count: 30374
list:
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  additionDate: '2025-04-03T15:58:10.986044Z'
  biotoolsCURIE: biotools:optimjv3
  biotoolsID: optimjv3
  collectionID: []
  confidence_flag: null
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  description: Optimize parameters of JARVIS3 - a genomic compressor - for compression
    of genomic sequences
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  download: []
  editPermission:
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  elixir_badge: 0
  function: []
  homepage: https://github.com/cobilab/OptimJV3
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-03T15:58:10.988726Z'
  license: null
  link: []
  maturity: null
  name: OptimJV3
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  owner: ritaferrolho
  publication: []
  relation: []
  toolType: []
  topic: []
  validated: 0
  version: []
- accessibility: null
  additionDate: '2015-01-21T13:30:06Z'
  biotoolsCURIE: biotools:nextprot
  biotoolsID: nextprot
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  - Rare Disease
  - Proteomics
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: SIB Swiss Institute of Bioinformatics
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: support@nextprot.org
    fundrefid: null
    gridid: null
    name: Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Pascale Gaudet
    note: null
    orcidid: http://orcid.org/0000-0003-1813-6857
    rorid: null
    typeEntity: Person
    typeRole:
    - Contributor
    url: null
  description: neXtProt was an innovative knowledge platform dedicated to human proteins.
    This resource contained a wealth of high-quality data on all the human proteins
    that are produced by the 20'000 protein-coding genes found in the human genome.
    The content of neXtProt was continuously extended so as to provide many more carefully
    selected data sets and analysis tools. neXtProt data and tools have been archived.
  documentation: []
  download: []
  editPermission:
    authors:
    - proteomics.bio.tools
    - mzahn
    - LanfearJ
    type: group
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  - Switzerland
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  - Data
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Protein sequence analysis
      uri: http://edamontology.org/operation_2479
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Protein structure analysis
      uri: http://edamontology.org/operation_2406
    output: []
  homepage: https://www.expasy.org/archives/nextprot
  homepage_status: 0
  language: []
  lastUpdate: '2025-04-02T08:05:19.163091Z'
  license: CC-BY-4.0
  link:
  - note: neXtProt software repository with issue tracking
    type:
    - Repository
    url: https://github.com/calipho-sib/
  - note: neXtProt release news, publications, presentations, tips and more. DM us
      with feedback!
    type:
    - Social media
    url: https://twitter.com/nextprot_news
  - note: neXtProt entry in Expasy
    type:
    - Software catalogue
    url: https://www.expasy.org/resources/nextprot
  maturity: Legacy
  name: neXtProt
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: SIB
  publication:
  - doi: 10.1093/nar/gkz995
    metadata:
      abstract: The neXtProt knowledgebase (https://www.nextprot.org) is an integrative
        resource providing both data on human protein and the tools to explore these.
        In order to provide comprehensive and up-to-date data, we evaluate and add
        new data sets. We describe the incorporation of three new data sets that provide
        expression, function, protein-protein binary interaction, post-translational
        modifications (PTM) and variant information. New SPARQL query examples illustrating
        uses of the new data were added. neXtProt has continued to develop tools for
        proteomics. We have improved the peptide uniqueness checker and have implemented
        a new protein digestion tool. Together, these tools make it possible to determine
        which proteases can be used to identify trypsin-resistant proteins by mass
        spectrometry. In terms of usability, we have finished revamping our web interface
        and completely rewritten our API. Our SPARQL endpoint now supports federated
        queries. All the neXtProt data are available via our user interface, API,
        SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally,
        the data on our FTP site is now CC BY 4.0 to promote its reuse.
      authors:
      - name: Zahn-Zabal M.
      - name: Michel P.-A.
      - name: Gateau A.
      - name: Nikitin F.
      - name: Schaeffer M.
      - name: Audot E.
      - name: Gaudet P.
      - name: Duek P.D.
      - name: Teixeira D.
      - name: De Laval V.R.
      - name: Samarasinghe K.
      - name: Bairoch A.
      - name: Lane L.
      citationCount: 155
      date: '2020-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'The neXtProt knowledgebase in 2020: Data, tools and usability improvements'
    note: null
    pmcid: null
    pmid: '31724716'
    type:
    - Primary
    version: null
  - doi: 10.1093/nar/gkr1179
    metadata:
      abstract: neXtProt (http://www.nextprot.org/) is a new human protein-centric
        knowledge platform. Developed at the Swiss Institute of Bioinformatics (SIB),
        it aims to help researchers answer questions relevant to human proteins. To
        achieve this goal, neXtProt is built on a corpus containing both curated knowledge
        originating from the UniProtKB/Swiss-Prot knowledgebase and carefully selected
        and filtered high-throughput data pertinent to human proteins. This article
        presents an overview of the database and the data integration process. We
        also lay out the key future directions of neXtProt that we consider the necessary
        steps to make neXtProt the one-stop-shop for all research projects focusing
        on human proteins. © The Author(s) 2011.
      authors:
      - name: Lane L.
      - name: Argoud-Puy G.
      - name: Britan A.
      - name: Cusin I.
      - name: Duek P.D.
      - name: Evalet O.
      - name: Gateau A.
      - name: Gaudet P.
      - name: Gleizes A.
      - name: Masselot A.
      - name: Zwahlen C.
      - name: Bairoch A.
      citationCount: 168
      date: '2012-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'NeXtProt: A knowledge platform for human proteins'
    note: null
    pmcid: PMC3245017
    pmid: '22139911'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  - term: Protein variants
    uri: http://edamontology.org/topic_3120
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2025-03-31T08:50:20.800385Z'
  biotoolsCURIE: biotools:csdr
  biotoolsID: csdr
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: Differential gene coexpression analysis based on the Conserved, Specific,
    and Differentiated (CSD) method
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://almaaslab.github.io/csdR/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-31T09:00:57.202555Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Software catalogue
    url: https://www.bioconductor.org/packages/release/bioc/html/csdR.html
  - note: null
    type:
    - Repository
    url: https://github.com/AlmaasLab/csdR
  maturity: Mature
  name: csdR
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: japet
  publication:
  - doi: 10.1186/s12859-022-04605-1
    metadata:
      abstract: 'Background: Differential co-expression network analysis has become
        an important tool to gain understanding of biological phenotypes and diseases.
        The CSD algorithm is a method to generate differential co-expression networks
        by comparing gene co-expressions from two different conditions. Each of the
        gene pairs is assigned conserved (C), specific (S) and differentiated (D)
        scores based on the co-expression of the gene pair between the two conditions.
        The result of the procedure is a network where the nodes are genes and the
        links are the gene pairs with the highest C-, S-, and D-scores. However, the
        existing CSD-implementations suffer from poor computational performance, difficult
        user procedures and lack of documentation. Results: We created the R-package
        csdR aimed at reaching good performance together with ease of use, sufficient
        documentation, and with the ability to play well with other tools for data
        analysis. csdR was benchmarked on a realistic dataset with 20,645 genes. After
        verifying that the chosen number of iterations gave sufficient robustness,
        we tested the performance against the two existing CSD implementations. csdR
        was superior in performance to one of the implementations, whereas the other
        did not run. Our implementation can utilize multiple processing cores. However,
        we were unable to achieve more than ∼ 2.7 parallel speedup with saturation
        reached at about 10 cores. Conclusion: The results suggest that csdR is a
        useful tool for differential co-expression analysis and is able to generate
        robust results within a workday on datasets of realistic sizes when run on
        a workstation or compute server.'
      authors:
      - name: Pettersen J.P.
      - name: Almaas E.
      citationCount: 3
      date: '2022-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: csdR, an R package for differential co-expression analysis
    note: null
    pmcid: PMC8858518
    pmid: '35183100'
    type:
    - Method
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Gene regulation
    uri: http://edamontology.org/topic_0204
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-03-27T13:31:15.690258Z'
  biotoolsCURIE: biotools:kisse
  biotoolsID: kisse
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: KISSE is a species search engine that utilizes collagen sequences from
    eight different species to identify unknown samples.
  documentation: []
  download:
  - note: null
    type: Source code
    url: https://github.com/hassanakthv/SIPMS
    version: null
  editPermission:
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    type: private
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  elixir_badge: 0
  function: []
  homepage: https://kisse.serve.scilifelab.se/app/kisse
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-28T21:26:36.097092Z'
  license: null
  link: []
  maturity: null
  name: Species Search Engine - KISSE
  operatingSystem: []
  otherID: []
  owner: egonw
  publication: []
  relation: []
  toolType:
  - Web application
  topic:
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-03-27T13:48:04.671506Z'
  biotoolsCURIE: biotools:titania
  biotoolsID: titania
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Suite for In Silico Property Prediction and NAM-Based Modeling
  documentation: []
  download: []
  editPermission:
    authors: []
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: calculate boiling point
    input:
    - data:
        term: SMILES string
        uri: http://edamontology.org/data_2301
      format: []
    note: calculate boiling point
    operation:
    - term: Calculation
      uri: http://edamontology.org/operation_3438
    output:
    - data:
        term: Structure
        uri: http://edamontology.org/data_0883
      format: []
  homepage: https://enaloscloud.novamechanics.com/EnalosWebApps/titania/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-28T21:26:03.296975Z'
  license: null
  link: []
  maturity: null
  name: Titania
  operatingSystem: []
  otherID: []
  owner: egonw
  publication: []
  relation: []
  toolType: []
  topic: []
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-28T11:13:12.813456Z'
  biotoolsCURIE: biotools:we_sa
  biotoolsID: we_sa
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: 'WeSA, (weighted socioaffinity), is a statistical score which can rank
    results from protein experiments of the bait-prey type, e.g. affinity purification,
    immunoprecipitation or proximity labelling.


    You can enter lists of interacting proteins according to the results of your experiment.
    Input your raw data before any filtering is done.


    As a result you will get a re-ordered list of scores which allows one to understand
    which interactions are more likely than others based on what previous experiments
    can add to your data.'
  documentation:
  - note: null
    type:
    - User manual
    url: https://wesa.russelllab.org/help
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://wesa.russelllab.org/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-03-28T12:14:32.901305Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/russelllab/wesa
  maturity: null
  name: WeSA
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/nar/gkae423
    metadata:
      abstract: 'Protein-protein interaction experiments still yield many false positive
        interactions. The socioaffinity metric can distinguish true protein-protein
        interactions from noise based on available data. Here, we present WeSA (Weighted
        SocioAffinity), which considers large datasets of interaction proteomics data
        (IntAct, BioGRID, the BioPlex) to score human protein interactions and, in
        a statistically robust way, flag those (even from a single experiment) that
        are likely to be false positives. ROC analysis (using CORUM-PDB positives
        and Negatome negatives) shows that WeSA improves over other measures of interaction
        confidence. WeSA shows consistently good results over all datasets (up to:
        AUC = 0.93 and at best threshold: TPR = 0.84, FPR = 0.11, Precision = 0.98).
        WeSA is freely available without login (wesa.russelllab.org). Users can submit
        their own data or look for organized information on human protein interactions
        using the web server. Users can either retrieve available information for
        a list of proteins of interest or calculate scores for new experiments. The
        server outputs either pre-computed or updated WeSA scores for the input enriched
        with information from databases. The summary is presented as a table and a
        network-based visualization allowing the user to remove those nodes/edges
        that the method considers spurious.'
      authors:
      - name: Shtetinska M.M.
      - name: Gonzalez-Sanchez J.-C.
      - name: Beyer T.
      - name: Boldt K.
      - name: Ueffing M.
      - name: Russell R.B.
      citationCount: 1
      date: '2024-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'WeSA: A web server for improving analysis of affinity proteomics data'
    note: null
    pmcid: PMC11223876
    pmid: '38795065'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Protein interaction experiment
    uri: http://edamontology.org/topic_3957
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-28T11:01:52.929561Z'
  biotoolsCURIE: biotools:chemfh
  biotoolsID: chemfh
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: yjc@nudt.edu.cn
    fundrefid: null
    gridid: null
    name: Jiacai Yi
    note: National University of Defense Technology, Changsha, Hunan
    orcidid: https://orcid.org/0000-0001-6823-1882
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://github.com/antwiser
  description: 'An integrated online platform developed for the screening and prediction
    of potential frequent drug hitters, thus improving the efficiency of drug R&D
    in colloidal aggregate, firefly luciferase reporter enzyme inhibition, fluorescence,
    chemical reactivity, and promiscuity. ChemFH: an integrated tool for screening
    frequent false positives in chemical biology and drug discovery.'
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/antwiser/ChemFH/blob/main/README.md
  - note: null
    type:
    - Quick start guide
    url: https://chemfh.scbdd.com/documentation/#/
  download:
  - note: null
    type: Source code
    url: https://github.com/antwiser/ChemFH/releases/tag/ChemFH-1.0.0
    version: 1.0.0
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: SMILES string
        uri: http://edamontology.org/data_2301
      format: []
    note: null
    operation:
    - term: Virtual screening
      uri: http://edamontology.org/operation_3938
    output:
    - data:
        term: Drug report
        uri: http://edamontology.org/data_1696
      format: []
    - data:
        term: Chemical structure image
        uri: http://edamontology.org/data_1712
      format: []
  homepage: https://chemfh.scbdd.com/
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2025-03-28T12:04:07.529430Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/antwiser/ChemFH
  - note: Webpage of the tool
    type:
    - Other
    url: https://chemfh.scbdd.com/
  maturity: Emerging
  name: ChemFH
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: etepf22
  publication:
  - doi: 10.1093/nar/gkae424
    metadata:
      abstract: High-throughput screening rapidly tests an extensive array of chemical
        compounds to identify hit compounds for specific biological targets in drug
        discovery. However, false-positive results disrupt hit compound screening,
        leading to wastage of time and resources. To address this, we propose ChemFH,
        an integrated online platform facilitating rapid virtual evaluation of potential
        false positives, including colloidal aggregators, spectroscopic interference
        compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous
        compounds, and other assay interferences. By leveraging a dataset containing
        823 391 compounds, we constructed high-quality prediction models using multi-task
        directed message-passing network (DMPNN) architectures combining uncertainty
        estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated
        1441 representative alert substructures derived from the collected data and
        ten commonly used frequent hitter screening rules. ChemFH was validated with
        an external set of 75 compounds. Subsequently, the virtual screening capability
        of ChemFH was successfully confirmed through its application to five virtual
        screening libraries. Furthermore, ChemFH underwent additional validation on
        two natural products and FDA-approved drugs, yielding reliable and accurate
        results. ChemFH is a comprehensive, reliable, and computationally efficient
        screening pipeline that facilitates the identification of true positive results
        in assays, contributing to enhanced efficiency and success rates in drug discovery.
        ChemFH is freely available via https://chemfh.scbdd.com/.
      authors:
      - name: Shi S.
      - name: Fu L.
      - name: Yi J.
      - name: Yang Z.
      - name: Zhang X.
      - name: Deng Y.
      - name: Wang W.
      - name: Wu C.
      - name: Zhao W.
      - name: Hou T.
      - name: Zeng X.
      - name: Lyu A.
      - name: Cao D.
      citationCount: 0
      date: '2024-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ChemFH: An integrated tool for screening frequent false positives in
        chemical biology and drug discovery'
    note: High-throughput screening rapidly tests an extensive array of chemical compounds
      to identify hit compounds for specific biological targets in drug discovery.
      However, false-positive results disrupt hit compound screening, leading to wastage
      of time and resources. To address this, we propose ChemFH, an integrated online
      platform facilitating rapid virtual evaluation of potential false positives,
      including colloidal aggregators, spectroscopic interference compounds, firefly
      luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and
      other assay interferences. ChemFH is freely available via https://chemfh.scbdd.com/.
    pmcid: PMC11223804
    pmid: '38783035'
    type:
    - Method
    version: 1.0.0
  relation: []
  toolType:
  - Script
  - Command-line tool
  topic:
  - term: Medicinal chemistry
    uri: http://edamontology.org/topic_0209
  validated: 0
  version:
  - 1.0.0
- accessibility: Open access
  additionDate: '2025-03-28T11:01:27.681477Z'
  biotoolsCURIE: biotools:lncrnaway
  biotoolsID: lncrnaway
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: qlyu@cqu.edu.cn
    fundrefid: null
    gridid: null
    name: Chongqing General Hospital, Chongqing University, Lyu Lab
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: A web-based sgRNA design tool for precise and effective suppression
    of long noncoding RNAs
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Ensembl gene ID
        uri: http://edamontology.org/data_1033
      format: []
    - data:
        term: Gene symbol
        uri: http://edamontology.org/data_1026
      format: []
    - data:
        term: RNA sequence
        uri: http://edamontology.org/data_3495
      format:
      - term: GFF
        uri: http://edamontology.org/format_2305
    note: Design of sgRNA for lncRNA knockout; Primer design for PCR and qPCR to knockout
      detection.
    operation:
    - term: Sequence generation
      uri: http://edamontology.org/operation_0230
    - term: PCR primer design
      uri: http://edamontology.org/operation_0308
    output:
    - data:
        term: RNA sequence
        uri: http://edamontology.org/data_3495
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
      - term: PDF
        uri: http://edamontology.org/format_3508
    - data:
        term: Text mining report
        uri: http://edamontology.org/data_0972
      format: []
  homepage: https://www.lncrnaway.com
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-28T11:54:36.402981Z'
  license: null
  link:
  - note: null
    type:
    - Service
    url: https://www.lncrnaway.com
  maturity: Mature
  name: LncRNAway
  operatingSystem:
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  - Linux
  - Windows
  otherID: []
  owner: inespinheiro
  publication:
  - doi: 10.1093/nar/gkae383
    metadata:
      abstract: Thousands of long noncoding RNAs (lncRNAs) have been annotated via
        high-throughput RNA sequencing, yet only a small fraction have been functionally
        investigated. Genomic knockout is the mainstream strategy for studying the
        biological function of protein-coding genes and lncRNAs, whereas the complexity
        of the lncRNA locus, especially the natural antisense lncRNAs (NAT-lncRNAs),
        presents great challenges. Knocking out lncRNAs often results in unintended
        disruptions of neighboring protein-coding genes and small RNAs, leading to
        ambiguity in observing phenotypes and interpreting biological function. To
        address this issue, we launched LncRNAway, a user-friendly web tool based
        on the BESST (branchpoint to 3' splicing site targeting) method, to design
        sgRNAs for lncRNA knockout. LncRNAway not only provides specific and effective
        lncRNA knockout guidelines but also integrates genotyping primers and quantitative
        PCR primers designing, thereby streamlining experimental procedures of lncRNA
        function study. LncRNAway is freely available at https://www.lncrnaway.com.
      authors:
      - name: Zhang S.
      - name: Wang S.
      - name: Lu F.
      - name: Bie L.
      - name: Luo Y.
      - name: Sun J.
      - name: Zhang Y.
      - name: Wang Y.
      - name: Zhang Y.
      - name: Lyu Q.R.
      citationCount: 0
      date: '2024-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'LncRNAway: A web-based sgRNA design tool for precise and effective suppression
        of long noncoding RNAs'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Functional genomics
    uri: http://edamontology.org/topic_0085
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-28T11:20:32.972026Z'
  biotoolsCURIE: biotools:isovis
  biotoolsID: isovis
  collectionID:
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  confidence_flag: null
  cost: Free of charge
  credit: []
  description: A webserver for visualization and annotation of alternative RNA isoforms
  documentation:
  - note: null
    type:
    - User manual
    url: https://isovis.gandallab.org/tutorial/
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  - note: null
    type: Source code
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    version: null
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  - cmd: --
    input:
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        term: Protein complex
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      - term: GTF
        uri: http://edamontology.org/format_2306
      - term: BED
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      - term: CSV
        uri: http://edamontology.org/format_3752
    note: isoform stack
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    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output:
    - data:
        term: Structure
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      format: []
  homepage: https://isomix.org/isovis/
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2025-03-28T11:41:10.718643Z'
  license: MPL-2.0
  link:
  - note: a webserver
    type:
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    url: https://isomix.org/isovis/
  - note: null
    type:
    - Repository
    url: https://github.com/ClarkLaboratory/IsoVis?tab=MPL-2.0-1-ov-file
  - note: Nucleic Acids Research
    type:
    - Other
    url: https://academic.oup.com/nar/article/52/W1/W341/7665639
  maturity: Mature
  name: IsoVis
  operatingSystem:
  - Linux
  - Windows
  otherID: []
  owner: Kvetoslava_Mahutova
  publication: []
  relation: []
  toolType:
  - Web application
  topic:
  - term: Biosciences
    uri: http://edamontology.org/topic_4019
  validated: 0
  version:
  - v1.10
- accessibility: null
  additionDate: '2025-03-28T11:04:18.911217Z'
  biotoolsCURIE: biotools:ddmut-ppi
  biotoolsID: ddmut-ppi
  collectionID: []
  confidence_flag: null
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  - email: null
    fundrefid: null
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    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: https://biosig.lab.uq.edu.au/contact
  description: A deep learning model that efficiently and accurately predicts the
    changes in PPI binding free energy upon single and multiple point mutations.
  documentation:
  - note: null
    type:
    - API documentation
    url: https://biosig.lab.uq.edu.au/ddmut_ppi/api
  download:
  - note: null
    type: Biological data
    url: https://biosig.lab.uq.edu.au/ddmut_ppi/datasets
    version: null
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  function:
  - cmd: $ curl https://biosig.lab.uq.edu.au/ddmut_ppi/api/single -X POST -i -F pdb_file=@/home/ubuntu/1cse.pdb
      -F mutation=L45G -F chain=I -F reverse=True
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        term: Text data
        uri: http://edamontology.org/data_2526
      format: []
    note: Single Prediction - Job Submission
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      uri: http://edamontology.org/operation_0276
    output:
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        term: Identifier
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      format: []
  homepage: https://biosig.lab.uq.edu.au/ddmut_ppi/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-28T11:05:03.279672Z'
  license: null
  link: []
  maturity: null
  name: DDMut-PPI
  operatingSystem:
  - Linux
  otherID: []
  owner: TomasGeraldes
  publication:
  - doi: 10.1093/nar/gkae412
    metadata:
      abstract: 'Protein-protein interactions (PPIs) play a vital role in cellular
        functions and are essential for therapeutic development and understanding
        diseases. However, current predictive tools often struggle to balance efficiency
        and precision in predicting the effects of mutations on these complex interactions.
        To address this, we present DDMut-PPI, a deep learning model that efficiently
        and accurately predicts changes in PPI binding free energy upon single and
        multiple point mutations. Building on the robust Siamese network architecture
        with graph-based signatures from our prior work, DDMut, the DDMut-PPI model
        was enhanced with a graph convolutional network operated on the protein interaction
        interface. We used residue-specific embeddings from ProtT5 protein language
        model as node features, and a variety of molecular interactions as edge features.
        By integrating evolutionary context with spatial information, this framework
        enables DDMut-PPI to achieve a robust Pearson correlation of up to 0.75 (root
        mean squared error: 1.33 kcal/mol) in our evaluations, outperforming most
        existing methods. Importantly, the model demonstrated consistent performance
        across mutations that increase or decrease binding affinity. DDMut-PPI offers
        a significant advancement in the field and will serve as a valuable tool for
        researchers probing the complexities of protein interactions. DDMut-PPI is
        freely available as a web server and an application programming interface
        at https://biosig.lab.uq.edu.au/ddmut_ppi.'
      authors:
      - name: Zhou Y.
      - name: Myung Y.
      - name: Rodrigues C.H.M.
      - name: Ascher D.B.
      citationCount: 6
      date: '2024-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'DDMut-PPI: Predicting effects of mutations on protein-protein interactions
        using graph-based deep learning'
    note: null
    pmcid: PMC11223791
    pmid: '38783112'
    type:
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    version: null
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  toolType:
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  topic:
  - term: Computational chemistry
    uri: http://edamontology.org/topic_3332
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-02-28T15:04:33.594183Z'
  biotoolsCURIE: biotools:prolfquapp
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  collectionID: []
  confidence_flag: null
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  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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  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:
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        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
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        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:
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        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
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        uri: http://edamontology.org/format_3620
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
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        term: Expression data
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      format:
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    note: null
    operation:
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      uri: http://edamontology.org/operation_2428
    output:
    - data:
        term: Quality control report
        uri: http://edamontology.org/data_3914
      format:
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  - cmd: null
    input:
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        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
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        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: null
  additionDate: '2021-10-29T13:09:25.625178Z'
  biotoolsCURIE: biotools:workflowhub
  biotoolsID: workflowhub
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: finn.bacall@manchester.ac.uk
    fundrefid: null
    gridid: null
    name: Finn Bacall
    note: null
    orcidid: https://orcid.org/0000-0002-0048-3300
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    - Developer
    - Primary contact
    url: null
  - 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: 'WorkflowHub is a registry for describing, sharing and publishing scientific
    computational workflows.


    The registry supports any workflow in its native repository.


    WorkflowHub aims to facilitate discovery and re-use of workflows in an accessible
    and interoperable way. This is achieved through extensive use of open standards
    and tools, including Common Workflow Language (CWL), RO-Crate, BioSchemas and
    TRS, in accordance with the FAIR principles.'
  documentation:
  - note: null
    type:
    - General
    url: https://about.workflowhub.eu/
  - note: null
    type:
    - API documentation
    url: https://workflowhub.eu/api
  download: []
  editPermission:
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  elixirCommunity: []
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    - term: Filtering
      uri: http://edamontology.org/operation_3695
    - term: Parsing
      uri: http://edamontology.org/operation_1812
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    output: []
  homepage: https://workflowhub.eu
  homepage_status: 0
  language:
  - Ruby
  lastUpdate: '2025-03-28T09:56:03.670536Z'
  license: BSD-3-Clause
  link:
  - note: 'While WorkflowHub is largely developed as a collaboration between several
      projects, any contributors are welcome to join our open community: the WorkflowHub
      Club.'
    type:
    - Discussion forum
    url: https://about.workflowhub.eu/community/
  maturity: Emerging
  name: WorkflowHub
  operatingSystem:
  - Linux
  otherID: []
  owner: fbacall
  publication: []
  relation:
  - biotoolsID: seek_for_science
    type: uses
  toolType:
  - Database portal
  - Web API
  - Web application
  topic:
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  validated: 0
  version: []
- 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:
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    - 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
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    - ilveroluca
    type: group
  elixirCommunity: []
  elixirNode:
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  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:
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    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:
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  - 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:
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    gridid: null
    name: Marco Enrico Piras
    note: null
    orcidid: https://orcid.org/0000-0002-5207-0030
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: simone.leo@crs4.it
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    note: null
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    url: null
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    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:
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    url: https://lifemonitor.eu
  - note: null
    type:
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  language:
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  - JavaScript
  lastUpdate: '2025-03-25T12:17:42.289116Z'
  license: MIT
  link:
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    type:
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    url: https://github.com/crs4/life_monitor
  - note: REST API (production instance)
    type:
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  - note: REST API (dev instance)
    type:
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  - note: Web App (production instance)
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    - Service
    url: https://app.lifemonitor.eu
  - note: Web App (dev instance)
    type:
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  - note: null
    type:
    - Issue tracker
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  maturity: Mature
  name: LifeMonitor
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: mep
  publication: []
  relation:
  - biotoolsID: rocrate-validator
    type: uses
  toolType:
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  - 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: '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: []
  homepage: https://minerva.uni.lu
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-24T08:35:28.416761Z'
  license: AGPL-3.0
  link:
  - note: GiLab repository for core functionalities (data and format handling, service
      stability, API access)
    type:
    - Repository
    url: https://gitlab.lcsb.uni.lu/minerva/core/
  - note: Issue tracker for core functionalities (data and format handling, service
      stability, API access)
    type:
    - Issue tracker
    url: https://gitlab.lcsb.uni.lu/minerva/core/-/issues
  - note: GiLab repository for frontend functionalities
    type:
    - Repository
    url: https://gitlab.lcsb.uni.lu/minerva/frontend
  - note: Issue tracker for frontend functionalities
    type:
    - Issue tracker
    url: https://gitlab.lcsb.uni.lu/minerva/frontend/-/issues
  maturity: Mature
  name: The MINERVA Platform
  operatingSystem: []
  otherID: []
  owner: mjostaszewski
  publication:
  - doi: 10.1038/npjsba.2016.20
    metadata:
      abstract: Our growing knowledge about various molecular mechanisms is becoming
        increasingly more structured and accessible. Different repositories of molecular
        interactions and available literature enable construction of focused and high-quality
        molecular interaction networks. Novel tools for curation and exploration of
        such networks are needed, in order to foster the development of a systems
        biology environment. In particular, solutions for visualization, annotation
        and data cross-linking will facilitate usage of network-encoded knowledge
        in biomedical research. To this end we developed the MINERVA (Molecular Interaction
        NEtwoRks VisuAlization) platform, a standalone webservice supporting curation,
        annotation and visualization of molecular interaction networks in Systems
        Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated
        content annotation and verification for improved quality control. The end
        users can explore and interact with hosted networks, and provide direct feedback
        to content curators. MINERVA enables mapping drug targets or overlaying experimental
        data on the visualized networks. Extensive export functions enable downloading
        areas of the visualized networks as SBGN-compliant models for efficient reuse
        of hosted networks. The software is available under Affero GPL 3.0 as a Virtual
        Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/.
        We believe that MINERVA is an important contribution to systems biology community,
        as its architecture enables set-up of locally or globally accessible SBGN-oriented
        repositories of molecular interaction networks. Its functionalities allow
        overlay of multiple information layers, facilitating exploration of content
        and interpretation of data. Moreover, annotation and verification workflows
        of MINERVA improve the efficiency of curation of networks, allowing life-science
        researchers to better engage in development and use of biomedical knowledge
        repositories.
      authors:
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Satagopam V.
      - name: Gebel S.
      - name: Mazein A.
      - name: Kuzma M.
      - name: Zorzan S.
      - name: McGee F.
      - name: Otjacques B.
      - name: Balling R.
      - name: Schneider R.
      citationCount: 60
      date: '2016-01-01T00:00:00Z'
      journal: npj Systems Biology and Applications
      title: MINERVA—A platform for visualization and curation of molecular interaction
        networks
    note: null
    pmcid: PMC5516855
    pmid: '28725475'
    type:
    - Primary
    version: '10.0'
  - doi: 10.1093/bioinformatics/btz286
    metadata:
      abstract: 'Summary: The complexity of molecular networks makes them difficult
        to navigate and interpret, creating a need for specialized software. MINERVA
        is a web platform for visualization, exploration and management of molecular
        networks. Here, we introduce an extension to MINERVA architecture that greatly
        facilitates the access and use of the stored molecular network data. It allows
        to incorporate such data in analytical pipelines via a programmatic access
        interface, and to extend the platform''s visual exploration and analytics
        functionality via plugin architecture. This is possible for any molecular
        network hosted by the MINERVA platform encoded in well-recognized systems
        biology formats. To showcase the possibilities of the plugin architecture,
        we have developed several plugins extending the MINERVA core functionalities.
        In the article, we demonstrate the plugins for interactive tree traversal
        of molecular networks, for enrichment analysis and for mapping and visualization
        of known disease variants or known adverse drug reactions to molecules in
        the network. Availability and implementation: Plugins developed and maintained
        by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/.
        The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.'
      authors:
      - name: Hoksza D.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Smula E.
      - name: Schneider R.
      citationCount: 24
      date: '2019-11-01T00:00:00Z'
      journal: Bioinformatics
      title: 'MINERVA API and plugins: Opening molecular network analysis and visualization
        to the community'
    note: null
    pmcid: PMC6821317
    pmid: '31074494'
    type:
    - Primary
    version: 12.2.3
  - doi: 10.1093/bib/bbz067
    metadata:
      abstract: The understanding of complex biological networks often relies on both
        a dedicated layout and a topology. Currently, there are three major competing
        layout-aware systems biology formats, but there are no software tools or software
        libraries supporting all of them. This complicates the management of molecular
        network layouts and hinders their reuse and extension. In this paper, we present
        a high-level overview of the layout formats in systems biology, focusing on
        their commonalities and differences, review their support in existing software
        tools, libraries and repositories and finally introduce a new conversion module
        within the MINERVA platform. The module is available via a REST API and offers,
        besides the ability to convert between layout-aware systems biology formats,
        the possibility to export layouts into several graphical formats. The module
        enables conversion of very large networks with thousands of elements, such
        as disease maps or metabolic reconstructions, rendering it widely applicable
        in systems biology.
      authors:
      - name: Hoksza D.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Hasenauer J.
      - name: Schneider R.
      citationCount: 15
      date: '2019-07-10T00:00:00Z'
      journal: Briefings in Bioinformatics
      title: Closing the gap between formats for storing layout information in systems
        biology
    note: null
    pmcid: PMC7373180
    pmid: '31273380'
    type:
    - Primary
    version: 13.1.1
  - doi: 10.1089/big.2015.0057
    metadata:
      abstract: Translational medicine is a domain turning results of basic life science
        research into new tools and methods in a clinical environment, for example,
        as new diagnostics or therapies. Nowadays, the process of translation is supported
        by large amounts of heterogeneous data ranging from medical data to a whole
        range of -omics data. It is not only a great opportunity but also a great
        challenge, as translational medicine big data is difficult to integrate and
        analyze, and requires the involvement of biomedical experts for the data processing.
        We show here that visualization and interoperable workflows, combining multiple
        complex steps, can address at least parts of the challenge. In this article,
        we present an integrated workflow for exploring, analysis, and interpretation
        of translational medicine data in the context of human health. Three Web services
        - tranSMART, a Galaxy Server, and a MINERVA platform - are combined into one
        big data pipeline. Native visualization capabilities enable the biomedical
        experts to get a comprehensive overview and control over separate steps of
        the workflow. The capabilities of tranSMART enable a flexible filtering of
        multidimensional integrated data sets to create subsets suitable for downstream
        processing. A Galaxy Server offers visually aided construction of analytical
        pipelines, with the use of existing or custom components. A MINERVA platform
        supports the exploration of health and disease-related mechanisms in a contextualized
        analytical visualization system. We demonstrate the utility of our workflow
        by illustrating its subsequent steps using an existing data set, for which
        we propose a filtering scheme, an analytical pipeline, and a corresponding
        visualization of analytical results. The workflow is available as a sandbox
        environment, where readers can work with the described setup themselves. Overall,
        our work shows how visualization and interfacing of big data processing services
        facilitate exploration, analysis, and interpretation of translational medicine
        data.
      authors:
      - name: Satagopam V.
      - name: Gu W.
      - name: Eifes S.
      - name: Gawron P.
      - name: Ostaszewski M.
      - name: Gebel S.
      - name: Barbosa-Silva A.
      - name: Balling R.
      - name: Schneider R.
      citationCount: 38
      date: '2016-06-01T00:00:00Z'
      journal: Big Data
      title: Integration and Visualization of Translational Medicine Data for Better
        Understanding of Human Diseases
    note: null
    pmcid: PMC4932659
    pmid: '27441714'
    type:
    - Usage
    version: '10.0'
  - doi: 10.1016/j.envpol.2019.04.005
    metadata:
      abstract: 'Polychlorinated biphenyls (PCBs) are a class of widespread environmental
        pollutants, commonly found in human blood, that have been suggested to be
        linked to the occurrence of sporadic Parkinson''s disease (PD). It has been
        reported that some non-coplanar PCBs accumulate in the brains of female PD
        patients. To improve our understanding of the association between PCB exposure
        and PD risk we have applied whole transcriptome gene expression analysis in
        blood cells from 594 PCB-exposed subjects (369 female, 225 male). Interestingly,
        we observe that in females, blood levels of non-coplanar PCBs appear to be
        associated with expression levels of PD-specific genes. However, no such association
        was detected in males. Among the 131 PD-specific genes affected, 39 have been
        shown to display similar changes in expression levels in the substantia nigra
        of deceased PD patients. Especially among the down-regulated genes, transcripts
        of genes involved in neurotransmitter vesicle-related functions were predominant.
        Capsule: Plasma PCB levels are associated with gene expression changes in
        females only, resulting in brain-related genes changing in blood cells of
        healthy individuals exposed to PCBs.'
      authors:
      - name: Bohler S.
      - name: Krauskopf J.
      - name: Espin-Perez A.
      - name: Gebel S.
      - name: Palli D.
      - name: Rantakokko P.
      - name: Kiviranta H.
      - name: Kyrtopoulos S.A.
      - name: Balling R.
      - name: Kleinjans J.
      citationCount: 5
      date: '2019-07-01T00:00:00Z'
      journal: Environmental Pollution
      title: Genes associated with Parkinson's disease respond to increasing polychlorinated
        biphenyl levels in the blood of healthy females
    note: null
    pmcid: null
    pmid: '30991279'
    type: []
    version: 13.1.1
  relation: []
  toolType: []
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Omics
    uri: http://edamontology.org/topic_3391
  - term: Translational medicine
    uri: http://edamontology.org/topic_3342
  validated: 0
  version:
  - 13.1.3
  - 13.2.0
  - 14.0.13
  - 15.0.3
  - 16.4.0
  - 17.1.3
  - 18.1.1
- accessibility: null
  additionDate: '2017-02-10T14:15:18Z'
  biotoolsCURIE: biotools:disulfind
  biotoolsID: disulfind
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: disulfind@ai.dinfo.unifi.it
    fundrefid: null
    gridid: null
    name: DISULFIND Support
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: DISULFIND is a server for predicting the disulfide bonding state of
    cysteines and their disulfide connectivity given a protein sequence.
  documentation:
  - note: null
    type:
    - General
    url: http://disulfind.dsi.unifi.it/help.php
  download: []
  editPermission:
    authors:
    - schmidt
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein sequence analysis
      uri: http://edamontology.org/operation_2479
    - term: Prediction and recognition
      uri: http://edamontology.org/operation_2423
    - term: Protein cysteine and disulfide bond assignment
      uri: http://edamontology.org/operation_1850
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    output: []
  homepage: http://disulfind.dsi.unifi.it/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-23T11:05:22.310421Z'
  license: null
  link: []
  maturity: null
  name: DISULFIND
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-EE
  publication:
  - doi: 10.1093/nar/gkl266
    metadata: null
    note: null
    pmcid: PMC1538823
    pmid: '16844986'
    type: []
    version: null
  - doi: 10.1093/bioinformatics/btg463
    metadata: null
    note: null
    pmcid: null
    pmid: '15033872'
    type: []
    version: null
  - doi: 10.1093/nar/gkr365
    metadata: null
    note: null
    pmcid: PMC3125771
    pmid: '21576237'
    type: []
    version: null
  - doi: 10.1093/bioinformatics/btn371
    metadata: null
    note: null
    pmcid: PMC2732205
    pmid: '18635571'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Sequence composition, complexity and repeats
    uri: http://edamontology.org/topic_0157
  - term: Gene and protein families
    uri: http://edamontology.org/topic_0623
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2025-03-22T00:35:21.183948Z'
  biotoolsCURIE: biotools:deepannotation
  biotoolsID: deepannotation
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: The python package 'DeepAnnotation' can be used to perform genomic
    selection (GS), which is a promising breeding strategy for agricultural breeding.
    DeepAnnotation predicts phenotypes from comprehensive multi-omics functional annotations
    with interpretable deep learning framework.
  documentation: []
  download:
  - note: GitHub repository
    type: Downloads page
    url: https://github.com/mawenlong2016/DeepAnnotation
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/mawenlong2016/DeepAnnotation
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-03-22T00:35:21.186342Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/mawenlong2016/DeepAnnotation
  maturity: null
  name: DeepAnnotation
  operatingSystem: []
  otherID: []
  owner: mawenlong2025
  publication: []
  relation: []
  toolType:
  - Command-line tool
  - Script
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-03-20T09:46:51.356874Z'
  biotoolsCURIE: biotools:spacedust
  biotoolsID: spacedust
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: Spacedust is a modular toolkit for identification of conserved gene
    clusters among multiple genomes based on homology and conservation of gene neighborhood.
    Spacedust adapts the fast and sensitive structure comparisons of Foldseek and
    homology search capabilities of MMseqs2. It introduces a novel approach of aggregating
    sets of homologous hits between pairs of genomes and identifies cluster of hits
    with conserved gene neighborhood between each using agglomerative hierarchical
    clustering algorithm.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/soedinglab/spacedust/
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2025-03-20T09:46:51.359476Z'
  license: GPL-3.0
  link: []
  maturity: Emerging
  name: Spacedust
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: milot-mirdita
  publication:
  - doi: 10.1101/2024.10.02.616292v1
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version: []
- accessibility: null
  additionDate: '2021-01-18T09:26:31Z'
  biotoolsCURIE: biotools:ppai
  biotoolsID: ppai
  collectionID: []
  confidence_flag: tool
  cost: null
  credit:
  - email: lijianwei@hebut.edu.cn
    fundrefid: null
    gridid: null
    name: Jianwei Li
    note: null
    orcidid: https://orcid.org/0000-0002-9795-2635
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'PPAI is a web server for predicting protein-aptamer interactions.


    The interactions between proteins and aptamers are prevalent in organisms and
    play an important role in various life activities. Thanks to the rapid accumulation
    of protein-aptamer interaction data, it is very important and feasible to construct
    an accurate computational model to predict protein-aptamer interactions, which
    is benefit for understanding mechanisms of protein-aptamer interactions and improving
    aptamer-based therapies.


    PPAI is a novel web-server to predict aptamers and protein-aptamer interactions
    with sequence features of proteins/aptamers and machine learning framework which
    integrated Adaboost and Random Forest. PPAI not only provides more accurate prediction
    functions, but also provides the protein aptamer information to the user for query.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein interaction prediction
      uri: http://edamontology.org/operation_2492
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence feature detection
      uri: http://edamontology.org/operation_0253
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output: []
  homepage: http://39.96.85.9/PPAI
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-20T07:27:32.204279Z'
  license: null
  link: []
  maturity: null
  name: PPAI
  operatingSystem: []
  otherID: []
  owner: zsmag19
  publication:
  - doi: 10.1186/S12859-020-03574-7
    metadata:
      abstract: 'Background: The interactions between proteins and aptamers are prevalent
        in organisms and play an important role in various life activities. Thanks
        to the rapid accumulation of protein-aptamer interaction data, it is necessary
        and feasible to construct an accurate and effective computational model to
        predict aptamers binding to certain interested proteins and protein-aptamer
        interactions, which is beneficial for understanding mechanisms of protein-aptamer
        interactions and improving aptamer-based therapies. Results: In this study,
        a novel web server named PPAI is developed to predict aptamers and protein-aptamer
        interactions with key sequence features of proteins/aptamers and a machine
        learning framework integrated adaboost and random forest. A new method for
        extracting several key sequence features of both proteins and aptamers is
        presented, where the features for proteins are extracted from amino acid composition,
        pseudo-amino acid composition, grouped amino acid composition, C/T/D composition
        and sequence-order-coupling number, while the features for aptamers are extracted
        from nucleotide composition, pseudo-nucleotide composition (PseKNC) and normalized
        Moreau-Broto autocorrelation coefficient. On the basis of these feature sets
        and balanced the samples with SMOTE algorithm, we validate the performance
        of PPAI by the independent test set. The results demonstrate that the Area
        Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches
        0.871 for prediction of protein-aptamer interactions. Conclusion: These results
        indicate that PPAI can query aptamers and proteins, predict aptamers and predict
        protein-aptamer interactions in batch mode precisely and efficiently, which
        would be a novel bioinformatics tool for the research of protein-aptamer interactions.
        PPAI web-server is freely available at http://39.96.85.9/PPAI.'
      authors:
      - name: Li J.
      - name: Ma X.
      - name: Li X.
      - name: Gu J.
      citationCount: 14
      date: '2020-06-09T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'PPAI: A web server for predicting protein-aptamer interactions'
    note: null
    pmcid: PMC7285591
    pmid: '32517696'
    type: []
    version: null
  - doi: 10.21203/RS.3.RS-27174/V2
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2025-03-11T14:55:53.492167Z'
  biotoolsCURIE: biotools:molgenis_european_health_research_data_and_sample_catalogue
  biotoolsID: molgenis_european_health_research_data_and_sample_catalogue
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: A collaborative effort to integrate the catalogues of diverse EU research
    projects and networks to accelerate reuse and improve citizens health.
  documentation: []
  download: []
  editPermission:
    authors:
    - mswertz
    - EleanorHyde
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://data-catalogue.molgeniscloud.org/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-19T15:08:24.036904Z'
  license: null
  link: []
  maturity: null
  name: European Health Research Data and Sample Catalogue
  operatingSystem: []
  otherID: []
  owner: EleanorHyde
  publication:
  - doi: 10.1055/s-0042-1742522
    metadata:
      abstract: 'Objectives: Existing individual-level human data cover large populations
        on many dimensions such as lifestyle, demography, laboratory measures, clinical
        parameters, etc. Recent years have seen large investments in data catalogues
        to FAIRify data descriptions to capitalise on this great promise, i.e. make
        catalogue contents more Findable, Accessible, Interoperable and Reusable.
        However, their valuable diversity also created heterogeneity, which poses
        challenges to optimally exploit their richness. Methods: In this opinion review,
        we analyse catalogues for human subject research ranging from cohort studies
        to surveillance, administrative and healthcare records. Results: We observe
        that while these catalogues are heterogeneous, have various scopes, and use
        different terminologies, still the underlying concepts seem potentially harmonizable.
        We propose a unified framework to enable catalogue data sharing, with catalogues
        of multi-center cohorts nested as a special case in catalogues of real-world
        data sources. Moreover, we list recommendations to create an integrated community
        of metadata catalogues and an open catalogue ecosystem to sustain these efforts
        and maximise impact. Conclusions: We propose to embrace the autonomy of motivated
        catalogue teams and invest in their collaboration via minimal standardisation
        efforts such as clear data licensing, persistent identifiers for linking same
        records between catalogues, minimal metadata ''common data elements'' using
        shared ontologies, symmetric architectures for data sharing (push/pull) with
        clear provenance tracks to process updates and acknowledge original contributors.
        And most importantly, we encourage the creation of environments for collaboration
        and resource sharing between catalogue developers, building on international
        networks such as OpenAIRE and research data alliance, as well as domain specific
        ESFRIs such as BBMRI and ELIXIR.'
      authors:
      - name: Swertz M.
      - name: Van Enckevort E.
      - name: Oliveira J.L.
      - name: Fortier I.
      - name: Bergeron J.
      - name: Thurin N.H.
      - name: Hyde E.
      - name: Kellmann A.
      - name: Pahoueshnja R.
      - name: Sturkenboom M.
      - name: Cunnington M.
      - name: Nybo Andersen A.-M.
      - name: Marcon Y.
      - name: Goncalves G.
      - name: Gini R.
      citationCount: 9
      date: '2022-12-04T00:00:00Z'
      journal: Yearbook of Medical Informatics
      title: Towards an Interoperable Ecosystem of Research Cohort and Real-world
        Data Catalogues Enabling Multi-center Studies
    note: null
    pmcid: PMC9719789
    pmid: '36463884'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic: []
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-12T22:55:02.224894Z'
  biotoolsCURIE: biotools:matheat
  biotoolsID: matheat
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: seyyedamin1381@gmail.com
    fundrefid: null
    gridid: null
    name: Seyyed Amin Seyyed Rezaei
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    - Maintainer
    url: https://matheat.streamlit.app/
  description: MatHeat transforms your gene expression data into stunning interactive
    heatmaps with advanced clustering, UMAP/KMeans, and Reactome pathway analysis.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://matheat.streamlit.app/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-19T15:07:19.209944Z'
  license: null
  link:
  - note: null
    type:
    - Service
    url: https://matheat.streamlit.app/
  - note: Landing page of the MatHeat
    type:
    - Other
    url: https://amin-mat.github.io/matheat-landing/
  - note: null
    type:
    - Other
    url: https://matheat.org/
  maturity: null
  name: MatHeat
  operatingSystem: []
  otherID: []
  owner: Aminn
  publication: []
  relation: []
  toolType: []
  topic:
  - term: Human genetics
    uri: http://edamontology.org/topic_3574
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Software engineering
    uri: http://edamontology.org/topic_3372
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Medical informatics
    uri: http://edamontology.org/topic_3063
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2016-05-04T15:36:53Z'
  biotoolsCURIE: biotools:inps-md
  biotoolsID: inps-md
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: piero.fariselli@unito.it
    fundrefid: null
    gridid: null
    name: Piero Fariselli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: savojard@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://biocomp.unibo.it/savojard/
  description: Predicting the impact of mutations on protein stability from sequence
    and structure.
  documentation:
  - note: null
    type:
    - General
    url: https://inpsmd.biocomp.unibo.it/help
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    - Pub2Tools
    type: group
  elixirCommunity:
  - 3D-BioInfo
  elixirNode:
  - Italy
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format: []
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format: []
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
      from sequence.
    operation: []
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format: []
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format: []
    - data:
        term: Polypeptide chain ID
        uri: http://edamontology.org/data_1008
      format: []
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format: []
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
      from structure.
    operation: []
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format: []
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format: []
  homepage: http://inpsmd.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-19T15:01:04.207003Z'
  license: null
  link: []
  maturity: Mature
  name: INPS-MD
  operatingSystem: []
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btv291
    metadata:
      abstract: 'Motivation: A tool for reliably predicting the impact of variations
        on protein stability is extremely important for both protein engineering and
        for understanding the effects of Mendelian and somatic mutations in the genome.
        Next Generation Sequencing studies are constantly increasing the number of
        protein sequences. Given the huge disproportion between protein sequences
        and structures, there is a need for tools suited to annotate the effect of
        mutations starting from protein sequence without relying on the structure.
        Here, we describe INPS, a novel approach for annotating the effect of non-synonymous
        mutations on the protein stability from its sequence. INPS is based on SVM
        regression and it is trained to predict the thermodynamic free energy change
        upon single-point variations in protein sequences. Results: We show that INPS
        performs similarly to the state-of-the-art methods based on protein structure
        when tested in cross-validation on a non-redundant dataset. INPS performs
        very well also on a newly generated dataset consisting of a number of variations
        occurring in the tumor suppressor protein p53. Our results suggest that INPS
        is a tool suited for computing the effect of non-synonymous polymorphisms
        on protein stability when the protein structure is not available. We also
        show that INPS predictions are complementary to those of the state-of-the-art,
        structure-based method mCSM. When the two methods are combined, the overall
        prediction on the p53 set scores significantly higher than those of the single
        methods.'
      authors:
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Casadio R.
      citationCount: 111
      date: '2015-02-06T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS: Predicting the impact of non-synonymous variations on protein
        stability from sequence'
    note: null
    pmcid: null
    pmid: '25957347'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btw192
    metadata:
      abstract: 'Motivation: Protein function depends on its structural stability.
        The effects of single point variations on protein stability can elucidate
        the molecular mechanisms of human diseases and help in developing new drugs.
        Recently, we introduced INPS, a method suited to predict the effect of variations
        on protein stability from protein sequence and whose performance is competitive
        with the available state-of-the-art tools. Results: In this article, we describe
        INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension),
        a web server for the prediction of protein stability changes upon single point
        variation from protein sequence and/or structure. Here, we complement INPS
        with a new predictor (INPS3D) that exploits features derived from protein
        3D structure. INPS3D scores with Pearson''s correlation to experimental ΔΔG
        values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based
        INPS scores slightly lower than the structure-based INPS3D and both on the
        same blind test sets well compare with the state-of-the-art methods.'
      authors:
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 189
      date: '2016-08-15T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS-MD: A web server to predict stability of protein variants from
        sequence and structure'
    note: null
    pmcid: null
    pmid: '27153629'
    type:
    - Primary
    version: null
  relation: []
  toolType: []
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '2.0'
- accessibility: Restricted access
  additionDate: '2025-03-17T14:01:59.601345Z'
  biotoolsCURIE: biotools:exposome_maps
  biotoolsID: exposome_maps
  collectionID: []
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: R.C.H.Vermeulen@uu.nl
    fundrefid: null
    gridid: null
    name: Roel Vermeulen
    note: null
    orcidid: https://orcid.org/0000-0003-4082-8163
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: null
  - email: z.s.r.m.boudewijns@uu.nl
    fundrefid: null
    gridid: null
    name: Zimbo Boudewijns
    note: null
    orcidid: https://orcid.org/0000-0002-9383-9571
    rorid: null
    typeEntity: null
    typeRole:
    - Maintainer
    url: null
  - email: l.q.roga@uu.nl
    fundrefid: null
    gridid: null
    name: Lloyd Roga
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: null
  description: "The Exposome Maps contain geospatially resolved data on environmental\
    \ exposures (Exposome Surfaces), categorized into the following Exposome dimensions:\n\
    Built Environment28 exposures related to:\n•\tGreen space\n•\tBlue space\n•\t\
    Grey space\nFood Environment4 exposures related to:\n•\tFood landscape / healthy\
    \ food index\nPhysico-Chemical Environment99 exposures related to:\n•\tAir pollution\n\
    •\tBiodiversity\n•\tWeather conditions\n•\tLight intensity at night\n•\tNoise\n\
    •\tPesticides\n•\tElectromagnetic fields\nSocial Environment70 exposures related\
    \ to:\n•\tNeighbourhood socio-economic position\n•\tNeighbourhood Demographic\
    \ surfaces and population characteristics\n•\tSocial capital\n•\tSecurity\n•\t\
    Mental health\n•\tProximity to facilities\n•\tEnergy use\n•\tLabour market"
  documentation:
  - note: Description of Environmental variables available through Exposome Maps
    type:
    - User manual
    url: https://surfdrive.surf.nl/files/index.php/s/uqUORDrd428H2F9
  - note: Exposome-NL data platform Data Access and Publication Policy
    type:
    - Terms of use
    url: https://surfdrive.surf.nl/files/index.php/s/fI0Gmuu5O1MJAI9
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://exposome.dataplatform.nl/#/home
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-19T14:50:02.027296Z'
  license: Other
  link:
  - note: Project information page
    type:
    - Other
    url: https://expanseproject.eu/
  maturity: Mature
  name: EXPOSOME maps
  operatingSystem: []
  otherID: []
  owner: Channel2291
  publication:
  - doi: 10.1126/science.aay3164
    metadata:
      abstract: Despite extensive evidence showing that exposure to specific chemicals
        can lead to disease, current research approaches and regulatory policies fail
        to address the chemical complexity of our world. To safeguard current and
        future generations from the increasing number of chemicals polluting our environment,
        a systematic and agnostic approach is needed. The “exposome” concept strives
        to capture the diversity and range of exposures to synthetic chemicals, dietary
        constituents, psychosocial stressors, and physical factors, as well as their
        corresponding biological responses. Technological advances such as high-resolution
        mass spectrometry and network science have allowed us to take the first steps
        toward a comprehensive assessment of the exposome. Given the increased recognition
        of the dominant role that nongenetic factors play in disease, an effort to
        characterize the exposome at a scale comparable to that of the human genome
        is warranted.
      authors:
      - name: Vermeulen R.
      - name: Schymanski E.L.
      - name: Barabasi A.-L.
      - name: Miller G.W.
      citationCount: 587
      date: '2020-01-24T00:00:00Z'
      journal: Science
      title: 'The exposome and health: Where chemistry meets biology'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1097/EE9.0000000000000162
    metadata:
      abstract: 'By 2030, more than 80% of Europe''s population will live in an urban
        environment. The urban exposome, consisting of factors such as where we live
        and work, where and what we eat, our social network, and what chemical and
        physical hazards we are exposed to, provides important targets to improve
        population health. The EXPANSE (EXposome Powered tools for healthy living
        in urbAN SEttings) project will study the impact of the urban exposome on
        the major contributors to Europe''s burden of disease: Cardio-Metabolic and
        Pulmonary Disease. EXPANSE will address one of the most pertinent questions
        for urban planners, policy makers, and European citizens: "How to maximize
        one''s health in a modern urban environment?" EXPANSE will take the next step
        in exposome research by (1) bringing together exposome and health data of
        more than 55 million adult Europeans and OMICS information for more than 2
        million Europeans; (2) perform personalized exposome assessment for 5,000
        individuals in five urban regions; (3) applying ultra-high-resolution mass-spectrometry
        to screen for chemicals in 10,000 blood samples; (4) evaluating the evolution
        of the exposome and health through the life course; and (5) evaluating the
        impact of changes in the urban exposome on the burden of cardiometabolic and
        pulmonary disease. EXPANSE will translate its insights and innovations into
        research and dissemination tools that will be openly accessible via the EXPANSE
        toolbox. By applying innovative ethics-by-design throughout the project, the
        social and ethical acceptability of these tools will be safeguarded. EXPANSE
        is part of the European Human Exposome Network.'
      authors:
      - name: Vlaanderen J.
      - name: De Hoogh K.
      - name: Hoek G.
      - name: Peters A.
      - name: Probst-Hensch N.
      - name: Scalbert A.
      - name: Melen E.
      - name: Tonne C.
      - name: De Wit G.A.
      - name: Chadeau-Hyam M.
      - name: Katsouyanni K.
      - name: Esko T.
      - name: Jongsma K.R.
      - name: Vermeulen R.
      citationCount: 36
      date: '2021-08-01T00:00:00Z'
      journal: Environmental Epidemiology
      title: 'Developing the building blocks to elucidate the impact of the urban
        exposome on cardiometabolic-pulmonary disease: The EU EXPANSE project'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: FAIR data
    uri: http://edamontology.org/topic_4012
  - term: Environmental sciences
    uri: http://edamontology.org/topic_3855
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-17T15:56:34.552871Z'
  biotoolsCURIE: biotools:chewbbaca
  biotoolsID: chewbbaca
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: chewBBACA is a software suite for the creation and evaluation of core
    genome and whole genome MultiLocus Sequence Typing (cg/wgMLST) schemas and results.
  documentation:
  - note: null
    type:
    - User manual
    url: https://chewbbaca.readthedocs.io/en/latest/index.html
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Multilocus sequence typing
      uri: http://edamontology.org/operation_3840
    output: []
  homepage: https://github.com/B-UMMI/chewBBACA
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-03-19T14:47:22.843193Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: chewBBaca
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: EngyNasr
  publication:
  - doi: 10.1099/MGEN.0.000166
    metadata:
      abstract: Gene-by-gene approaches are becoming increasingly popular in bacterial
        genomic epidemiology and outbreak detection. However, there is a lack of open-source
        scalable software for schema definition and allele calling for these methodologies.
        The chewBBACA suite was designed to assist users in the creation and evaluation
        of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent
        allele calling in bacterial strains of interest. chewBBACA performs the schema
        creation and allele calls on complete or draft genomes resulting from de novo
        assemblers. The chewBBACA software uses Python 3.4 or higher and can run on
        a laptop or in high performance clusters making it useful for both small laboratories
        and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA.
      authors:
      - name: Silva M.
      - name: Machado M.P.
      - name: Silva D.N.
      - name: Rossi M.
      - name: Moran-Gilad J.
      - name: Santos S.
      - name: Ramirez M.
      - name: Carrico J.A.
      citationCount: 276
      date: '2018-03-01T00:00:00Z'
      journal: Microbial genomics
      title: 'chewBBACA: A complete suite for gene-by-gene schema creation and strain
        identification'
    note: null
    pmcid: PMC5885018
    pmid: '29543149'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  - Command-line tool
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-17T16:13:12.338060Z'
  biotoolsCURIE: biotools:pymlst
  biotoolsID: pymlst
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: A Python Mlst Local Search Tool. The input can be (i) an assembler-generated
    draft genome, (ii) the direct raw data, or (iii) other genomes stored in the sequence
    database.
  documentation:
  - note: null
    type:
    - User manual
    url: https://pymlst.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Multilocus sequence typing
      uri: http://edamontology.org/operation_3840
    output: []
  homepage: https://github.com/bvalot/pyMLST
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-03-19T14:40:54.280165Z'
  license: null
  link: []
  maturity: null
  name: pyMLST
  operatingSystem: []
  otherID: []
  owner: EngyNasr
  publication:
  - doi: 10.1099/mgen.0.001126
    metadata:
      abstract: Core genome multilocus sequence typing (cgMLST) has gained in popularity
        for bacterial typing since whole-genome sequencing (WGS) has become affordable.
        We introduce here pyMLST, a new complete, stand-alone, free and open source
        pipeline for cgMLST analysis. pyMLST can create or import a core genome database.
        For each gene, the first allele is aligned against the bacterial genome of
        interest using BLAT. Incomplete genes are aligned using MAFT. All data are
        stored in a SQLite database. pyMLST accepts assembly genomes or raw data (with
        the option pyMLST-KMA) as input. To evaluate our new tool, we selected three
        genome collections of major bacterial pathogens (Escherichia coli, Pseudomonas
        aeruginosa and Staphylococcus aureus) and compared them with pyMLST, pyMLST-KMA,
        ChewBBACA, SeqSphere and the variant calling approach. We compared the sensitivity,
        precision and false-positive rate for each method with those of the variant
        calling approach. Minimal spanning trees were generated with each type of
        software to evaluate their interest in the context of a bacterial outbreak.
        We found that pyMLST-KMA is a convenient screening method to avoid assembling
        large bacterial collections. Our data showed that pyMLST (free, open source,
        available in Galaxy and pipeline ready) performed similarly to the commercial
        SeqSphere and performed better than ChewBBACA and pyMLST-KMA.
      authors:
      - name: Biguenet A.
      - name: Bordy A.
      - name: Atchon A.
      - name: Hocquet D.
      - name: Valot B.
      citationCount: 7
      date: '2023-11-01T00:00:00Z'
      journal: Microbial Genomics
      title: 'Introduction and benchmarking of pyMLST: open-source software for assessing
        bacterial clonality using core genome MLST'
    note: null
    pmcid: PMC10711306
    pmid: '37966168'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Library
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-03-17T16:24:23.278579Z'
  biotoolsCURIE: biotools:phinch
  biotoolsID: phinch
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Data visualization framework aimed at promoting novel explorations
    of large biological datasets (microbiomes, metagenomes, etc.).
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://phinch.org/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-19T14:31:46.591915Z'
  license: null
  link: []
  maturity: null
  name: Phinch
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: EngyNasr
  publication:
  - doi: 10.1101/009944
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Desktop application
  - Web application
  topic:
  - term: Data visualisation
    uri: http://edamontology.org/topic_0092
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-18T13:10:47.976399Z'
  biotoolsCURIE: biotools:pisad
  biotoolsID: pisad
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: Phsaed Intraspecies Sample Anomalies Detection tool
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/ZhantianXu/PISAD
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-19T14:27:51.531329Z'
  license: MIT
  link: []
  maturity: null
  name: pisad
  operatingSystem: []
  otherID: []
  owner: zhantianxu
  publication: []
  relation: []
  toolType:
  - Command-line tool
  topic: []
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-17T14:56:35.753220Z'
  biotoolsCURIE: biotools:ddgemb
  biotoolsID: ddgemb
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Maintainer
    - Developer
    - Primary contact
    url: null
  description: Predicting the impact of mutations on protein stability from sequence
    using protein language models
  documentation:
  - note: null
    type:
    - General
    url: https://ddgemb.biocomp.unibo.it/help/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity:
  - Rare Diseases
  - 3D-BioInfo
  elixirNode:
  - Italy
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format: []
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format: []
    note: null
    operation: []
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format: []
  homepage: https://ddgemb.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-17T15:08:51.796427Z'
  license: null
  link: []
  maturity: Mature
  name: DDGemb
  operatingSystem: []
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btaf019
    metadata:
      abstract: 'Motivation: The knowledge of protein stability upon residue variation
        is an important step for functional protein design and for understanding how
        protein variants can promote disease onset. Computational methods are important
        to complement experimental approaches and allow a fast screening of large
        datasets of variations. Results: In this work, we present DDGemb, a novel
        method combining protein language model embeddings and transformer architectures
        to predict protein ΔΔG upon both single- and multi-point variations. DDGemb
        has been trained on a high-quality dataset derived from literature and tested
        on available benchmark datasets of single- and multi-point variations. DDGemb
        performs at the state of the art in both single- and multi-point variations.'
      authors:
      - name: Savojardo C.
      - name: Manfredi M.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 0
      date: '2025-01-01T00:00:00Z'
      journal: Bioinformatics
      title: 'DDGemb: predicting protein stability change upon single- and multi-point
        variations with embeddings and deep learning'
    note: null
    pmcid: PMC11783275
    pmid: '39799516'
    type:
    - Primary
    version: null
  relation: []
  toolType: []
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version:
  - '1'
- accessibility: Open access
  additionDate: '2016-05-04T15:36:53Z'
  biotoolsCURIE: biotools:inps
  biotoolsID: inps
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Division
    typeRole:
    - Provider
    url: http://www.biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    url: null
  - email: piero.fariselli@unito.it
    fundrefid: null
    gridid: null
    name: Piero Fariselli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Predicting the impact of mutations on protein stability from sequence
  documentation:
  - note: null
    type:
    - General
    url: https://inpsmd.biocomp.unibo.it/help
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format: []
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
    operation: []
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format: []
  homepage: https://inpsmd.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-17T14:54:19.762674Z'
  license: null
  link: []
  maturity: Mature
  name: INPS
  operatingSystem: []
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btv291
    metadata:
      abstract: 'Motivation: A tool for reliably predicting the impact of variations
        on protein stability is extremely important for both protein engineering and
        for understanding the effects of Mendelian and somatic mutations in the genome.
        Next Generation Sequencing studies are constantly increasing the number of
        protein sequences. Given the huge disproportion between protein sequences
        and structures, there is a need for tools suited to annotate the effect of
        mutations starting from protein sequence without relying on the structure.
        Here, we describe INPS, a novel approach for annotating the effect of non-synonymous
        mutations on the protein stability from its sequence. INPS is based on SVM
        regression and it is trained to predict the thermodynamic free energy change
        upon single-point variations in protein sequences. Results: We show that INPS
        performs similarly to the state-of-the-art methods based on protein structure
        when tested in cross-validation on a non-redundant dataset. INPS performs
        very well also on a newly generated dataset consisting of a number of variations
        occurring in the tumor suppressor protein p53. Our results suggest that INPS
        is a tool suited for computing the effect of non-synonymous polymorphisms
        on protein stability when the protein structure is not available. We also
        show that INPS predictions are complementary to those of the state-of-the-art,
        structure-based method mCSM. When the two methods are combined, the overall
        prediction on the p53 set scores significantly higher than those of the single
        methods.'
      authors:
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Casadio R.
      citationCount: 111
      date: '2015-02-06T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS: Predicting the impact of non-synonymous variations on protein
        stability from sequence'
    note: null
    pmcid: null
    pmid: '25957347'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btw192
    metadata:
      abstract: 'Motivation: Protein function depends on its structural stability.
        The effects of single point variations on protein stability can elucidate
        the molecular mechanisms of human diseases and help in developing new drugs.
        Recently, we introduced INPS, a method suited to predict the effect of variations
        on protein stability from protein sequence and whose performance is competitive
        with the available state-of-the-art tools. Results: In this article, we describe
        INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension),
        a web server for the prediction of protein stability changes upon single point
        variation from protein sequence and/or structure. Here, we complement INPS
        with a new predictor (INPS3D) that exploits features derived from protein
        3D structure. INPS3D scores with Pearson''s correlation to experimental ΔΔG
        values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based
        INPS scores slightly lower than the structure-based INPS3D and both on the
        same blind test sets well compare with the state-of-the-art methods.'
      authors:
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 189
      date: '2016-08-15T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS-MD: A web server to predict stability of protein variants from
        sequence and structure'
    note: null
    pmcid: null
    pmid: '27153629'
    type:
    - Primary
    version: null
  relation: []
  toolType: []
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2025-03-17T14:11:03.252329Z'
  biotoolsCURIE: biotools:raster_extraction
  biotoolsID: raster_extraction
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: l.q.roga@uu.nl
    fundrefid: null
    gridid: null
    name: Lloyd Roga
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    - Maintainer
    url: null
  description: This Python script provides functions to process raster files in chunks
    and extract values at specified coordinates using multiprocessing. The script
    is designed to handle large raster datasets efficiently by dividing the workload
    across multiple CPU cores.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation: []
    output: []
  homepage: https://github.com/UtrechtUniversity/raster_extraction
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-17T14:13:20.238876Z'
  license: Not licensed
  link: []
  maturity: Mature
  name: Raster extraction
  operatingSystem: []
  otherID: []
  owner: Channel2291
  publication: []
  relation:
  - biotoolsID: exposome_maps
    type: uses
  toolType: []
  topic:
  - term: Environmental sciences
    uri: http://edamontology.org/topic_3855
  - term: Data management
    uri: http://edamontology.org/topic_3071
  validated: 0
  version: []
- accessibility: Open access (with restrictions)
  additionDate: '2025-03-16T20:15:23.278615Z'
  biotoolsCURIE: biotools:hdn
  biotoolsID: hdn
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit: []
  description: The Health Data Nexus is T-CAIREM's unique online platform for health
    data sharing & analytics that emphasizes patient privacy and security, provides
    transparent and speedy access, and simplifies data discovery and analysis.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation: []
    output: []
  homepage: https://healthdatanexus.ai/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-16T20:26:12.603608Z'
  license: MIT
  link:
  - note: null
    type:
    - Other
    url: https://tcairem.utoronto.ca/infrastructure-overview
  - note: null
    type:
    - Repository
    url: https://github.com/T-CAIREM/physionet-build
  maturity: Mature
  name: Health Data Nexus
  operatingSystem: []
  otherID: []
  owner: rutvikrj26
  publication: []
  relation: []
  toolType: []
  topic:
  - term: Data management
    uri: http://edamontology.org/topic_3071
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version: []
- accessibility: null
  additionDate: '2025-03-13T03:39:14.949353Z'
  biotoolsCURIE: biotools:emmtyper
  biotoolsID: emmtyper
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: "emmtyper is a command line tool for emm-typing of Streptococcus pyogenes\
    \ using a de novo or complete assembly.\n\nBy default, we use the U.S. Centers\
    \ for Disease Control and Prevention trimmed emm subtype database, which can be\
    \ found here (https://www2a.cdc.gov/ncidod/biotech/strepblast.asp). The database\
    \ is curated by Dr. Velusamy Srinivasan. \n\nInner workings\nThe difficulty in\
    \ performing M-typing is that there is a single gene of interest (emm), but two\
    \ other homologue genes (enn and mrp), often referred to as emm-like. The homologue\
    \ genes may or may not occur in the isolate of interest. When performing emm-typing\
    \ from an assembly, we can distinguish betweeen one or more clusters of matches\
    \ on the contigs. The best match for each of the clusters identified is then parsed\
    \ from the BLAST results. Where possible, we try to distinguish between matches\
    \ to the emm gene, and matches to one of the emm-like genes."
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/MDU-PHL/emmtyper
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-13T03:45:42.872847Z'
  license: null
  link: []
  maturity: null
  name: emmtyper
  operatingSystem: []
  otherID: []
  owner: maxlcummins
  publication: []
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - 0.2.0
- accessibility: Open access
  additionDate: '2025-03-12T15:38:32.218141Z'
  biotoolsCURIE: biotools:gtestimate
  biotoolsID: gtestimate
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: GTestimate is a scRNA-seq normalization method. In contrast to other
    methods it uses the Simple Good-Turing estimator for the per cell relative gene
    expression estimation.
  documentation:
  - note: null
    type:
    - General
    url: https://github.com/Martin-Fahrenberger/GTestimate
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: GTestimate()
    input:
    - data:
        term: Count matrix
        uri: http://edamontology.org/data_3917
      format: []
    note: GTestimate is provided as an R-package containing the GTestimate() function.
    operation:
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output:
    - data:
        term: Gene expression matrix
        uri: http://edamontology.org/data_3112
      format: []
  homepage: https://github.com/Martin-Fahrenberger/GTestimate
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-03-12T15:38:32.221353Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Issue tracker
    - Repository
    url: https://github.com/Martin-Fahrenberger/GTestimate
  maturity: Mature
  name: GTestimate
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: martin_fahrenberger
  publication:
  - doi: 10.1101/2024.07.02.601501
    metadata: null
    note: preprint version 2
    pmcid: null
    pmid: null
    type:
    - Method
    version: '2'
  relation: []
  toolType:
  - Library
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2025-03-11T10:18:23.285192Z'
  biotoolsCURIE: biotools:ms2lda_2.0
  biotoolsID: ms2lda_2.0
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Updated version of MS2LDA for Substructure finding in MS/MS data
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function: []
  homepage: https://github.com/vdhooftcompmet/MS2LDA
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2025-03-11T10:18:23.287500Z'
  license: Freeware
  link: []
  maturity: Emerging
  name: MS2LDA 2.0
  operatingSystem: []
  otherID: []
  owner: rosina_to
  publication: []
  relation: []
  toolType:
  - Workflow
  topic:
  - term: Analytical chemistry
    uri: http://edamontology.org/topic_3370
  validated: 0
  version:
  - 2.0.0
  - '2.0'
- accessibility: Open access
  additionDate: '2017-02-10T14:14:47Z'
  biotoolsCURIE: biotools:act
  biotoolsID: act
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: D.R.Westhead@leeds.ac.uk
    fundrefid: null
    gridid: null
    name: David R Westhead
    note: null
    orcidid: https://orcid.org/0000-0002-0519-3820
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://biologicalsciences.leeds.ac.uk/molecular-and-cellular-biology/staff/154/professor-david-r-westhead
  - email: imichalop@bioacademy.gr
    fundrefid: null
    gridid: null
    name: Ioannis Michalopoulos
    note: null
    orcidid: https://orcid.org/0000-0001-8991-8712
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://www.michalopoulos.net/
  description: The Arabidopsis Coexpression Tool (ACT) is based on the coexpression
    analysis of 21273 Arabidopsis thaliana genes from gene pair correlation data of
    3500 Affymetrix Arabidopsis ATH1 Genome Array Chip microarray samples.
  documentation:
  - note: null
    type:
    - FAQ
    url: https://www.michalopoulos.net/act2.6/help.php
  download: []
  editPermission:
    authors:
    - imichalop@bioacademy.gr
    type: private
  elixirCommunity:
  - Plant Sciences
  elixirNode:
  - Greece
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Gene identifier
        uri: http://edamontology.org/data_1025
      format: []
    note: null
    operation:
    - term: Dendrogram visualisation
      uri: http://edamontology.org/operation_2938
    output:
    - data:
        term: Gene tree
        uri: http://edamontology.org/data_3271
      format: []
  homepage: https://www.michalopoulos.net/act/
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-10T11:30:02.403040Z'
  license: null
  link: []
  maturity: Mature
  name: Arabidopsis Co-expression Tool (ACT)
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: imichalop@bioacademy.gr
  publication:
  - doi: 10.3390/genes16030258
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: 10.1016/j.xpro.2022.101208
    metadata:
      abstract: Coexpressed genes tend to participate in related biological processes.
        Gene coexpression analysis allows the discovery of functional gene partners
        or the assignment of biological roles to genes of unknown function. In this
        protocol, we describe the steps necessary to create a gene coexpression tree
        for Arabidopsis thaliana, using publicly available Affymetrix CEL microarray
        data. Because the computational analysis described here is highly dependent
        on sample quality, we detail an automatic quality control approach. For complete
        details on the use and execution of this protocol, please refer to Zogopoulos
        et al. (2021).
      authors:
      - name: Zogopoulos V.L.
      - name: Malatras A.
      - name: Michalopoulos I.
      citationCount: 5
      date: '2022-03-18T00:00:00Z'
      journal: STAR Protocols
      title: Gene coexpression analysis in Arabidopsis thaliana based on public microarray
        data
    note: null
    pmcid: PMC8885756
    pmid: '35243384'
    type:
    - Method
    version: null
  - doi: 10.3390/biology11071019
    metadata:
      abstract: Gene coexpression analysis constitutes a widely used practice for
        gene partner identification and gene function prediction, consisting of many
        intricate procedures. The analysis begins with the collection of primary transcriptomic
        data and their preprocessing, continues with the calculation of the similarity
        between genes based on their expression values in the selected sample dataset
        and results in the construction and visualisation of a gene coexpression network
        (GCN) and its evaluation using biological term enrichment analysis. As gene
        coexpression analysis has been studied ex-tensively, we present most parts
        of the methodology in a clear manner and the reasoning behind the selection
        of some of the techniques. In this review, we offer a comprehensive and comprehensi-ble
        account of the steps required for performing a complete gene coexpression
        analysis in eukary-otic organisms. We comment on the use of RNA‐Seq vs. microarrays,
        as well as the best practices for GCN construction. Furthermore, we recount
        the most popular webtools and standalone applications performing gene coexpression
        analysis, with details on their methods, features and outputs.
      authors:
      - name: Zogopoulos V.L.
      - name: Saxami G.
      - name: Malatras A.
      - name: Papadopoulos K.
      - name: Tsotra I.
      - name: Iconomidou V.A.
      - name: Michalopoulos I.
      citationCount: 9
      date: '2022-07-01T00:00:00Z'
      journal: Biology
      title: Approaches in Gene Coexpression Analysis in Eukaryotes
    note: null
    pmcid: PMC9312353
    pmid: '36101400'
    type:
    - Review
    version: null
  - doi: 10.1093/nar/gkl204
    metadata:
      abstract: The Arabidopsis Co-expression Tool, ACT, ranks the genes across a
        large microarray dataset according to how closely their expression follows
        the expression of a query gene. A database stores pre-calculated co-expression
        results for ∼21 800 genes based on data from over 300 arrays. These results
        can be corroborated by calculation of co-expression results for user-defined
        sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique
        Finder (CF) identifies groups of genes which are consistently co-expressed
        with each other across a user-defined co-expression list. The parameters can
        be altered easily to adjust cluster size and the output examined for optimal
        inclusion of genes with known biological roles. Alternatively, a Scatter Plot
        tool displays the correlation coefficients for all genes against two user-selected
        queries on a scatter plot which can be useful for visual identification of
        clusters of genes with similar r-values. User-input groups of genes can be
        highlighted on the scatter plots. Inclusion of genes with known biology in
        sets of genes identified using CF and Scatter Plot tools allows inferences
        to be made about the roles of the other genes in the set and both tools can
        therefore be used to generate short lists of genes for further characterization.
        ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT. © The Author 2006.
        Published by Oxford University Press. All rights reserved.
      authors:
      - name: Manfield I.W.
      - name: Jen C.-H.
      - name: Pinney J.W.
      - name: Michalopoulos I.
      - name: Bradford J.R.
      - name: Gilmartin P.M.
      - name: Westhead D.R.
      citationCount: 130
      date: '2006-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'Arabidopsis Co-expression Tool (ACT): Web server tools for microarray-based
        gene expression analysis'
    note: null
    pmcid: PMC1538833
    pmid: '16845059'
    type:
    - Other
    version: null
  - doi: 10.1111/j.1365-313x.2006.02681.x
    metadata:
      abstract: 'We present a new WWW-based tool for plant gene analysis, the Arabidopsis
        Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray
        data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression
        analysis tool allows users to identify genes whose expression patterns are
        correlated across selected experiments or the complete data set. Results are
        accompanied by estimates of the statistical significance of the correlation
        relationships, expressed as probability (P) and expectation (E) values. Additionally,
        highly ranked genes on a correlation list can be examined using the novel
        CLIQUE FINDER tool to determine the sets of genes most likely to be regulated
        in a similar manner. In combination, these tools offer three levels of analysis:
        creation of correlation lists of co-expressed genes, refinement of these lists
        using two-dimensional scatter plots, and dissection into cliques of co-regulated
        genes. We illustrate the applications of the software by analysing genes encoding
        functionally related proteins, as well as pathways involved in plant responses
        to environmental stimuli. These analyses demonstrate novel biological relationships
        underlying the observed gene co-expression patterns. To demonstrate the ability
        of the software to develop testable hypotheses on gene function within a defined
        biological process we have used the example of cell wall biosynthesis genes.
        The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/.
        © 2006 The Authors.'
      authors:
      - name: Jen C.-H.
      - name: Manfield I.W.
      - name: Michalopoulos I.
      - name: Pinney J.W.
      - name: Willats W.G.T.
      - name: Gilmartin P.M.
      - name: Westhead D.R.
      citationCount: 65
      date: '2006-04-01T00:00:00Z'
      journal: Plant Journal
      title: 'The Arabidopsis co-expression tool (ACT): A WWW-based tool and database
        for microarray-based gene expression analysis'
    note: null
    pmcid: null
    pmid: '16623895'
    type:
    - Other
    version: null
  - doi: 10.1016/j.isci.2021.102848
    metadata:
      abstract: Gene coexpression analysis refers to the discovery of sets of genes
        which exhibit similar expression patterns across multiple transcriptomic data
        sets, such as microarray experiment data of public repositories. Arabidopsis
        Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis
        thaliana, identifies genes which are correlated to a driver gene. Primary
        microarray data from ATH1 Affymetrix platform were processed with Single-Channel
        Array Normalization algorithm and combined to produce a coexpression tree
        which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades
        of coexpressed genes, as well as to perform gene set enrichment analysis,
        being unique in revealing enriched transcription factors targeting coexpressed
        genes. ACT offers a simple and user-friendly interface producing working hypotheses
        which can be experimentally verified for the discovery of gene partnership,
        pathway membership, and transcriptional regulation. ACT analyses have been
        successful in identifying not only genes with coordinated ubiquitous expressions
        but also genes with tissue-specific expressions.
      authors:
      - name: Zogopoulos V.L.
      - name: Saxami G.
      - name: Malatras A.
      - name: Angelopoulou A.
      - name: Jen C.-H.
      - name: Duddy W.J.
      - name: Daras G.
      - name: Hatzopoulos P.
      - name: Westhead D.R.
      - name: Michalopoulos I.
      citationCount: 14
      date: '2021-08-20T00:00:00Z'
      journal: iScience
      title: 'Arabidopsis Coexpression Tool: a tool for gene coexpression analysis
        in Arabidopsis thaliana'
    note: null
    pmcid: PMC8334378
    pmid: '34381973'
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    version: null
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  description: PEP-FOLD4 is a fast and accurate structure prediction tool for peptides
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  - note: null
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    metadata:
      abstract: Accurate and fast structure prediction of peptides of less 40 amino
        acids in aqueous solution has many biological applications, but their conformations
        are pH-and salt concentration-dependent. In this work, we present PEP-FOLD4
        which goes one step beyond many machine-learning approaches, such as AlphaFold2,
        TrRosetta and RaptorX. Adding the Debye-Hueckel formalism for charged-charged
        side chain interactions to a Mie formalism for all intramolecular (backbone
        and side chain) interactions, PEP-FOLD4, based on a coarse-grained representation
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        peptides, but displays significant improvements for poly-charged peptides.
        PEP-FOLD4 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4.
        This server is free and there is no login requirement.
      authors:
      - name: Rey J.
      - name: Murail S.
      - name: De Vries S.
      - name: Derreumaux P.
      - name: Tuffery P.
      citationCount: 39
      date: '2023-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'PEP-FOLD4: A pH-dependent force field for peptide structure prediction
        in aqueous solution'
    note: null
    pmcid: PMC10320157
    pmid: '37166962'
    type:
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    version: null
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  - term: Computational chemistry
    uri: http://edamontology.org/topic_3332
  validated: 0
  version: []
- accessibility: null
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  biotoolsCURIE: biotools:vcztools
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  credit: []
  description: Partial reimplementation of bcftools for VCF Zarr.
  documentation:
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    type:
    - General
    url: https://sgkit-dev.github.io/vcztools
  download:
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    url: https://pypi.org/project/vcztools/
    version: null
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    metadata: null
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  biotoolsCURIE: biotools:bio2zarr
  biotoolsID: bio2zarr
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  description: Converts bioinformatics formats to Zarr.
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    type:
    - General
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  download:
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    metadata: null
    note: null
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  validated: 0
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- accessibility: Open access
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  biotoolsCURIE: biotools:lipidone_2.2
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    fundrefid: null
    gridid: null
    name: Roberto Maria Pellegrino
    note: null
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    rorid: null
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    url: https://lipidone.eu/
  description: User-friendly lipidomic data analysis tool for a deeper interpretation
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  license: Freeware
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  publication:
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    metadata:
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        for the analysis of lipidomic data. It facilitates the exploration of the
        three structural levels of lipids: classes, molecular species, and lipid building
        blocks (acyl, alkyl, or alkenes chains). The tool''s flexibility empowers
        users to seamlessly include or exclude experimental groups and lipid classes
        at any stage of the analysis. LipidOne 2.0 offers a range of mono- and multivariate
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        diverse statistical parameters. LipidOne 2.0 incorporates Lipid Pathway analysis
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      - name: Mancini D.F.
      - name: Buratta S.
      - name: Calzoni E.
      - name: Giacomo D.D.
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      - name: Martino S.
      - name: Urbanelli L.
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      journal: Current Protocols
      title: 'LipidOne 2.0: A Web Tool for Discovering Biological Meanings Hidden
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    note: null
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    type: []
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  relation:
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  - term: Data mining
    uri: http://edamontology.org/topic_3473
  - term: Biomarkers
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  biotoolsCURIE: biotools:autobigs.cli
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  collectionID: []
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  cost: Free of charge
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  description: A command-line interface (CLI) based program that allows quickly batched
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    Institut Pasteur and PubMLST.
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  biotoolsCURIE: biotools:coxmos
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  description: The Coxmos (Cox MultiBlock Survival) R package is an end-to-end pipeline
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    classical methods and adding new ones based on sPLS technologies. Furthermore,
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    survival accuracy.
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  description: MORE (Multi-Omics REgulation) is an R package for the application of
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  - doi: 10.1101/2024.01.25.577162
    metadata: null
    note: This is a preprint available at bioRxiv. When the paper is published the
      link wil be updated.
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  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:
  - 1.0.0
- accessibility: Open access
  additionDate: '2025-03-03T15:06:10.867996Z'
  biotoolsCURIE: biotools:multipower
  biotoolsID: multipower
  collectionID: []
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: sotacam@eio.upv.es
    fundrefid: null
    gridid: null
    name: Sonia Tarazona
    note: null
    orcidid: https://orcid.org/0000-0001-5346-1407
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Maintainer
    url: null
  description: The MultiPower R method performs statistical power studies for multi-omics
    experiments, and is designed to assist users in experimental design as well as
    in the evaluation of already-generated multi-omics datasets.
  documentation:
  - note: null
    type:
    - User manual
    url: https://github.com/ConesaLab/MultiPower/blob/master/MultiPowerUsersGuide_v2.pdf
  download: []
  editPermission:
    authors: []
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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: '2025-03-03T08:03:47.168169Z'
  biotoolsCURIE: biotools:attune
  biotoolsID: attune
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Attune offers a versatile framework for integrating gene expression
    and chromatin accessibility, enabling the inference of regulatory mechanisms and
    the prediction of gene expression from cross-modal data.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
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  elixirNode: []
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  elixir_badge: 0
  function: []
  homepage: https://github.com/melobio/Attune
  homepage_status: 0
  language: []
  lastUpdate: '2025-03-03T08:08:06.816312Z'
  license: null
  link: []
  maturity: null
  name: Attune
  operatingSystem: []
  otherID: []
  owner: admin
  publication: []
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - '1.0'
- accessibility: null
  additionDate: '2016-04-15T11:52:42Z'
  biotoolsCURIE: biotools:comparems2
  biotoolsID: comparems2
  collectionID:
  - ms-utils
  - Proteomics
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: lumc.nl
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Support
    url: https://www.lumc.nl
  - email: magnus.palmblad@gmail.com
    fundrefid: null
    gridid: null
    name: Magnus Palmblad
    note: null
    orcidid: http://orcid.org/0000-0002-5865-8994
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    - Documentor
    url: https://github.com/magnuspalmblad
  - email: null
    fundrefid: null
    gridid: null
    name: Rob Marissen
    note: null
    orcidid: https://orcid.org/0000-0002-1220-9173
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://github.com/524D
  description: compareMS2 is a tool for comparing sets of (tandem) mass spectra for
    clustering samples, molecular phylogenetics, identification of biological species
    or tissues, and quality control. compareMS2 currently consumes Mascot Generic
    Format, or MGF, and produces output in a variety of common image and distance
    matrix formats.
  documentation:
  - note: null
    type:
    - General
    - Command-line options
    url: http://www.ms-utils.org/compareMS2.html
  - note: null
    type:
    - General
    - User manual
    - Command-line options
    - Installation instructions
    url: https://github.com/524D/compareMS2
  download:
  - note: null
    type: Source code
    url: http://www.ms-utils.org/compareMS2.c
    version: '1.0'
  - note: null
    type: Binaries
    url: http://www.ms-utils.org/compareMS2.html
    version: '1.0'
  - note: null
    type: Source code
    url: http://www.ms-utils.org/compareMS2.c
    version: '1.0'
  - note: null
    type: Source code
    url: https://github.com/524D/compareMS2/tree/main/src
    version: '2.0'
  - note: null
    type: Binaries
    url: https://github.com/524D/compareMS2/tree/main
    version: '2.0'
  editPermission:
    authors:
    - proteomics.bio.tools
    type: group
  elixirCommunity:
  - Proteomics
  elixirNode:
  - Netherlands
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: MGF
        uri: http://edamontology.org/format_3651
    note: null
    operation:
    - term: Comparison
      uri: http://edamontology.org/operation_2424
    - term: Phylogenetic tree visualisation
      uri: http://edamontology.org/operation_0567
    output:
    - data:
        term: Species tree
        uri: http://edamontology.org/data_3272
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Distance matrix
        uri: http://edamontology.org/data_2855
      format:
      - term: mega
        uri: http://edamontology.org/format_1991
      - term: Nexus format
        uri: http://edamontology.org/format_1912
      - term: newick
        uri: http://edamontology.org/format_1910
  homepage: https://github.com/524D/compareMS2
  homepage_status: 0
  language:
  - C
  - JavaScript
  lastUpdate: '2025-02-28T16:15:29.918624Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/524D/compareMS2
  - note: null
    type:
    - Software catalogue
    url: https://www.ms-utils.org/compareMS2.html
  - note: null
    type:
    - Software catalogue
    url: https://research-software-directory.org/software/comparems2
  maturity: Mature
  name: compareMS2
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: n.m.palmblad@lumc.nl
  publication:
  - doi: 10.1002/rcm.6162
    metadata:
      abstract: 'Rationale: Molecular phylogenetics is the study of evolution and
        relatedness of organisms or genes. Mass spectrometry is used routinely for
        bacterial identification and has also been used for phylogenetic analysis,
        for instance from bone material. Unfortunately, only a small fraction of the
        acquired tandem mass spectra allow direct interpretation. Methods: We describe
        a new algorithm and software for molecular phylogenetics using pairwise comparisons
        of tandem mass spectra from enzymatically digested proteins. The spectra need
        not be annotated and all acquired data is used in the analysis. To demonstrate
        the method, we analyzed tryptic digests of sera from four great apes and two
        other primates. Results: The distribution of spectra dot products for thousands
        of tandem mass spectra collected from two samples provides a measure on the
        fraction of shared peptides between the two samples. When inverted, this becomes
        a distance metric. By pairwise comparison between species and averaging over
        four individuals per species, it was possible to reconstruct the unique correct
        phylogenetic tree for the great apes and other primates. Conclusions: The
        new method described here has several attractive features compared with existing
        methods, among them simplicity, the unbiased use of all acquired data rather
        than a small subset of spectra, and the potential use of heavily degraded
        proteins or proteins with a priori unknown modifications. © 2012 John Wiley
        & Sons, Ltd.'
      authors:
      - name: Palmblad M.
      - name: Deelder A.M.
      citationCount: 30
      date: '2012-04-15T00:00:00Z'
      journal: Rapid Communications in Mass Spectrometry
      title: Molecular phylogenetics by direct comparison of tandem mass spectra
    note: null
    pmcid: null
    pmid: '22368051'
    type:
    - Primary
    version: '1.0'
  - doi: 10.1021/acs.jproteome.2c00457
    metadata:
      abstract: It has long been known that biological species can be identified from
        mass spectrometry data alone. Ten years ago, we described a method and software
        tool, compareMS2, for calculating a distance between sets of tandem mass spectra,
        as routinely collected in proteomics. This method has seen use in species
        identification and mixture characterization in food and feed products, as
        well as other applications. Here, we present the first major update of this
        software, including a new metric, a graphical user interface and additional
        functionality. The data have been deposited to ProteomeXchange with dataset
        identifier PXD034932.
      authors:
      - name: Marissen R.
      - name: Varunjikar M.S.
      - name: Laros J.F.J.
      - name: Rasinger J.D.
      - name: Neely B.A.
      - name: Palmblad M.
      citationCount: 7
      date: '2023-02-03T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry
        Datasets'
    note: null
    pmcid: PMC9903320
    pmid: '36173614'
    type:
    - Primary
    version: '2.0'
  - doi: 10.1021/acs.jproteome.1c00528
    metadata:
      abstract: Science is full of overlooked and undervalued research waiting to
        be rediscovered. Proteomics is no exception. In this perspective, we follow
        the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing
        tryptic peptides across animal species. This pioneering work directly led
        to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics.
        In the decades following, proteins continued to provide essential clues on
        evolutionary history. While technology has continued to improve, contemporary
        proteomics has strayed from this larger biological context, rarely comparing
        species or asking how protein structure, function, and interactions have evolved.
        Here we recombine proteomics with molecular phylogenetics, highlighting the
        value of framing proteomic results in a larger biological context and how
        almost forgotten research, though technologically surpassed, can still generate
        new ideas and illuminate our work from a different perspective. Though it
        is infeasible to read all research published on a large topic, looking up
        older papers can be surprisingly rewarding when rediscovering a "gem"at the
        end of a long citation chain, aided by digital collections and perpetually
        helpful librarians. Proper literature study reduces unnecessary repetition
        and allows research to be more insightful and impactful by truly standing
        on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/)
        as dataset MSV000087993.
      authors:
      - name: Neely B.A.
      - name: Palmblad M.
      citationCount: 1
      date: '2021-10-01T00:00:00Z'
      journal: Journal of Proteome Research
      title: 'Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics
        Experiments in the Light of Proteomics'
    note: null
    pmcid: PMC8491155
    pmid: '34523928'
    type:
    - Review
    version: '2.0'
  relation: []
  toolType:
  - Command-line tool
  - Desktop application
  topic:
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  validated: 1
  version:
  - '1.0'
  - '2.0'
- accessibility: null
  additionDate: '2024-02-02T14:42:46.030561Z'
  biotoolsCURIE: biotools:hictk
  biotoolsID: hictk
  collectionID: []
  confidence_flag: null
  cost: null
  credit: []
  description: Blazing fast toolkit to work with .hic and .cool files
  documentation:
  - note: null
    type:
    - User manual
    url: https://hictk.readthedocs.io/en/stable/index.html
  download:
  - note: null
    type: Downloads page
    url: https://github.com/paulsengroup/hictk/releases
    version: null
  - note: null
    type: Binaries
    url: https://anaconda.org/bioconda/hictk
    version: null
  - note: null
    type: Container file
    url: https://github.com/paulsengroup/hictk/pkgs/container/hictk
    version: null
  editPermission:
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    type: private
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  homepage: https://github.com/paulsengroup/hictk
  homepage_status: 0
  language: []
  lastUpdate: '2025-02-27T17:22:32.508665Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/paulsengroup/hictk
  - note: null
    type:
    - Issue tracker
    url: https://github.com/paulsengroup/hictk/issues
  - note: null
    type:
    - Helpdesk
    url: https://github.com/paulsengroup/hictk/discussions
  maturity: Mature
  name: hictk
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: robomics
  publication:
  - doi: 10.1093/bioinformatics/btae408
    metadata:
      abstract: 'Motivation: Hi-C is gaining prominence as a method for mapping genome
        organization. With declining sequencing costs and a growing demand for higher-resolution
        data, efficient tools for processing Hi-C datasets at different resolutions
        are crucial. Over the past decade, the. hic and Cooler file formats have become
        the de-facto standard to store interaction matrices produced by Hi-C experiments
        in binary format. Interoperability issues make it unnecessarily difficult
        to convert between the two formats and to develop applications that can process
        each format natively. Results: We developed hictk, a toolkit that can transparently
        operate on. hic and. cool files with excellent performance. The toolkit is
        written in C++ and consists of a C++ library with Python and R bindings as
        well as CLI tools to perform common operations directly from the shell, including
        converting between. hic and. mcool formats. We benchmark the performance of
        hictk and compare it with other popular tools and libraries. We conclude that
        hictk significantly outperforms existing tools while providing the flexibility
        of natively working with both file formats without code duplication.'
      authors:
      - name: Rossini R.
      - name: Paulsen J.
      citationCount: 0
      date: '2024-07-01T00:00:00Z'
      journal: Bioinformatics
      title: 'hictk: blazing fast toolkit to work with. hic and. cool files'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: hictkpy
    type: usedBy
  - biotoolsID: hictkr
    type: usedBy
  toolType:
  - Command-line tool
  topic:
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version:
  - 0.0.1
  - 0.0.2
  - 0.0.3
  - 0.0.4
  - 0.0.5
  - 0.0.6
  - 0.0.7
  - 0.0.8
  - 0.0.9
  - 0.0.10
  - 0.0.11
  - 0.0.12
  - 1.0.0
  - 2.0.0
  - 2.0.1
  - 2.0.2
- accessibility: Open access
  additionDate: '2021-01-18T09:08:27Z'
  biotoolsCURIE: biotools:metidfyr
  biotoolsID: metidfyr
  collectionID: []
  confidence_flag: tool
  cost: null
  credit: []
  description: 'Open-Source R Package to Decipher Small-Molecule Drugs Metabolism
    Through High Resolution Mass Spectrometry.


    MetIDfyR is an open-source, cross-platform and versatile R script to predict and
    detect metabolites in mass spectrometry data (mzML) based on the raw formula of
    the drug of interest.'
  documentation:
  - note: null
    type:
    - Quick start guide
    - Installation instructions
    - Citation instructions
    url: https://github.com/agnesbrnb/MetIDfyR
  download: []
  editPermission:
    authors: []
    type: private
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Mass spectrometry data
        uri: http://edamontology.org/data_2536
      format:
      - term: mzML
        uri: http://edamontology.org/format_3244
    - data:
        term: Chemical formula
        uri: http://edamontology.org/data_0846
      format: []
    note: null
    operation:
    - term: Natural product identification
      uri: http://edamontology.org/operation_3803
    - term: Phasing
      uri: http://edamontology.org/operation_3454
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: SVG
        uri: http://edamontology.org/format_3604
  homepage: https://github.com/agnesbrnb/MetIDfyR
  homepage_status: 0
  language:
  - R
  lastUpdate: '2025-02-27T15:46:01.807140Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: MetIDfyR
  operatingSystem: []
  otherID: []
  owner: agnesbarnabe
  publication:
  - doi: 10.1021/acs.analchem.0c02281
    metadata:
      abstract: With recent advances in analytical chemistry, liquid chromatography
        high-resolution tandem mass spectrometry (LC-HRMS/MS) has become an essential
        tool for metabolite discovery and detection. Even if most of the common drug
        transformations have already been extensively described, manual search of
        drug metabolites in LC-HRMS/MS datasets is still a common practice in toxicology
        laboratories, complicating metabolite discovery. Furthermore, the availability
        of free open-source software for metabolite discovery is still limited. In
        this article, we present MetIDfyR, an open-source and cross-platform R package
        for in silico drug phase I/II biotransformation prediction and mass-spectrometric
        data mining. MetIDfyR has proven its efficacy for advanced metabolite identification
        in semi-complex and complex mixtures in in vitro or in vivo drug studies and
        is freely available at github.com/agnesblch/MetIDfyR.
      authors:
      - name: Delcourt V.
      - name: Barnabe A.
      - name: Loup B.
      - name: Garcia P.
      - name: Andre F.
      - name: Chabot B.
      - name: Trevisiol S.
      - name: Moulard Y.
      - name: Popot M.-A.
      - name: Bailly-Chouriberry L.
      citationCount: 10
      date: '2020-10-06T00:00:00Z'
      journal: Analytical Chemistry
      title: 'MetIDfyR: An Open-Source R Package to Decipher Small-Molecule Drug Metabolism
        through High-Resolution Mass Spectrometry'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Script
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Proteomics experiment
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