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  description: Program which plots schematic diagrams of protein-ligand interactions.
  documentation:
  - note: null
    type:
    - User manual
    url: http://www.ebi.ac.uk/thornton-srv/software/LIGPLOT/manual/
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    type:
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  homepage: http://www.ebi.ac.uk/thornton-srv/software/LIGPLOT/
  homepage_status: 0
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  lastUpdate: '2026-07-21T21:18:36.432397Z'
  license: null
  link:
  - note: null
    type:
    - Software catalogue
    url: http://www.mybiosoftware.com/ligplot-4-5-3-ligplot-1-3-plot-schematic-diagrams-protein-ligand-interactions.html
  maturity: null
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  publication:
  - doi: 10.1093/protein/8.2.127
    metadata:
      abstract: The LIGPLOT program automatically generates schematic 2-D representations
        of protein-ligand complexes from standard Protein Data Bank file input The
        output is a colour, or black-and-white, PostScript file giving a simple and
        informative representation of the intermolecular interactions and their strengths,
        including hydrogen bonds, hydrophobic interactions and atom accessibilities.
        The program is completely general for any ligand and can also be used to show
        other types of interaction in proteins and nucleic acids. It was designed
        to facilitate the rapid inspection of many enzyme complexes, but has found
        many other applications. © 1995 Oxford University Press.
      authors:
      - name: Wallace A.C.
      - name: Laskowski R.A.
      - name: Thornton J.M.
      citationCount: 4892
      date: '1995-02-01T00:00:00Z'
      journal: Protein Engineering, Design and Selection
      title: 'Ligplot: A program to generate schematic diagrams of protein-ligand
        interactions'
    note: null
    pmcid: null
    pmid: '7630882'
    type: []
    version: null
  - doi: 10.1021/ci200227u
    metadata:
      abstract: We describe a graphical system for automatically generating multiple
        2D diagrams of ligand-protein interactions from 3D coordinates. The diagrams
        portray the hydrogen-bond interaction patterns and hydrophobic contacts between
        the ligand(s) and the main-chain or side-chain elements of the protein. The
        system is able to plot, in the same orientation, related sets of ligand-protein
        interactions. This facilitates popular research tasks, such as analyzing a
        series of small molecules binding to the same protein target, a single ligand
        binding to homologous proteins, or the completely general case where both
        protein and ligand change. © 2011 American Chemical Society.
      authors:
      - name: Laskowski R.A.
      - name: Swindells M.B.
      citationCount: 4265
      date: '2011-10-24T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: 'LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery'
    note: null
    pmcid: null
    pmid: '21919503'
    type: []
    version: null
  relation: []
  toolType:
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  topic:
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Protein structure analysis
    uri: http://edamontology.org/topic_2814
  validated: 1
  version: []
- accessibility: null
  additionDate: '2015-01-22T11:13:24Z'
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    note: null
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    typeEntity: Institute
    typeRole:
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    url: null
  - email: tauno.metsalu@ut.ee
    fundrefid: null
    gridid: null
    name: Tauno Metsalu
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: biit.support@ut.ee
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    url: null
  description: Web tool which allows users to upload their own data and easily create
    Principal Component Analysis (PCA) plots and heatmaps. Data can be uploaded as
    a file or by copy-pasteing it to the text box.
  documentation:
  - note: null
    type:
    - General
    url: https://biit.cs.ut.ee/clustvis/
  download:
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    url: https://hub.docker.com/r/taunometsalu/clustvis/
    version: null
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      - term: Matrix format
        uri: http://edamontology.org/format_3033
    - data:
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      format:
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      - term: Matrix format
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    - data:
        term: Gene expression profile
        uri: http://edamontology.org/data_0928
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: Matrix format
        uri: http://edamontology.org/format_3033
    note: null
    operation:
    - term: Gene expression clustering
      uri: http://edamontology.org/operation_0313
    - term: Treemap visualisation
      uri: http://edamontology.org/operation_2942
    - term: Statistical calculation
      uri: http://edamontology.org/operation_2238
    - term: Principal component plotting
      uri: http://edamontology.org/operation_2939
    - term: Heat map generation
      uri: http://edamontology.org/operation_0531
    output:
    - data:
        term: Heat map
        uri: http://edamontology.org/data_1636
      format:
      - term: EPS
        uri: http://edamontology.org/format_3466
      - term: SVG
        uri: http://edamontology.org/format_3604
      - term: PDF
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    - data:
        term: Image
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      format:
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      - term: SVG
        uri: http://edamontology.org/format_3604
      - term: PDF
        uri: http://edamontology.org/format_3508
  homepage: http://biit.cs.ut.ee/clustvis
  homepage_status: 0
  language:
  - R
  lastUpdate: '2026-07-21T21:16:04.483218Z'
  license: GPL-3.0
  link:
  - note: A code repository in github.
    type:
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    url: https://github.com/taunometsalu/ClustVis
  maturity: null
  name: ClustVis
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  - Mac
  otherID:
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    version: null
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  publication:
  - doi: 10.1093/nar/gkv468
    metadata:
      abstract: 'The Principal Component Analysis (PCA) is a widely used method of
        reducing the dimensionality of highdimensional data, often followed by visualizing
        two of the components on the scatterplot. Although widely used, the method
        is lacking an easy-to-use web interface that scientists with little programming
        skills could use to make plots of their own data. The same applies to creating
        heatmaps: it is possible to add conditional formatting for Excel cells to
        show colored heatmaps, but for more advanced features such as clustering and
        experimental annotations, more sophisticated analysis tools have to be used.
        We present a web tool called ClustVis that aims to have an intuitive user
        interface. Users can upload data from a simple delimited text file that can
        be created in a spreadsheet program. It is possible to modify data processing
        methods and the final appearance of the PCA and heatmap plots by using drop-down
        menus, text boxes, sliders etc. Appropriate defaults are given to reduce the
        time needed by the user to specify input parameters. As an output, users can
        download PCA plot and heatmap in one of the preferred file formats. This web
        server is freely available at http://biit.cs.ut.ee/clustvis/.'
      authors:
      - name: Metsalu T.
      - name: Vilo J.
      citationCount: 2504
      date: '2015-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ClustVis: A web tool for visualizing clustering of multivariate data
        using Principal Component Analysis and heatmap'
    note: null
    pmcid: PMC4489295
    pmid: '25969447'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 1
  version:
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- accessibility: null
  additionDate: '2026-06-26T09:04:29.196971Z'
  biotoolsCURIE: biotools:GlycoDash
  biotoolsID: GlycoDash
  collectionID: []
  confidence_flag: null
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  description: GlycoDash is an R Shiny dashboard for processing glycomics data obtained
    from LaCyTools, SweetSuite and Skyline.
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/Center-for-Proteomics-and-Metabolomics/GlycoDash/releases/download/v1.11.5-master/GlycoDash_user_guide_v1.11.5.pdf
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  function:
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    input:
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        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
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        uri: http://edamontology.org/format_3752
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: xlsx
        uri: http://edamontology.org/format_3620
    note: null
    operation:
    - term: Validation
      uri: http://edamontology.org/operation_2428
    - term: Annotation
      uri: http://edamontology.org/operation_0226
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: xlsx
        uri: http://edamontology.org/format_3620
  homepage: https://github.com/Center-for-Proteomics-and-Metabolomics/GlycoDash
  homepage_status: 0
  language:
  - R
  lastUpdate: '2026-07-21T21:16:02.046479Z'
  license: MIT
  link: []
  maturity: Emerging
  name: GlycoDash
  operatingSystem: []
  otherID: []
  owner: ingghrid
  publication:
  - doi: 10.1007/s00216-025-05794-3
    metadata: null
    note: null
    pmcid: PMC11961463
    pmid: '39985669'
    type: []
    version: null
  relation:
  - biotoolsID: skyline
    type: uses
  toolType: []
  topic:
  - term: Biochemistry
    uri: http://edamontology.org/topic_3292
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version:
  - v1.11.5-master
- accessibility: Open access (with restrictions)
  additionDate: '2021-05-05T11:09:42Z'
  biotoolsCURIE: biotools:rrsb-plot
  biotoolsID: rrsb-plot
  collectionID:
  - MATLAB
  - File Exchange
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Ivan Brezani
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://www.mathworks.com/matlabcentral/profile/2241235-ivan-brezani
  description: This tool plots the Rosin-Rammler Diagram (RRSB) and calculates various
    related parameters.
  documentation: []
  download:
  - note: null
    type: Screenshot
    url: https://www.mathworks.com//matlabcentral/mlc-downloads/downloads/submissions/28013/versions/17/screenshot.png
    version: null
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  - cmd: null
    input: []
    note: null
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  homepage: https://www.mathworks.com/matlabcentral/fileexchange/28013-rosin-rammler-diagram-plotting-tool
  homepage_status: 0
  language:
  - MATLAB
  lastUpdate: '2026-07-21T21:16:00.781732Z'
  license: null
  link: []
  maturity: null
  name: Rosin-Rammler Diagram plotting tool
  operatingSystem:
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  - Linux
  - Mac
  otherID: []
  owner: zsmag19
  publication: []
  relation: []
  toolType:
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  topic:
  - term: Biomedical science
    uri: http://edamontology.org/topic_3344
  - term: Biology
    uri: http://edamontology.org/topic_3070
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  validated: 0
  version:
  - 1.16.0.0
- accessibility: null
  additionDate: '2017-08-20T15:58:06Z'
  biotoolsCURIE: biotools:moods
  biotoolsID: moods
  collectionID:
  - Animal and Crop Genomics
  confidence_flag: null
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  credit:
  - email: janne.h.korhonen@aalto.fi
    fundrefid: null
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    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
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    url: null
  description: A suite of algorithms for matching position weight matrices (PWM) against
    DNA sequences. It features advanced matrix matching algorithms implemented in
    C++ that can be used to scan hundreds of matrices against chromosome-sized sequences
    in few seconds. MOODS can also process high-order PWMs with dependencies between
    adjacent positions and sequence variants such as SNPs, insertions and deletions.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/jhkorhonen/MOODS/wiki/Getting-started
  download: []
  editPermission:
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  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence motif recognition
      uri: http://edamontology.org/operation_0239
    output: []
  homepage: https://www.cs.helsinki.fi/group/pssmfind/
  homepage_status: 0
  language:
  - C++
  - Python
  lastUpdate: '2026-07-21T21:15:59.464608Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/jhkorhonen/MOODS
  - note: null
    type:
    - Issue tracker
    url: https://github.com/jhkorhonen/MOODS/issues
  maturity: null
  name: MOODS
  operatingSystem:
  - Linux
  otherID: []
  owner: Bamboo
  publication:
  - doi: 10.1109/tcbb.2009.35
    metadata:
      abstract: Position weight matrices are an important method for modeling signals
        or motifs in biological sequences, both in DNA and protein contexts. In this
        paper, we present fast algorithms for the problem of finding significant matches
        of such matrices. Our algorithms are of the online type, and they generalize
        classical multipattern matching, filtering, and superalphabet techniques of
        combinatorial string matching to the problem of weight matrix matching. Several
        variants of the algorithms are developed, including multiple matrix extensions
        that perform the search for several matrices in one scan through the sequence
        database. Experimental performance evaluation is provided to compare the new
        techniques against each other as well as against some other online and index-based
        algorithms proposed in the literature. Compared to the brute-force O(mn) approach,
        our solutions can be faster by a factor that is proportional to the matrix
        length m. Our multiple-matrix filtration algorithm had the best performance
        in the experiments. On a current PC, this algorithm finds significant matches
        (p = 0.0001) of the 123 JASPAR matrices in the human genome in about 18 minutes.
        © 2011 IEEE.
      authors:
      - name: Pizzi C.
      - name: Rastas P.
      - name: Ukkonen E.
      citationCount: 24
      date: '2011-01-01T00:00:00Z'
      journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
      title: Finding significant matches of position weight matrices in linear time
    note: null
    pmcid: null
    pmid: '21071798'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btw683
    metadata:
      abstract: 'Motivation: While the position weight matrix (PWM) is the most popular
        model for sequence motifs, there is growing evidence of the usefulness of
        more advanced models such as first-order Markov representations, and such
        models are also becoming available in well-known motif databases. There has
        been lots of research of how to learn these models from training data but
        the problem of predicting putative sites of the learned motifs by matching
        the model against new sequences has been given less attention. Moreover, motif
        site analysis is often concerned about how different variants in the sequence
        affect the sites. So far, though, the corresponding efficient software tools
        for motif matching have been lacking. Results: We develop fast motif matching
        algorithms for the aforementioned tasks. First, we formalize a framework based
        on high-order position weight matrices for generic representation of motif
        models with dinucleotide or general q-mer dependencies, and adapt fast PWM
        matching algorithms to the high-order PWM framework. Second, we show how to
        incorporate different types of sequence variants, such as SNPs and indels,
        and their combined effects into efficient PWM matching workflows. Benchmark
        results show that our algorithms perform well in practice on genome-sized
        sequence sets and are for multiple motif search much faster than the basic
        sliding window algorithm.'
      authors:
      - name: Korhonen J.H.
      - name: Palin K.
      - name: Taipale J.
      - name: Ukkonen E.
      citationCount: 23
      date: '2017-02-15T00:00:00Z'
      journal: Bioinformatics
      title: 'Fast motif matching revisited: High-order PWMs, SNPs and indels'
    note: null
    pmcid: null
    pmid: '28011774'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btp554
    metadata:
      abstract: 'Summary: MOODS (MOtif Occurrence Detection Suite) is a software package
        for matching position weight matrices against DNA sequences. MOODS implements
        state-of-the-art online matching algorithms, achieving considerably faster
        scanning speed than with a simple brute-force search. MOODS is written in
        C++, with bindings for the popular BioPerl and Biopython toolkits. It can
        easily be adapted for different purposes and integrated into existing workflows.
        It can also be used as a C++ library. © The Author(s) 2009. Published by Oxford
        University Press.'
      authors:
      - name: Korhonen J.
      - name: Martinmaki P.
      - name: Pizzi C.
      - name: Rastas P.
      - name: Ukkonen E.
      citationCount: 116
      date: '2009-09-22T00:00:00Z'
      journal: Bioinformatics
      title: 'MOODS: Fast search for position weight matrix matches in DNA sequences'
    note: null
    pmcid: PMC2778336
    pmid: '19773334'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Script
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Sequence composition, complexity and repeats
    uri: http://edamontology.org/topic_0157
  validated: 1
  version: []
- accessibility: null
  additionDate: '2021-05-27T09:17:19Z'
  biotoolsCURIE: biotools:supernova
  biotoolsID: supernova
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: david.jaffe@10xgenomics.com
    fundrefid: null
    gridid: null
    name: David B. Jaffe
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: support@10xgenomics.com
    fundrefid: null
    gridid: null
    name: 10x Genomics
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole: []
    url: https://10xgenomics.com
  description: Supernova is a software package for de novo assembly from Chromium
    Linked-Reads that are made from a single whole-genome library from an individual
    DNA source. A key feature of Supernova is that it creates diploid assemblies,
    thus separately representing maternal and paternal chromosomes over very long
    distances. Almost all other methods instead merge homologous chromosomes into
    single incorrect 'consensus' sequences. Supernova is the only practical method
    for creating diploid assemblies of large genomes.
  documentation:
  - note: null
    type:
    - Installation instructions
    url: https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/installation
  - note: null
    type:
    - Release notes
    url: https://support.10xgenomics.com/de-novo-assembly/software/release-notes/2-1
  download:
  - note: null
    type: Downloads page
    url: https://support.10xgenomics.com/de-novo-assembly/software/downloads/latest
    version: null
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Demultiplexing
      uri: http://edamontology.org/operation_3933
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Sequence trimming
      uri: http://edamontology.org/operation_3192
    output: []
  homepage: https://support.10xgenomics.com/de-novo-assembly/software/overview/latest/welcome
  homepage_status: 0
  language: []
  lastUpdate: '2026-07-21T21:15:54.674089Z'
  license: Proprietary
  link: []
  maturity: null
  name: Supernova
  operatingSystem:
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  owner: user.1783928322
  publication:
  - doi: 10.1101/gr.214874.116
    metadata:
      abstract: Determining the genome sequence of an organism is challenging, yet
        fundamental to understanding its biology. Over the past decade, thousands
        of human genomes have been sequenced, contributing deeply to biomedical research.
        In the vast majority of cases, these have been analyzed by aligning sequence
        reads to a single reference genome, biasing the resulting analyses, and in
        general, failing to capture sequences novel to a given genome. Some de novo
        assemblies have been constructed free of reference bias, but nearly all were
        constructed by merging homologous loci into single "consensus" sequences,
        generally absent from nature. These assemblies do not correctly represent
        the diploid biology of an individual. In exactly two cases, true diploid de
        novo assemblies have been made, at great expense. One was generated using
        Sanger sequencing, and one using thousands of clone pools. Here, we demonstrate
        a straightforward and low-cost method for creating true diploid de novo assemblies.
        We make a single library from ~1 ng of high molecular weight DNA, using the
        10x Genomics microfluidic platform to partition the genome. We applied this
        technique to seven human samples, generating low-cost HiSeq X data, then assembled
        these using a new "pushbutton" algorithm, Supernova. Each computation took
        2 d on a single server. Each yielded contigs longer than 100 kb, phase blocks
        longer than 2.5 Mb, and scaffolds longer than 15 Mb. Our method provides a
        scalable capability for determining the actual diploid genome sequence in
        a sample, opening the door to new approaches in genomic biology and medicine.
      authors:
      - name: Weisenfeld N.I.
      - name: Kumar V.
      - name: Shah P.
      - name: Church D.M.
      - name: Jaffe D.B.
      citationCount: 519
      date: '2017-05-01T00:00:00Z'
      journal: Genome Research
      title: Direct determination of diploid genome sequences
    note: null
    pmcid: PMC5411770
    pmid: '28381613'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Suite
  - Workflow
  - Command-line tool
  topic:
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2022-07-26T10:11:09.660345Z'
  biotoolsCURIE: biotools:pangolin
  biotoolsID: pangolin
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: yangili1@uchicago.edu
    fundrefid: null
    gridid: null
    name: Yang I Li
    note: null
    orcidid: https://orcid.org/0000-0002-0736-251X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Tony Zeng
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Pangolin is a deep-learning based method for predicting splice site
    strengths (for details, see Zeng and Li, Genome Biology 2022). It is available
    as a command-line tool that can be run on a VCF or CSV file containing variants
    of interest; Pangolin will predict changes in splice site strength due to each
    variant, and return a file of the same format. Pangolin's models can also be used
    with custom sequences.
  documentation: []
  download: []
  editPermission:
    authors:
    - xiangjun_zhao
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  function:
  - cmd: null
    input:
    - data:
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      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Splice site prediction
      uri: http://edamontology.org/operation_0433
    - term: Sequence merging
      uri: http://edamontology.org/operation_0232
    - term: Alternative splicing prediction
      uri: http://edamontology.org/operation_0264
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    output: []
  homepage: https://github.com/tkzeng/Pangolin
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2026-07-21T21:15:53.848510Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: Pangolin
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1186/s13059-022-02664-4
    metadata:
      abstract: Recent progress in deep learning has greatly improved the prediction
        of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning
        model to predict splice site strength in multiple tissues. Pangolin outperforms
        state-of-the-art methods for predicting RNA splicing on a variety of prediction
        tasks. Pangolin improves prediction of the impact of genetic variants on RNA
        splicing, including common, rare, and lineage-specific genetic variation.
        In addition, Pangolin identifies loss-of-function mutations with high accuracy
        and recall, particularly for mutations that are not missense or nonsense,
        demonstrating remarkable potential for identifying pathogenic variants.
      authors:
      - name: Zeng T.
      - name: Li Y.I.
      citationCount: 52
      date: '2022-12-01T00:00:00Z'
      journal: Genome Biology
      title: Predicting RNA splicing from DNA sequence using Pangolin
    note: null
    pmcid: PMC9022248
    pmid: '35449021'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: RNA splicing
    uri: http://edamontology.org/topic_3320
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Exome sequencing
    uri: http://edamontology.org/topic_3676
  validated: 1
  version: []
- accessibility: null
  additionDate: '2021-03-24T10:36:29Z'
  biotoolsCURIE: biotools:itsonewb
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  collectionID: []
  confidence_flag: null
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  description: ITSoneWB (ITSone WorkBench) is a Galaxy-based bioinformatic environment
    where comprehensive and high-quality reference data are connected with established
    pipelines and new tools in an automated and easy-to-use service targeted at global
    taxonomic analysis of eukaryotic communities based on Internal Transcribed Spacer
    1 variants high-throughput sequencing.
  documentation: []
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  license: null
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  maturity: null
  name: ITSoneWB
  operatingSystem: []
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  owner: bfosso
  publication: []
  relation: []
  toolType: []
  topic:
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  validated: 0
  version: []
- accessibility: null
  additionDate: '2021-01-18T11:19:37Z'
  biotoolsCURIE: biotools:asap-assemble
  biotoolsID: asap-assemble
  collectionID: []
  confidence_flag: tool
  cost: null
  credit:
  - email: puillandre@mnhn.fr
    fundrefid: null
    gridid: null
    name: Nicolas Puillandre
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'ASAP (Assemble Species by Automatic Partitioning) is a method to build
    species partitions from single locus sequence alignments.

    ASAP is the implementation of a hierarchical clustering algorithm that only uses
    pairwise genetic distances, avoiding the computational burden of phylogenetic
    reconstruction. Importantly, ASAP proposes species partitions ranked by a new
    scoring system that uses no biological prior insight of intraspecific diversity.'
  documentation:
  - note: null
    type:
    - General
    url: https://bioinfo.mnhn.fr/abi/public/asap/help_asap.html
  - note: null
    type:
    - FAQ
    url: https://bioinfo.mnhn.fr/abi/public/asap/FAQ_asap.html
  download: []
  editPermission:
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  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Phylogenetic reconstruction
      uri: http://edamontology.org/operation_3478
    - term: DNA barcoding
      uri: http://edamontology.org/operation_3200
    - term: Sequence assembly
      uri: http://edamontology.org/operation_0310
    - term: Splitting
      uri: http://edamontology.org/operation_3359
    output: []
  homepage: https://bioinfo.mnhn.fr/abi/public/asap
  homepage_status: 0
  language: []
  lastUpdate: '2026-07-21T21:15:51.417139Z'
  license: null
  link: []
  maturity: null
  name: ASAP
  operatingSystem: []
  otherID: []
  owner: Kigaard
  publication:
  - doi: 10.1111/1755-0998.13281
    metadata:
      abstract: © 2020 John Wiley & Sons LtdHere, we describe Assemble Species by
        Automatic Partitioning (ASAP), a new method to build species partitions from
        single locus sequence alignments (i.e., barcode data sets). ASAP is efficient
        enough to split data sets as large 104 sequences into putative species in
        several minutes. Although grounded in evolutionary theory, ASAP is the implementation
        of a hierarchical clustering algorithm that only uses pairwise genetic distances,
        avoiding the computational burden of phylogenetic reconstruction. Importantly,
        ASAP proposes species partitions ranked by a new scoring system that uses
        no biological prior insight of intraspecific diversity. ASAP is a stand-alone
        program that can be used either through a graphical web-interface or that
        can be downloaded and compiled for local usage. We have assessed its power
        along with three others programs (ABGD, PTP and GMYC) on 10 real COI barcode
        data sets representing various degrees of challenge (from small and easy cases
        to large and complicated data sets). We also used Monte-Carlo simulations
        of a multispecies coalescent framework to assess the strengths and weaknesses
        of ASAP and the other programs. Through these analyses, we demonstrate that
        ASAP has the potential to become a major tool for taxonomists as it proposes
        rapidly in a full graphical exploratory interface relevant species hypothesis
        as a first step of the integrative taxonomy process.
      authors:
      - name: Puillandre N.
      - name: Brouillet S.
      - name: Achaz G.
      citationCount: 15
      date: '2021-02-01T00:00:00Z'
      journal: Molecular Ecology Resources
      title: 'ASAP: assemble species by automatic partitioning'
    note: null
    pmcid: null
    pmid: '33058550'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Desktop application
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  validated: 0
  version: []
- accessibility: null
  additionDate: '2017-03-30T23:00:28Z'
  biotoolsCURIE: biotools:jgi_genome_portal
  biotoolsID: jgi_genome_portal
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: ivgrigoriev@lbl.gov
    fundrefid: null
    gridid: null
    name: I.V. Grigoriev
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: The JGI Genome Portal provides a unified access point to all JGI genomic
    databases and analytical tools. A user can find all DOE JGI sequencing projects
    and their status, search for and download assemblies and annotations of sequenced
    genomes, and interactively explore those genomes and compare them with other sequenced
    microbes, fungi, plants or metagenomes using specialized systems tailored to each
    particular class of organisms.
  documentation:
  - note: null
    type:
    - General
    url: https://jgi.doe.gov/user-program-info/
  download: []
  editPermission:
    authors:
    - ursula
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: Sequence assembly
      uri: http://edamontology.org/operation_0310
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    output: []
  homepage: http://genome.jgi.doe.gov/
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2026-07-21T21:15:50.005041Z'
  license: null
  link: []
  maturity: null
  name: JGI Genome Portal
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID:
  - type: rrid
    value: RRID:SCR_002383
    version: null
  owner: ELIXIR-EE
  publication:
  - doi: 10.1093/nar/gkr947
    metadata:
      abstract: The Department of Energy (DOE) Joint Genome Institute (JGI) is a national
        user facility with massive-scale DNA sequencing and analysis capabilities
        dedicated to advancing genomics for bioenergy and environmental applications.
        Beyond generating tens of trillions of DNA bases annually, the Institute develops
        and maintains data management systems and specialized analytical capabilities
        to manage and interpret complex genomic data sets, and to enable an expanding
        community of users around the world to analyze these data in different contexts
        over the web. The JGI Genome Portal (http://genome.jgi.doe. gov) provides
        a unified access point to all JGI genomic databases and analytical tools.
        A user can find all DOE JGI sequencing projects and their status, search for
        and download assemblies and annotations of sequenced genomes, and interactively
        explore those genomes and compare them with other sequenced microbes, fungi,
        plants or metagenomes using specialized systems tailored to each particular
        class of organisms. We describe here the general organization of the Genome
        Portal and the most recent addition, MycoCosm (http://jgi.doe.gov/ fungi),
        a new integrated fungal genomics resource. © The Author(s) 2011.
      authors:
      - name: Grigoriev I.V.
      - name: Nordberg H.
      - name: Shabalov I.
      - name: Aerts A.
      - name: Cantor M.
      - name: Goodstein D.
      - name: Kuo A.
      - name: Minovitsky S.
      - name: Nikitin R.
      - name: Ohm R.A.
      - name: Otillar R.
      - name: Poliakov A.
      - name: Ratnere I.
      - name: Riley R.
      - name: Smirnova T.
      - name: Rokhsar D.
      - name: Dubchak I.
      citationCount: 386
      date: '2012-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: The Genome Portal of the Department of Energy Joint Genome Institute
    note: null
    pmcid: PMC3245080
    pmid: '22110030'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  validated: 1
  version: []
- accessibility: null
  additionDate: '2021-01-18T10:54:08Z'
  biotoolsCURIE: biotools:chimerax
  biotoolsID: chimerax
  collectionID: []
  confidence_flag: tool
  cost: null
  credit:
  - email: tef@cgl.ucsf.edu
    fundrefid: null
    gridid: null
    name: Thomas E. Ferrin
    note: null
    orcidid: https://orcid.org/0000-0001-6227-0637
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'Structure visualization for researchers, educators, and developers.


    Tutorials can be viewed in any browser, but those with click-to-execute links
    should be viewed from within ChimeraX for the links to work. To view a web page
    from within ChimeraX, start ChimeraX and use the command open URL (substituting
    in the actual URL), or navigate here using the ChimeraX menu: Help... Tutorials.


    UCSF ChimeraX is the next-generation molecular visualization program from the
    Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF
    Chimera.'
  documentation:
  - note: null
    type:
    - General
    url: https://www.rbvi.ucsf.edu/chimerax/features.html
  - note: null
    type:
    - Training material
    url: https://www.rbvi.ucsf.edu/chimerax/tutorials.html
  download: []
  editPermission:
    authors:
    - sergitobara
    - Puja123
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Molecular surface calculation
      uri: http://edamontology.org/operation_0387
    - term: Side chain modelling
      uri: http://edamontology.org/operation_0480
    - term: Molecular docking
      uri: http://edamontology.org/operation_0478
    output: []
  homepage: https://www.rbvi.ucsf.edu/chimerax
  homepage_status: 0
  language: []
  lastUpdate: '2026-07-21T21:15:49.368925Z'
  license: null
  link:
  - note: null
    type:
    - Other
    url: https://www.rbvi.ucsf.edu/chimerax/gallery.html
  maturity: null
  name: ChimeraX
  operatingSystem: []
  otherID: []
  owner: zsmag19
  publication:
  - doi: 10.1002/pro.3943
    metadata:
      abstract: UCSF ChimeraX is the next-generation interactive visualization program
        from the Resource for Biocomputing, Visualization, and Informatics (RBVI),
        following UCSF Chimera. ChimeraX brings (a) significant performance and graphics
        enhancements; (b) new implementations of Chimera's most highly used tools,
        many with further improvements; (c) several entirely new analysis features;
        (d) support for new areas such as virtual reality, light-sheet microscopy,
        and medical imaging data; (e) major ease-of-use advances, including toolbars
        with icons to perform actions with a single click, basic “undo” capabilities,
        and more logical and consistent commands; and (f) an app store for researchers
        to contribute new tools. ChimeraX includes full user documentation and is
        free for noncommercial use, with downloads available for Windows, Linux, and
        macOS from https://www.rbvi.ucsf.edu/chimerax.
      authors:
      - name: Pettersen E.F.
      - name: Goddard T.D.
      - name: Huang C.C.
      - name: Meng E.C.
      - name: Couch G.S.
      - name: Croll T.I.
      - name: Morris J.H.
      - name: Ferrin T.E.
      citationCount: 4079
      date: '2021-01-01T00:00:00Z'
      journal: Protein Science
      title: 'UCSF ChimeraX: Structure visualization for researchers, educators, and
        developers'
    note: null
    pmcid: PMC7737788
    pmid: '32881101'
    type: []
    version: null
  - doi: 10.1002/pro.3235
    metadata:
      abstract: UCSF ChimeraX is next-generation software for the visualization and
        analysis of molecular structures, density maps, 3D microscopy, and associated
        data. It addresses challenges in the size, scope, and disparate types of data
        attendant with cutting-edge experimental methods, while providing advanced
        options for high-quality rendering (interactive ambient occlusion, reliable
        molecular surface calculations, etc.) and professional approaches to software
        design and distribution. This article highlights some specific advances in
        the areas of visualization and usability, performance, and extensibility.
        ChimeraX is free for noncommercial use and is available from http://www.rbvi.ucsf.edu/chimerax/
        for Windows, Mac, and Linux.
      authors:
      - name: Goddard T.D.
      - name: Huang C.C.
      - name: Meng E.C.
      - name: Pettersen E.F.
      - name: Couch G.S.
      - name: Morris J.H.
      - name: Ferrin T.E.
      citationCount: 2746
      date: '2018-01-01T00:00:00Z'
      journal: Protein Science
      title: 'UCSF ChimeraX: Meeting modern challenges in visualization and analysis'
    note: null
    pmcid: PMC5734306
    pmid: '28710774'
    type: []
    version: null
  relation:
  - biotoolsID: artiax
    type: includes
  toolType: []
  topic:
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: Informatics
    uri: http://edamontology.org/topic_0605
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: RNA immunoprecipitation
    uri: http://edamontology.org/topic_3794
  validated: 0
  version: []
- accessibility: null
  additionDate: '2019-11-14T18:10:12Z'
  biotoolsCURIE: biotools:CyTargetLinker
  biotoolsID: CyTargetLinker
  collectionID: []
  confidence_flag: tool
  cost: null
  credit:
  - email: martina.kutmon@maastrichtuniversity.nl
    fundrefid: null
    gridid: null
    name: Martina Kutmon
    note: null
    orcidid: https://orcid.org/0000-0002-7699-8191
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: A flexible solution for network extension in Cytoscape | The CyTargetLinker
    app extends biological networks with regulatory interactions, for example miRNA-target,
    TF-target or drug-target interactions | CyTargetLinker website and tutorials |
    CyTargetLinker - Extend your biological networks in Cytoscape
  documentation:
  - note: null
    type:
    - Citation instructions
    url: https://cytargetlinker.github.io/pages/citation
  - note: null
    type:
    - General
    url: https://cytargetlinker.github.io/pages/docs
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Pathway or network prediction
      uri: http://edamontology.org/operation_3439
    - term: Pathway or network comparison
      uri: http://edamontology.org/operation_0277
    - term: Gene regulatory network analysis
      uri: http://edamontology.org/operation_1781
    output: []
  homepage: https://cytargetlinker.github.io/
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2026-07-21T21:15:46.757924Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: http://apps.cytoscape.org/apps/cytargetlinker
  - note: null
    type:
    - Issue tracker
    url: https://github.com/CyTargetLinker/cytargetlinker/issues
  maturity: null
  name: CyTargetLinker
  operatingSystem: []
  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.12688/F1000RESEARCH.14613.2
    metadata:
      abstract: © 2019 Kutmon M et al.Here, we present an update of the open-source
        CyTargetLinker app for Cytoscape (http://apps.cytoscape.org/apps/cytargetlinker)
        that introduces new automation features. CyTargetLinker provides a simple
        interface to extend networks with links to relevant data and/or knowledge
        extracted from so-called linksets. The linksets are provided on the CyTargetLinker
        website (https://cytargetlinker.github.io/) or can be custom-made for specific
        use cases. The new automation feature enables users to programmatically execute
        the app’s functionality in Cytoscape (command line tool) and with external
        tools (e.g. R, Jupyter, Python, etc). This allows users to share their analysis
        workflows and therefore increase repeatability and reproducibility. Three
        use cases demonstrate automated workflows, combinations with other Cytoscape
        apps and core Cytoscape functionality. We first extend a protein-protein interaction
        network created with the stringApp, with compound-target interactions and
        disease-gene annotations. In the second use case, we created a workflow to
        load differentially expressed genes from an experimental dataset and extend
        it with gene-pathway associations. Lastly, we chose an example outside the
        biological domain and used CyTargetLinker to create an author-article-journal
        network for the five authors of this manuscript using a two-step extension
        mechanism. With 400 downloads per month in the last year and nearly 20,000
        downloads in total, CyTargetLinker shows the adoption and relevance of the
        app in the field of network biology. In August 2019, the original publication
        was cited in 83 articles demonstrating the applicability in biomedical research.
      authors:
      - name: Kutmon M.
      - name: Ehrhart F.
      - name: Willighagen E.L.
      - name: Evelo C.T.
      - name: Coort S.L.
      citationCount: 11
      date: '2019-01-01T00:00:00Z'
      journal: F1000Research
      title: 'Cytargetlinker app update: A flexible solution for network extension
        in cytoscape'
    note: null
    pmcid: PMC6707396
    pmid: '31489175'
    type: []
    version: null
  - doi: 10.1371/journal.pone.0082160
    metadata:
      abstract: 'Introduction: The high complexity and dynamic nature of the regulation
        of gene expression, protein synthesis, and protein activity pose a challenge
        to fully understand the cellular machinery. By deciphering the role of important
        players, including transcription factors, microRNAs, or small molecules, a
        better understanding of key regulatory processes can be obtained. Various
        databases contain information on the interactions of regulators with their
        targets for different organisms, data recently being extended with the results
        of the ENCODE (Encyclopedia of DNA Elements) project. A systems biology approach
        integrating our understanding on different regulators is essential in interpreting
        the regulation of molecular biological processes. Implementation: We developed
        CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker), a Cytoscape
        app, for integrating regulatory interactions in network analysis. Recently
        we released CyTargetLinker as one of the first apps for Cytoscape 3. It provides
        a user-friendly and flexible interface to extend biological networks with
        regulatory interactions, such as microRNA-target, transcription factor-target
        and/or drug-target. Importantly, CyTargetLinker employs identifier mapping
        to combine various interaction data resources that use different types of
        identifiers. Results: Three case studies demonstrate the strength and broad
        applicability of CyTargetLinker, (i) extending a mouse molecular interaction
        network, containing genes linked to diabetes mellitus, with validated and
        predicted microRNAs, (ii) enriching a molecular interaction network, containing
        DNA repair genes, with ENCODE transcription factor and (iii) building a regulatory
        meta-network in which a biological process is extended with information on
        transcription factor, microRNA and drug regulation. Conclusions: CyTargetLinker
        provides a simple and extensible framework for biologists and bioinformaticians
        to integrate different regulatory interactions into their network analysis
        approaches. Visualization options enable biological interpretation of complex
        regulatory networks in a graphical way. Importantly the incorporation of our
        tool into the Cytoscape framework allows the application of CyTargetLinker
        in combination with a wide variety of other apps for state-ofthe- art network
        analysis. Copyright: © 2013 Kutmon et al.'
      authors:
      - name: Kutmon M.
      - name: Kelder T.
      - name: Mandaviya P.
      - name: Evelo C.T.A.
      - name: Coort S.L.
      citationCount: 83
      date: '2013-12-05T00:00:00Z'
      journal: PLoS ONE
      title: 'Cytargetlinker: A Cytoscape app to integrate regulatory interactions
        in network analysis'
    note: null
    pmcid: PMC3855388
    pmid: '24340000'
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    uri: http://edamontology.org/topic_0602
  - term: Machine learning
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  - term: Gene regulation
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    metadata: null
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  topic:
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  - term: Molecular genetics
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  validated: 0
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- accessibility: Open access
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  description: ArrayAnalysis is a web-based application for transcriptomic data analysis.
    It supports the analysis of both microarray and RNA-seq data. The tool may also
    be installed locally as a desktop app, Docker image, or R package.
  documentation:
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    metadata: null
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  toolType:
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  topic:
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  - term: Data architecture, analysis and design
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  biotoolsCURIE: biotools:pinpath
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  description: PinPath enables flexible visualization of (omics) data onto pathways
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    Data can be displayed on both native pathway layouts and network representations
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    url: https://doi.org/doi:10.18129/B9.bioc.PinPath
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  toolType:
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  topic:
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  - term: Molecular interactions, pathways and networks
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  validated: 0
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  additionDate: '2026-07-20T02:40:16.161114Z'
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  description: Standalone version of dbcan to run CAZyme and CGC annotation.
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    GenBank, EMBL, Swiss-Prot, AB1/ABIF, SCF, Clustal, Stockholm, PHYLIP, NEXUS, MSF,
    PIR and more — see every sequence inside, pick the ones you want, and save them
    as FASTA. The format is detected from the file content, not from the extension,
    so unlabelled or misnamed files still open. Runs entirely in the browser — files
    are never uploaded.
  documentation: []
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    type: Downloads page
    url: https://fishka.bio/download
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    input:
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      - term: AB1
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        uri: http://edamontology.org/format_1929
  homepage: https://fishka.bio/picklet
  homepage_status: 0
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  relation: []
  toolType:
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  topic:
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  description: Browser-based viewer for Sanger sequencing chromatograms in AB1/ABIF
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    quality values and the ABIF directory, and puts two reads side by side for comparison.
    Aligns a read against a reference sequence to highlight mismatches. Exports the
    read as FASTA, FASTQ or ABIF and the chromatogram as a high-resolution image.
    Runs entirely in the browser — files are never uploaded.
  documentation: []
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  description: "Browser-based viewer for GenBank and GenPept files. Renders an interactive\
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  description: Molecular Biology Tools is a free browser-based collection of molecular
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    primer planning, cloning setup, and wet-lab calculations. The site includes tools
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    checks, restriction site analysis, reverse complement generation, ORF and protein
    translation, codon optimization, ligation calculations, molarity calculations,
    dilution calculations, and multi-solute solution recipe preparation. The tools
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  documentation:
  - note: null
    type:
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  language:
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  - Python
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  maturity: Mature
  name: metilene3
  operatingSystem:
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  otherID: []
  owner: johangustafsson
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    metadata: null
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    pmcid: null
    pmid: '42401582'
    type:
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    version: null
  relation: []
  toolType:
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- accessibility: null
  additionDate: '2026-07-17T01:57:03.419070Z'
  biotoolsCURIE: biotools:nf-test
  biotoolsID: nf-test
  collectionID: []
  confidence_flag: null
  cost: null
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  description: Simple test framework for Nextflow pipelines
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  download: []
  editPermission:
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  - Java
  - Groovy
  lastUpdate: '2026-07-17T01:57:03.424277Z'
  license: MIT
  link:
  - note: null
    type:
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    url: https://www.nf-test.com/
  maturity: Mature
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  operatingSystem: []
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  owner: johangustafsson
  publication:
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    metadata: null
    note: null
    pmcid: PMC12616847
    pmid: '41124057'
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    version: null
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  additionDate: '2026-07-17T01:38:47.905724Z'
  biotoolsCURIE: biotools:CebraEM
  biotoolsID: CebraEM
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  confidence_flag: null
  cost: null
  credit: []
  description: CebraEM is a bioinformatics tool for analyzing and processing large-scale
    imaging data, providing a pipeline for segmentation, annotation, and analysis
    with support for both Linux and Windows environments. It includes modules for
    core functionality, annotation, and network analysis, requiring specific dependencies
    and a conda environment for execution.
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    operation:
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      uri: http://edamontology.org/operation_3553
    output: []
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  - Python
  lastUpdate: '2026-07-17T01:38:47.911528Z'
  license: MIT
  link: []
  maturity: Mature
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  - Windows
  otherID: []
  owner: johangustafsson
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    metadata: null
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    pmcid: null
    pmid: null
    type:
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  relation: []
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  topic:
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    uri: http://edamontology.org/topic_0611
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- accessibility: null
  additionDate: '2026-07-17T01:26:44.176393Z'
  biotoolsCURIE: biotools:blue-crab
  biotoolsID: blue-crab
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  confidence_flag: null
  cost: null
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  description: blue-crab is a tool to convert from ONT POD5 format to the community
    maintained SLOW5/BLOW5 format. Lossless nanopore pod5 s/blow5 file conversion.
  documentation:
  - note: null
    type:
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    url: https://github.com/Psy-Fer/blue-crab/blob/main/docs/cli.md
  - note: null
    type:
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    url: https://github.com/Psy-Fer/blue-crab/blob/main/README.md
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  editPermission:
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  function:
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    input:
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        uri: http://edamontology.org/data_2977
      format: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_3434
    output:
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        uri: http://edamontology.org/data_2977
      format: []
  homepage: https://github.com/Psy-Fer/blue-crab
  homepage_status: 0
  language:
  - Shell
  - Python
  lastUpdate: '2026-07-17T01:26:44.178882Z'
  license: MIT
  link: []
  maturity: Mature
  name: blue-crab
  operatingSystem: []
  otherID: []
  owner: johangustafsson
  publication:
  - doi: 10.1038/s41587-021-01147-4
    metadata: null
    note: null
    pmcid: PMC9287168
    pmid: '34980914'
    type:
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    version: null
  - doi: 10.1093/gigascience/giaf118
    metadata: null
    note: null
    pmcid: PMC12530089
    pmid: '41100284'
    type:
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    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version:
  - v0.5.0
- accessibility: Open access
  additionDate: '2025-06-30T08:52:48.500476Z'
  biotoolsCURIE: biotools:fair_eva_4_eucaim
  biotoolsID: fair_eva_4_eucaim
  collectionID:
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  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Fernando Aguilar
    note: 'Creator and devloper of the main package FAIR EVA.

      This software started to be developed within IFCA-Advanced-Computing receives
      funding from the European Union’s Horizon 2020 research and innovation programme
      under grant agreement No 857647.'
    orcidid: https://orcid.org/0000-0001-9462-4831
    rorid: null
    typeEntity: Person
    typeRole:
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    - Maintainer
    - Documentor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: David Rodríguez
    note: Developer of the EUCAIM plugin.
    orcidid: https://orcid.org/0000-0002-9160-5106
    rorid: null
    typeEntity: Person
    typeRole:
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    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Inés Victoria
    note: Developer of the EUCAIM plugin.
    orcidid: https://orcid.org/0009-0003-7950-3937
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: 'FAIR EVA: Evaluator, Validator & Advisor has been developed to check
    the FAIRness level of digital objects from different repositories or data portals.
    It requires the object identifier (preferably persistent and unique identifier)
    and the repository to check. It also provides a generic and agnostic way to check
    digital objects.

    This software started to be developed within IFCA-Advanced-Computing receives
    funding from the European Union’s Horizon 2020 research and innovation programme
    under grant agreement No 857647.'
  documentation:
  - note: Documentation for the main package, EUCAIM plugin specific documentation
      not included.
    type:
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    url: https://github.com/IFCA-Advanced-Computing/FAIR_eva/blob/main/docs/index.md
  - note: Wiki page with FAIR EVA for EUCAIM specific instructions.
    type:
    - User manual
    url: https://gitlabpages.ifca.es/fair-eva-guide-608baa/
  download: []
  editPermission:
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  elixir_badge: 0
  function:
  - cmd: fair-eva-cli batch -p eucaim_legacy
    input:
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        term: Database ID
        uri: http://edamontology.org/data_1048
      format: []
    note: null
    operation:
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      uri: http://edamontology.org/operation_0336
    - term: Validation
      uri: http://edamontology.org/operation_2428
    output:
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        term: Score
        uri: http://edamontology.org/data_1772
      format: []
  homepage: https://github.com/IFCA-Advanced-Computing/FAIR_eva
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  language:
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  lastUpdate: '2026-07-16T12:49:00.960818Z'
  license: Apache-2.0
  link:
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    type:
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    url: https://github.com/IFCA-Advanced-Computing/FAIR_eva
  - note: Repository for FAIR-EVA for eucaim plugin. Currently not public.
    type:
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    url: https://gitlab.ifca.es/eucaim/fair_eucaim
  maturity: Mature
  name: FAIR EVA for EUCAIM
  operatingSystem:
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  otherID: []
  owner: davrodgon
  publication:
  - doi: 10.1038/s41597-023-02652-8
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
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  - Plug-in
  - Web service
  topic:
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    uri: http://edamontology.org/topic_4012
  validated: 0
  version:
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- accessibility: null
  additionDate: '2026-04-15T09:05:44.140162Z'
  biotoolsCURIE: biotools:trackastra-galaxy
  biotoolsID: trackastra-galaxy
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: Trackastra is a transformer-based cell-tracking tool for live-cell
    microscopy. It links already segmented cell instances across time by predicting
    associations between detections. It supports greedy tracking with or without cell
    divisions, optional ILP-based linking, pretrained tracking models, and export
    of tracked masks and lineage information in Cell Tracking Challenge format.
  documentation:
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    type:
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    url: https://github.com/weigertlab/trackastra
  download:
  - note: pypi installation
    type: Downloads page
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  - note: Conda installation
    type: Downloads page
    url: https://anaconda.org/channels/conda-forge/packages/trackastra/overview
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  elixirPlatform:
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  function:
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    - data:
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        uri: http://edamontology.org/data_2968
      format:
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        uri: http://edamontology.org/format_3591
    note: Link already segmented cell instances across a microscopy time series and
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    operation:
    - term: Image annotation
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    output:
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      format:
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    - data:
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    - data:
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      format: []
  - cmd: trackastra track --imgs images/ --masks masks/ --output-ctc tracked_ctc --output-edge-table
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    input:
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    - data:
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      format:
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        uri: http://edamontology.org/format_3591
    note: Link already segmented cell instances across a microscopy time series and
      produce tracked masks, lineage information and association edges.
    operation:
    - term: Image analysis
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    output:
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      format: []
    - data:
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        uri: http://edamontology.org/data_2048
      format: []
    - data:
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        uri: http://edamontology.org/data_2048
      format: []
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  language:
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  lastUpdate: '2026-07-16T11:37:36.608593Z'
  license: BSD-3-Clause
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  maturity: Emerging
  name: trackastra
  operatingSystem:
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  otherID: []
  owner: khaled4
  publication:
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    metadata: null
    note: Peer-reviewed publication describing the Trackastra transformer-based method
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    pmcid: null
    pmid: null
    type:
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    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Light microscopy
    uri: http://edamontology.org/topic_3385
  validated: 0
  version:
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- accessibility: Open access
  additionDate: '2026-02-26T12:47:29.542367Z'
  biotoolsCURIE: biotools:inflexa
  biotoolsID: inflexa
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
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    url: null
  description: Inflexa is an open-source, agentic orchestration platform for computational
    biology and translational medicine. It is designed to assist researchers in analyzing
    multi-omics, cheminformatics, and imaging data by reading published literature,
    designing multi-step analysis plans, and executing experiments with full reproducibility.
  documentation: []
  download:
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    type: Binaries
    url: https://github.com/inflexa-ai/inflexa/releases
    version: null
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  function:
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    note: null
    operation:
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      uri: http://edamontology.org/operation_2945
    output: []
  homepage: https://inflexa.ai
  homepage_status: 0
  language:
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  - Go
  lastUpdate: '2026-07-16T09:46:41.686377Z'
  license: Apache-2.0
  link:
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    type:
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    url: https://github.com/inflexa-ai/inflexa
  - note: Pharmacogenomic Drug Screen in Mouse Cerebrocortical Cultures (Re-analysis
      of GSE110256)
    type:
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    url: https://public.inflexa.ai/GSE110256/pharmacogenomic-mouse-cerebrocortical-cultures
  - note: Genome-wide Dissection of IL-33 and IL-4Rα Blockade in HDM-driven Airway
      Inflammation (Re-analysis of GSE282861)
    type:
    - Other
    url: https://public.inflexa.ai/GSE282861/il-33-il-4ra-blockade-hdm-airway-inflammation
  - note: How Genetic Ancestry Shapes Breast Cancer Biology (Re-analysis of Roelands
      et al. 2021 (npj Breast Cancer) )
    type:
    - Other
    url: https://public.inflexa.ai/33558495/breast-cancer-african-arab-european-ancestry
  - note: ATG7 Deficiency Reshapes Microglia Biology in Alzheimer’s Disease (Re-analysis
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    type:
    - Other
    url: https://public.inflexa.ai/GSE286094-GSE286095/atg7-deficiency-reshapes-microglia-biology-alzheimers-disease
  - note: Atopic Dermatitis Transcriptome Under Dupilumab and Cyclosporine (Re-analysis
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    type:
    - Other
    url: https://public.inflexa.ai/GSE157194/atopic-dermatitis-dupilumab-cyclosporine
  maturity: null
  name: Inflexa
  operatingSystem:
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  - Mac
  - Windows
  otherID: []
  owner: radu.tanasa
  publication: []
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Cheminformatics
    uri: http://edamontology.org/topic_2258
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Natural language processing
    uri: http://edamontology.org/topic_0218
  - term: Omics
    uri: http://edamontology.org/topic_3391
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2026-07-16T05:56:10.804636Z'
  biotoolsCURIE: biotools:reactr
  biotoolsID: reactr
  collectionID:
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  confidence_flag: null
  cost: Free of charge
  credit: []
  description: 'Reactr is an modularized, Snakemake workflow for automated, species-agnostic
    characterization of gene families from sequence to experimental design. Given
    a query protein sequence and NCBI taxonomy IDs (or RefSeq assembly accessions),
    reactr retrieves genomic data and runs comprehensive analysis across 4 integrated
    tiers: (1) evolutionary analysis, including homolog detection, domain-based clustering,
    multiple sequence alignment, and phylogenetic inference; (2) synteny and selection
    analysis, detecting collinear blocks and calculating Ka/Ks ratios; (3) structural
    and regulatory characterization, including motif discovery, chromosomal mapping,
    biochemical property prediction, subcellular localization prediction, and promoter
    analysis; and (4) experimental design tools, generating PCR primers and scored
    CRISPR gRNAs for lab validation. Reactr bridges computational prediction and experimental
    validation, thus enabling rapid transition from genomic discovery to functional
    studies.'
  documentation: []
  download: []
  editPermission:
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  elixirNode: []
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  elixir_badge: 0
  function:
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    note: Loads genomic data and prepares it for query analysis
    operation:
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      uri: http://edamontology.org/operation_2422
    output: []
  - cmd: '`snakemake -s MainPipeline.smk`'
    input: []
    note: Runs query analysis
    operation:
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      uri: http://edamontology.org/operation_0226
    - term: Comparison
      uri: http://edamontology.org/operation_2424
    - term: Generation
      uri: http://edamontology.org/operation_3429
    - term: Prediction and recognition
      uri: http://edamontology.org/operation_2423
    - term: Alignment
      uri: http://edamontology.org/operation_2928
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Indexing
      uri: http://edamontology.org/operation_0227
    - term: Data analysis
      uri: http://edamontology.org/operation_2945
    - term: Protein property calculation
      uri: http://edamontology.org/operation_0250
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    - term: Validation
      uri: http://edamontology.org/operation_2428
    - term: Classification
      uri: http://edamontology.org/operation_2990
    output: []
  homepage: https://zenodo.org/records/21125109
  homepage_status: 0
  language:
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  lastUpdate: '2026-07-16T06:16:23.845202Z'
  license: MIT
  link: []
  maturity: null
  name: REACTR
  operatingSystem:
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  otherID: []
  owner: anteel
  publication: []
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version:
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