
    url: https://docs.biosimulations.org/about/terms/
  download: []
  editPermission:
    authors:
    - bilalshaikh42
    - Bioinfo
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mathematical model
        uri: http://edamontology.org/data_0950
      format:
      - term: SBML
        uri: http://edamontology.org/format_2585
      - term: CellML
        uri: http://edamontology.org/format_3240
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    output:
    - data:
        term: Trajectory data
        uri: http://edamontology.org/data_3870
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
      - term: SVG
        uri: http://edamontology.org/format_3604
  homepage: https://www.biosimulations.org/
  homepage_status: 0
  language:
  - JavaScript
  - Python
  lastUpdate: '2025-06-17T16:32:28.548746Z'
  license: MIT
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/reproducible-biomedical-modeling/Biosimulations/issues
  - note: null
    type:
    - Repository
    url: https://github.com/reproducible-biomedical-modeling/Biosimulations
  - note: null
    type:
    - Service
    url: https://api.biosimulations.org/
  maturity: Emerging
  name: BioSimulations
  operatingSystem: []
  otherID:
  - type: rrid
    value: RRID:SCR_018733
    version: null
  owner: Bioinfo
  publication: []
  relation:
  - biotoolsID: runbiosimulations
    type: uses
  - biotoolsID: biosimulators
    type: uses
  - biotoolsID: biomodels
    type: uses
  - biotoolsID: sbml
    type: uses
  - biotoolsID: bionetgen
    type: uses
  - biotoolsID: cobrapy
    type: uses
  - biotoolsID: copasi
    type: uses
  - biotoolsID: libroadrunner
    type: uses
  toolType:
  - Command-line tool
  - Web API
  - Web application
  - Web service
  topic:
  - term: Systems biology
    uri: http://edamontology.org/topic_2259
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  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: Bracken is a companion program to Kraken 1, KrakenUniq, or Kraken 2
    While Kraken classifies reads to multiple levels in the taxonomic tree, Bracken
    allows estimation of abundance at a single level using those classifications (e.g.
    Bracken can estimate abundance of species within a 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: '2025-06-11T12:25:29.118910Z'
  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: 1084
      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:
  - biotoolsID: kraken2
    type: uses
  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: null
  additionDate: '2023-08-30T09:06:30.320364Z'
  biotoolsCURIE: biotools:pym2aia
  biotoolsID: pym2aia
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Jonas Cordes
    note: null
    orcidid: https://orcid.org/0000-0003-3148-4282
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: pyM²aia is a Python package for memory-efficient access and processing
    of mass spectrometry image data. The I/O functionality is derived from the interactive
    desktop application M²aia. Special features are the batch generator utilities
    for deep learning applications.
  documentation:
  - note: null
    type:
    - General
    url: https://data.jtfc.de/pym2aia/sphinx-build/html/index.html
  download:
  - note: null
    type: Source code
    url: https://github.com/m2aia/pym2aia
    version: null
  editPermission:
    authors: []
    type: public
  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
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    note: null
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: imzML metadata file
        uri: http://edamontology.org/format_3682
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
  homepage: https://m2aia.de/pym2aia.html
  homepage_status: 0
  language:
  - Python
  - C++
  lastUpdate: '2025-06-05T12:19:20.571346Z'
  license: BSD-3-Clause
  link:
  - note: null
    type:
    - Issue tracker
    - Repository
    url: https://github.com/m2aia/pym2aia
  maturity: null
  name: pyM2aia
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: jtfc
  publication:
  - doi: 10.1093/gigascience/giab049
    metadata:
      abstract: 'Background: Mass spectrometry imaging (MSI) is a label-free analysis
        method for resolving bio-molecules or pharmaceuticals in the spatial domain.
        It offers unique perspectives for the examination of entire organs or other
        tissue specimens. Owing to increasing capabilities of modern MSI devices,
        the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting
        in hundreds of gigabytes of data. To fully leverage such MSI acquisitions,
        interactive tools for 3D image reconstruction, visualization, and analysis
        are required, which preferably should be open-source to allow scientists to
        develop custom extensions. Findings: We introduce M2aia (MSI applications
        for interactive analysis in MITK), a software tool providing interactive and
        memory-efficient data access and signal processing of multiple large MSI datasets
        stored in imzML format. M2aia extends MITK, a popular open-source tool in
        medical image processing. Besides the steps of a typical signal processing
        workflow, M2aia offers fast visual interaction, image segmentation, deformable
        3D image reconstruction, and multi-modal registration. A unique feature is
        that fused data with individual mass axes can be visualized in a shared coordinate
        system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse
        kidney dataset and 3D reconstruction and multi-modal image registration of
        a lipid and peptide dataset of a mouse brain, which we make publicly available.
        Conclusions: To our knowledge, M2aia is the first extensible open-source application
        that enables a fast, user-friendly, and interactive exploration of large datasets.
        M2aia is applicable to a wide range of MSI analysis tasks.'
      authors:
      - name: Cordes J.
      - name: Enzlein T.
      - name: Marsching C.
      - name: Hinze M.
      - name: Engelhardt S.
      - name: Hopf C.
      - name: Wolf I.
      citationCount: 17
      date: '2021-07-01T00:00:00Z'
      journal: GigaScience
      title: M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal
        mass spectrometry imaging data
    note: 'Cordes J; Enzlein T; Marsching C; Hinze M; Engelhardt S; Hopf C; Wolf I
      (July, 2021): M²aia - Interactive, fast and memory efficient analysis of 2D
      and 3D multi-modal mass spectrometry imaging data https://doi.org/10.1093/gigascience/giab049'
    pmcid: PMC8290197
    pmid: '34282451'
    type: []
    version: null
  relation:
  - biotoolsID: m2aia
    type: uses
  toolType:
  - Library
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version: []
- 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
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  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
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    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
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    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
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
    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
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: ibd
        uri: http://edamontology.org/format_3839
  homepage: https://cran.r-project.org/package=MALDIquant
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-06-05T11:28:57.497366Z'
  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: 499
      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: Open access
  additionDate: '2017-01-13T13:16:12Z'
  biotoolsCURIE: biotools:samtools
  biotoolsID: samtools
  collectionID:
  - Rare Disease
  - Animal and Crop Genomics
  - SAMtools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: rd@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Richard Durbin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    - Primary contact
    url: https://www.sanger.ac.uk/
  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Support
    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: SAMtools and BCFtools are widely used programs for processing and analysing
    high-throughput sequencing data. They include tools for file format conversion
    and manipulation, sorting, querying, statistics, variant calling, and effect analysis
    amongst other methods.
  documentation:
  - note: HowTos for samtools
    type:
    - Other
    url: http://www.htslib.org/doc/#howtos
  - note: null
    type:
    - User manual
    url: http://www.htslib.org/doc/#manual-pages
  - note: null
    type:
    - Installation instructions
    url: http://www.htslib.org/download/
  download:
  - note: null
    type: Downloads page
    url: http://www.htslib.org/download/
    version: null
  editPermission:
    authors:
    - ELIXIR-EE
    - animalandcropgenomics
    - alice
    - awhitwham
    - sergitobara
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: CRAM
        uri: http://edamontology.org/format_3462
    note: null
    operation:
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Data editing
      uri: http://edamontology.org/operation_3096
    - term: Data parsing
      uri: http://edamontology.org/operation_1812
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Data formatting
      uri: http://edamontology.org/operation_0335
    - term: Data sorting
      uri: http://edamontology.org/operation_3802
    - term: Data filtering
      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: CRAM
        uri: http://edamontology.org/format_3462
  homepage: http://www.htslib.org/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2025-06-03T09:08:10.199446Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/samtools/samtools
  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
  - note: null
    type:
    - Issue tracker
    url: https://github.com/samtools/samtools/issues
  maturity: Mature
  name: SAMtools
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/bioinformatics/btp352
    metadata:
      abstract: 'Summary: The Sequence Alignment/Map (SAM) format is a generic alignment
        format for storing read alignments against reference sequences, supporting
        short and long reads (up to 128 Mbp) produced by different sequencing platforms.
        It is flexible in style, compact in size, efficient in random access and is
        the format in which alignments from the 1000 Genomes Project are released.
        SAM tools implements various utilities for post-processing alignments in the
        SAM format, such as indexing, variant caller and alignment viewer, and thus
        provides universal tools for processing read alignments. © 2009 The Author(s).'
      authors:
      - name: Li H.
      - name: Handsaker B.
      - name: Wysoker A.
      - name: Fennell T.
      - name: Ruan J.
      - name: Homer N.
      - name: Marth G.
      - name: Abecasis G.
      - name: Durbin R.
      citationCount: 42239
      date: '2009-08-01T00:00:00Z'
      journal: Bioinformatics
      title: The Sequence Alignment/Map format and SAMtools
    note: The Sequence Alignment/Map format and SAMtools.
    pmcid: PMC2723002
    pmid: '19505943'
    type:
    - Primary
    version: null
  - doi: 10.1093/gigascience/giab008
    metadata:
      abstract: 'Background: SAMtools and BCFtools are widely used programs for processing
        and analysing high-throughput sequencing data. They include tools for file
        format conversion and manipulation, sorting, querying, statistics, variant
        calling, and effect analysis amongst other methods. Findings: The first version
        appeared online 12 years ago and has been maintained and further developed
        ever since, with many new features and improvements added over the years.
        The SAMtools and BCFtools packages represent a unique collection of tools
        that have been used in numerous other software projects and countless genomic
        pipelines. Conclusion: Both SAMtools and BCFtools are freely available on
        GitHub under the permissive MIT licence, free for both non-commercial and
        commercial use. Both packages have been installed >1 million times via Bioconda.
        The source code and documentation are available from https://www.htslib.org.'
      authors:
      - name: Danecek P.
      - name: Bonfield J.K.
      - name: Liddle J.
      - name: Marshall J.
      - name: Ohan V.
      - name: Pollard M.O.
      - name: Whitwham A.
      - name: Keane T.
      - name: McCarthy S.A.
      - name: Davies R.M.
      citationCount: 6224
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: Twelve years of SAMtools and BCFtools
    note: Twelve years of SAMtools and BCFtools.
    pmcid: PMC7931819
    pmid: '33590861'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btr509
    metadata:
      abstract: 'Motivation: Most existing methods for DNA sequence analysis rely
        on accurate sequences or genotypes. However, in applications of the next-generation
        sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample
        low-coverage sequencing or somatic mutation discovery). These applications
        press for the development of new methods for analyzing sequence data with
        uncertainty. Results: We present a statistical framework for calling SNPs,
        discovering somatic mutations, inferring population genetical parameters and
        performing association tests directly based on sequencing data without explicit
        genotyping or linkage-based imputation. On real data, we demonstrate that
        our method achieves comparable accuracy to alternative methods for estimating
        site allele count, for inferring allele frequency spectrum and for association
        mapping. We also highlight the necessity of using symmetric datasets for finding
        somatic mutations and confirm that for discovering rare events, mismapping
        is frequently the leading source of errors. © The Author 2011. Published by
        Oxford University Press. All rights reserved.'
      authors:
      - name: Li H.
      citationCount: 4405
      date: '2011-11-01T00:00:00Z'
      journal: Bioinformatics
      title: A statistical framework for SNP calling, mutation discovery, association
        mapping and population genetical parameter estimation from sequencing data
    note: null
    pmcid: PMC3198575
    pmid: '21903627'
    type: []
    version: null
  relation:
  - biotoolsID: htslib
    type: uses
  toolType:
  - Suite
  - Command-line tool
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '1.0'
  - '1.1'
  - '1.2'
  - '1.3'
  - 1.3.1
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - 1.16.1
  - '1.17'
  - '1.18'
  - '1.19'
  - 1.19.1
  - 1.19.2
  - '1.20'
  - '1.21'
  - '1.22'
- accessibility: Open access
  additionDate: '2017-08-20T16:07:58Z'
  biotoolsCURIE: biotools:htslib
  biotoolsID: htslib
  collectionID:
  - Animal and Crop Genomics
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    - Primary contact
    url: https://www.sanger.ac.uk/
  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Support
    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: The main purpose of HTSlib is to provide access to genomic information
    files, both alignment data (SAM, BAM, and CRAM formats) and variant data (VCF
    and BCF formats). The library also provides interfaces to access and index genome
    reference data in FASTA format and tab-delimited files with genomic coordinates.
    It is utilized and incorporated into both SAMtools and BCFtools.
  documentation:
  - note: null
    type:
    - User manual
    url: http://www.htslib.org/doc/#manual-pages
  download:
  - note: null
    type: Downloads page
    url: http://www.htslib.org/download/
    version: null
  editPermission:
    authors:
    - animalandcropgenomics
    - smoe
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: CRAM
        uri: http://edamontology.org/format_3462
      - term: FASTA
        uri: http://edamontology.org/format_1929
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
    note: null
    operation:
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    output:
    - data:
        term: Sequence trace
        uri: http://edamontology.org/data_0924
      format:
      - term: SAM
        uri: http://edamontology.org/format_2573
      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: CRAM
        uri: http://edamontology.org/format_3462
      - term: FASTA
        uri: http://edamontology.org/format_1929
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
  homepage: http://www.htslib.org/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2025-06-03T09:00:20.313887Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/samtools/htslib
  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
  - note: null
    type:
    - Issue tracker
    url: https://github.com/samtools/htslib/issues
  maturity: Mature
  name: HTSlib
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/gigascience/giab007
    metadata:
      abstract: 'Background: Since the original publication of the VCF and SAM formats,
        an explosion of software tools have been created to process these data files.
        To facilitate this a library was produced out of the original SAMtools implementation,
        with a focus on performance and robustness. The file formats themselves have
        become international standards under the jurisdiction of the Global Alliance
        for Genomics and Health. Findings: We present a software library for providing
        programmatic access to sequencing alignment and variant formats. It was born
        out of the widely used SAMtools and BCFtools applications. Considerable improvements
        have been made to the original code plus many new features including newer
        access protocols, the addition of the CRAM file format, better indexing and
        iterators, and better use of threading. Conclusion: Since the original Samtools
        release, performance has been considerably improved, with a BAM read-write
        loop running 5 times faster and BAM to SAM conversion 13 times faster (both
        using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen
        HTSlib downloaded >1 million times from GitHub and conda. The C library has
        been used directly by an estimated 900 GitHub projects and has been incorporated
        into Perl, Python, Rust, and R, significantly expanding the number of uses
        via other languages. HTSlib is open source and is freely available from htslib.org
        under MIT/BSD license.'
      authors:
      - name: Bonfield J.K.
      - name: Marshall J.
      - name: Danecek P.
      - name: Li H.
      - name: Ohan V.
      - name: Whitwham A.
      - name: Keane T.
      citationCount: 189
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: 'HTSlib: C library for reading/writing high-Throughput sequencing data'
    note: 'HTSlib: C library for reading/writing high-throughput sequencing data.'
    pmcid: PMC7931820
    pmid: '33594436'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: samtools
    type: usedBy
  - biotoolsID: bcftools
    type: usedBy
  toolType:
  - Library
  topic:
  - term: Data management
    uri: http://edamontology.org/topic_3071
  validated: 1
  version:
  - '1.0'
  - '1.1'
  - '1.2'
  - 1.2.1
  - '1.3'
  - 1.3.1
  - 1.3.2
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - 1.10.1
  - 1.10.2
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - '1.17'
  - '1.18'
  - '1.19'
  - '1.20'
  - '1.21'
  - '1.22'
- accessibility: Open access
  additionDate: '2015-08-24T08:35:55Z'
  biotoolsCURIE: biotools:bcftools
  biotoolsID: bcftools
  collectionID:
  - BCFtools
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.sanger.ac.uk/
  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Support
    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: BCFtools is a set of utilities that manipulate variant calls in the
    Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently
    with both VCFs and BCFs, both uncompressed and BGZF-compressed.
  documentation:
  - note: null
    type:
    - General
    url: http://www.htslib.org/doc/bcftools.html
  - note: A workflow for BCFtools.
    type:
    - Other
    url: http://www.htslib.org/workflow/#mapping_to_variant
  download:
  - note: null
    type: Downloads page
    url: http://www.htslib.org/download/
    version: null
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
    note: Multiple data munging operations.
    operation:
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    output:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
  homepage: http://www.htslib.org/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2025-06-03T08:59:30.745713Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/samtools/bcftools
  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
  - note: null
    type:
    - Issue tracker
    url: https://github.com/samtools/bcftools/issues
  maturity: Mature
  name: BCFtools
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/bioinformatics/btp352
    metadata:
      abstract: 'Summary: The Sequence Alignment/Map (SAM) format is a generic alignment
        format for storing read alignments against reference sequences, supporting
        short and long reads (up to 128 Mbp) produced by different sequencing platforms.
        It is flexible in style, compact in size, efficient in random access and is
        the format in which alignments from the 1000 Genomes Project are released.
        SAM tools implements various utilities for post-processing alignments in the
        SAM format, such as indexing, variant caller and alignment viewer, and thus
        provides universal tools for processing read alignments. © 2009 The Author(s).'
      authors:
      - name: Li H.
      - name: Handsaker B.
      - name: Wysoker A.
      - name: Fennell T.
      - name: Ruan J.
      - name: Homer N.
      - name: Marth G.
      - name: Abecasis G.
      - name: Durbin R.
      citationCount: 42239
      date: '2009-08-01T00:00:00Z'
      journal: Bioinformatics
      title: The Sequence Alignment/Map format and SAMtools
    note: The Sequence Alignment/Map format and SAMtools.
    pmcid: PMC2723002
    pmid: '19505943'
    type:
    - Primary
    version: null
  - doi: 10.1093/gigascience/giab008
    metadata:
      abstract: 'Background: SAMtools and BCFtools are widely used programs for processing
        and analysing high-throughput sequencing data. They include tools for file
        format conversion and manipulation, sorting, querying, statistics, variant
        calling, and effect analysis amongst other methods. Findings: The first version
        appeared online 12 years ago and has been maintained and further developed
        ever since, with many new features and improvements added over the years.
        The SAMtools and BCFtools packages represent a unique collection of tools
        that have been used in numerous other software projects and countless genomic
        pipelines. Conclusion: Both SAMtools and BCFtools are freely available on
        GitHub under the permissive MIT licence, free for both non-commercial and
        commercial use. Both packages have been installed >1 million times via Bioconda.
        The source code and documentation are available from https://www.htslib.org.'
      authors:
      - name: Danecek P.
      - name: Bonfield J.K.
      - name: Liddle J.
      - name: Marshall J.
      - name: Ohan V.
      - name: Pollard M.O.
      - name: Whitwham A.
      - name: Keane T.
      - name: McCarthy S.A.
      - name: Davies R.M.
      citationCount: 6224
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: Twelve years of SAMtools and BCFtools
    note: Twelve years of SAMtools and BCFtools.
    pmcid: PMC7931819
    pmid: '33590861'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: htslib
    type: uses
  toolType:
  - Command-line tool
  - Suite
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: GWAS study
    uri: http://edamontology.org/topic_3517
  - term: Genotyping experiment
    uri: http://edamontology.org/topic_3516
  validated: 1
  version:
  - '1.0'
  - '1.1'
  - '1.2'
  - '1.3'
  - 1.3.1
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - 1.10.1
  - 1.10.2
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - '1.17'
  - '1.18'
  - '1.19'
  - '1.20'
  - '1.21'
  - '1.22'
- 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: 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: 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:
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    - 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: 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:
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    type: private
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  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
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  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:
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    - ilveroluca
    type: group
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  elixirNode:
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  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
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  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
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  elixirNode: []
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  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:
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  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:
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  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: 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
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  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:
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  topic:
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    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'
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  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
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  function:
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    input:
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        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:
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  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
  - 1.1.1
- 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
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  download:
  - note: null
    type: Source code
    url: https://github.com/soedinglab/MMseqs2/releases
    version: null
  editPermission:
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  elixir_badge: 0
  function:
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    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:
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  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:
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  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
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  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
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  additionDate: '2018-07-17T13:51:36Z'
  biotoolsCURIE: biotools:chromswitch
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    fundrefid: null
    gridid: null
    name: Selin Jessa
    note: null
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  description: Flexible method to detect chromatin state switches between samples
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    or chromatin state calls from ChIP-seq data.
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  - note: null
    type:
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  homepage: http://bioconductor.org/packages/release/bioc/html/chromswitch.html
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  license: MIT
  link:
  - note: null
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  - note: null
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  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:
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    version: null
  relation: []
  toolType:
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  topic:
  - term: Epigenetics
    uri: http://edamontology.org/topic_3295
  - term: Functional genomics
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  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  validated: 1
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  additionDate: '2018-06-11T17:34:32Z'
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    name: Rolf I. Skotheim
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  description: Visualization tools for gene fusions.
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  - note: null
    type:
    - User manual
    url: https://bioconductor.org/packages/release/bioc/manuals/chimeraviz/man/chimeraviz.pdf
  download:
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  function:
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  license: Artistic-2.0
  link:
  - note: null
    type:
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    url: https://github.com/stianlagstad/chimeraviz
  - note: null
    type:
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  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
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    type: []
    version: null
  relation: []
  toolType:
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  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
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- accessibility: null
  additionDate: '2018-07-14T20:21:43Z'
  biotoolsCURIE: biotools:rcistarget
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  collectionID:
  - BioConductor
  confidence_flag: null
  cost: null
  credit:
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    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
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  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:
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  license: GPL-3.0
  link:
  - note: null
    type:
    - Mirror
    url: http://scenic.aertslab.org/
  - note: null
    type:
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  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
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    - Primary
    version: null
  relation: []
  toolType:
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  topic:
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    uri: http://edamontology.org/topic_0204
  - term: Transcriptomics
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  validated: 1
  version:
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next: ?page=2
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