count: 18836
list:
- accessibility: null
  additionDate: '2023-06-21T16:32:23.746446Z'
  biotoolsCURIE: biotools:massivefold
  biotoolsID: massivefold
  collectionID: []
  community: null
  confidence_flag: null
  cost: null
  credit: []
  description: MassiveFold allows massive sampling with AlphaFold or ColabFold, splitting
    the computing on CPUs and many GPUs.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein structure prediction
      uri: http://edamontology.org/operation_0474
    output: []
  - cmd: null
    input: []
    note: null
    operation:
    - term: Multiple sequence alignment
      uri: http://edamontology.org/operation_0492
    output: []
  homepage: https://github.com/GBLille/MassiveFold
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-22T13:17:03.098045Z'
  license: null
  link: []
  maturity: null
  name: MassiveFold
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: gbrysbaert
  publication: []
  relation: []
  toolType: []
  topic:
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  validated: 0
  version:
  - '1.2'
- accessibility: null
  additionDate: '2017-07-20T18:20:28Z'
  biotoolsCURIE: biotools:bridgedb_api
  biotoolsID: bridgedb_api
  collectionID:
  - Rare Disease
  - BridgeDb
  community: null
  confidence_flag: null
  cost: null
  credit:
  - email: bridgedb-discuss@googlegroups.com
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: 'BridgeDb is a framework for finding and mapping equivalent database
    identifiers. It has many facets: it is both a framework, live services, and are
    identifier mapping files for genes, proteins, and metabolites.'
  documentation:
  - note: null
    type:
    - General
    url: https://www.bridgedb.org/
  download: []
  editPermission:
    authors:
    - ChrisEvelo
    - LanfearJ
    type: group
  elixirCommunity: []
  elixirNode:
  - Netherlands
  elixirPlatform:
  - Interoperability
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    note: Returns a list of xrefs that map to a given identifier, and data source.
      Optionally restrict results to a given data source.
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format: []
  - cmd: null
    input:
    - data:
        term: Species name
        uri: http://edamontology.org/data_1045
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    - data:
        term: Identifier
        uri: http://edamontology.org/data_0842
      format: []
    note: Returns `true` or `false` based on whether or not an xref exists in the
      database given an identifier, data source, and organism.
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output:
    - data:
        term: Data
        uri: http://edamontology.org/data_0006
      format: []
  homepage: http://www.bridgedb.org/
  homepage_status: 0
  language:
  - Java
  lastUpdate: '2024-04-22T13:11:17.887951Z'
  license: null
  link: []
  maturity: null
  name: BridgeDb API
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: egonw
  publication:
  - doi: null
    metadata:
      abstract: 'Background: Many complementary solutions are available for the identifier
        mapping problem. This creates an opportunity for bioinformatics tool developers.
        Tools can be made to flexibly support multiple mapping services or mapping
        services could be combined to get broader coverage. This approach requires
        an interface layer between tools and mapping services.Results: Here we present
        BridgeDb, a software framework for gene, protein and metabolite identifier
        mapping. This framework provides a standardized interface layer through which
        bioinformatics tools can be connected to different identifier mapping services.
        This approach makes it easier for tool developers to support identifier mapping.
        Mapping services can be combined or merged to support multi-omics experiments
        or to integrate custom microarray annotations. BridgeDb provides its own ready-to-go
        mapping services, both in webservice and local database forms. However, the
        framework is intended for customization and adaptation to any identifier mapping
        service. BridgeDb has already been integrated into several bioinformatics
        applications.Conclusion: By uncoupling bioinformatics tools from mapping services,
        BridgeDb improves capability and flexibility of those tools. All described
        software is open source and available at http://www.bridgedb.org. © 2010 van
        Iersel et al; licensee BioMed Central Ltd.'
      authors:
      - name: van Iersel M.P.
      - name: Pico A.R.
      - name: Kelder T.
      - name: Gao J.
      - name: Ho I.
      - name: Hanspers K.
      - name: Conklin B.R.
      - name: Evelo C.T.
      citationCount: 123
      date: '2010-01-04T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'The BridgeDb framework: Standardized access to gene, protein and metabolite
        identifier mapping services'
    note: null
    pmcid: null
    pmid: '20047655'
    type: []
    version: null
  relation: []
  toolType:
  - Web API
  topic:
  - term: Data identity and mapping
    uri: http://edamontology.org/topic_3345
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2024-04-19T11:31:13.116284Z'
  biotoolsCURIE: biotools:lion_localizer
  biotoolsID: lion_localizer
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: roca@illinois.edu
    fundrefid: null
    gridid: null
    name: Alfred L Roca
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Wesley C Au
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Software tool for inferring the provenance of lions (Panthera leo)
    using mitochondrial DNA.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence trimming
      uri: http://edamontology.org/operation_3192
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Haplotype mapping
      uri: http://edamontology.org/operation_0487
    output: []
  homepage: https://lionlocalizer.org
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-19T11:31:13.118404Z'
  license: null
  link: []
  maturity: null
  name: Lion Localizer
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/JHERED/ESAD072
    metadata:
      abstract: The illegal poaching of lions for their body parts poses a severe
        threat to lion populations across Africa. Poaching accounts for 35% of all
        human-caused lion deaths, with 51% attributed to retaliatory killings following
        livestock predation. In nearly half of the retaliatory killings, lion body
        parts are removed, suggesting that high demand for lion body parts may fuel
        killings attributed to human-lion conflict. Trafficked items are often confiscated
        in transit or destination countries far from their country of origin. DNA
        from lion parts may in some cases be the only available means for examining
        their geographic origins. In this paper, we present the Lion Localizer, a
        full-stack software tool that houses a comprehensive database of lion mitochondrial
        DNA (mtDNA) sequences sourced from previously published studies. The database
        covers 146 localities from across the African continent and India, providing
        information on the potential provenance of seized lion body parts. Lion mtDNA
        sequences of 350 or 1,140 bp corresponding to the cytochrome b region can
        be generated from lion products and queried against the Lion Localizer database.
        Using the query sequence, the Lion Localizer generates a listing of exact
        or partial matches, which are displayed on an interactive map of Africa. This
        allows for the rapid identification of potential regions and localities where
        lions have been or are presently being targeted by poachers. By examining
        the potential provenance of lion samples, the Lion Localizer serves as a valuable
        resource in the fight against lion poaching. The software is available at
        https://lionlocalizer.org.
      authors:
      - name: Au W.C.
      - name: Dures S.G.
      - name: Ishida Y.
      - name: Green C.E.
      - name: Zhao K.
      - name: Ogden R.
      - name: Roca A.L.
      citationCount: 0
      date: '2024-03-01T00:00:00Z'
      journal: Journal of Heredity
      title: 'Lion Localizer: A software tool for inferring the provenance of lions
        (Panthera leo) using mitochondrial DNA'
    note: null
    pmcid: null
    pmid: '37952226'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: DNA
    uri: http://edamontology.org/topic_0654
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-19T11:28:09.722051Z'
  biotoolsCURIE: biotools:metgene
  biotoolsID: metgene
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: shsubramaniam@ucsd.edu
    fundrefid: null
    gridid: null
    name: Shankar Subramaniam
    note: null
    orcidid: https://orcid.org/0000-0002-8059-4659
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Sumana Srinivasan
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Gene-centric metabolomics information retrieval tool.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    - term: Filtering
      uri: http://edamontology.org/operation_3695
    - term: Natural product identification
      uri: http://edamontology.org/operation_3803
    output: []
  homepage: https://bdcw.org/MetGENE/index.php
  homepage_status: 0
  language:
  - PHP
  - R
  lastUpdate: '2024-04-19T11:28:09.723894Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/metabolomicsworkbench/MetGENE
  maturity: null
  name: MetGENE
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/GIGASCIENCE/GIAD089
    metadata:
      abstract: 'Background: Biomedical research often involves contextual integration
        of multimodal and multiomic data in search of mechanisms for improved diagnosis,
        treatment, and monitoring. Researchers need to access information from diverse
        sources, comprising data in various and sometimes incongruent formats. The
        downstream processing of the data to decipher mechanisms by reconstructing
        networks and developing quantitative models warrants considerable effort.
        Results: MetGENE is a knowledge-based, gene-centric data aggregator that hierarchically
        retrieves information about the gene(s), their related pathway(s), reaction(s),
        metabolite(s), and metabolomic studies from standard data repositories under
        one dashboard to enable ease of access through centralization of relevant
        information. We note that MetGENE focuses only on those genes that encode
        for proteins directly associated with metabolites. All other gene–metabolite
        associations are beyond the current scope of MetGENE. Further, the information
        can be contextualized by filtering by species, anatomy (tissue), and condition
        (disease or phenotype). Conclusions: MetGENE is an open-source tool that aggregates
        metabolite information for a given gene(s) and presents them in different
        computable formats (e.g., JSON) for further integration with other omics studies.
        MetGENE is available at https://bdcw.org/MetGENE/index.php.'
      authors:
      - name: Srinivasan S.
      - name: Maurya M.R.
      - name: Ramachandran S.
      - name: Fahy E.
      - name: Subramaniam S.
      citationCount: 0
      date: '2023-01-01T00:00:00Z'
      journal: GigaScience
      title: 'MetGENE: gene-centric metabolomics information retrieval tool'
    note: null
    pmcid: PMC10659118
    pmid: '37983749'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Anatomy
    uri: http://edamontology.org/topic_3067
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:32:44.536054Z'
  biotoolsCURIE: biotools:drava
  biotoolsID: drava
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Qianwen Wang
    note: null
    orcidid: https://orcid.org/0000-0003-1728-4102
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Aligning human concepts with machine learning latent dimensions for
    the visual exploration of small multiples.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Dimensionality reduction
      uri: http://edamontology.org/operation_3935
    - term: Essential dynamics
      uri: http://edamontology.org/operation_3891
    output: []
  homepage: https://qianwen.info/DRAVA/
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-19T10:32:44.538164Z'
  license: null
  link: []
  maturity: null
  name: Drava
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1145/3544548.3581127
    metadata:
      abstract: Latent vectors extracted by machine learning (ML) are widely used
        in data exploration (e.g., t-SNE) but suffer from a lack of interpretability.
        While previous studies employed disentangled representation learning (DRL)
        to enable more interpretable exploration, they often overlooked the potential
        mismatches between the concepts of humans and the semantic dimensions learned
        by DRL. To address this issue, we propose Drava, a visual analytics system
        that supports users in 1) relating the concepts of humans with the semantic
        dimensions of DRL and identifying mismatches, 2) providing feedback to minimize
        the mismatches, and 3) obtaining data insights from concept-driven exploration.
        Drava provides a set of visualizations and interactions based on visual piles
        to help users understand and refine concepts and conduct concept-driven exploration.
        Meanwhile, Drava employs a concept adaptor model to fine-tune the semantic
        dimensions of DRL based on user refinement. The usefulness of Drava is demonstrated
        through application scenarios and experimental validation.
      authors:
      - name: Wang Q.
      - name: L'Yi S.
      - name: Gehlenborg N.
      citationCount: 2
      date: '2023-04-19T00:00:00Z'
      journal: Conference on Human Factors in Computing Systems - Proceedings
      title: 'DRAVA: Aligning Human Concepts with Machine Learning Latent Dimensions
        for the Visual Exploration of Small Multiples'
    note: null
    pmcid: PMC10707479
    pmid: '38074525'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Physics
    uri: http://edamontology.org/topic_3318
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:29:44.116723Z'
  biotoolsCURIE: biotools:chemprop
  biotoolsID: chemprop
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: mcgillc2@vcu.edu
    fundrefid: null
    gridid: null
    name: Charles J. McGill
    note: null
    orcidid: https://orcid.org/0000-0003-2704-7717
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Machine learning package for chemical property prediction.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: Splitting
      uri: http://edamontology.org/operation_3359
    output: []
  homepage: http://github.com/chemprop/chemprop
  homepage_status: 0
  language:
  - Python
  - Shell
  - D
  lastUpdate: '2024-04-19T10:29:44.118952Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: http://github.com/chemprop/chemprop_benchmark
  maturity: null
  name: Chemprop
  operatingSystem:
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1021/ACS.JCIM.3C01250
    metadata:
      abstract: Deep learning has become a powerful and frequently employed tool for
        the prediction of molecular properties, thus creating a need for open-source
        and versatile software solutions that can be operated by nonexperts. Among
        the current approaches, directed message-passing neural networks (D-MPNNs)
        have proven to perform well on a variety of property prediction tasks. The
        software package Chemprop implements the D-MPNN architecture and offers simple,
        easy, and fast access to machine-learned molecular properties. Compared to
        its initial version, we present a multitude of new Chemprop functionalities
        such as the support of multimolecule properties, reactions, atom/bond-level
        properties, and spectra. Further, we incorporate various uncertainty quantification
        and calibration methods along with related metrics as well as pretraining
        and transfer learning workflows, improved hyperparameter optimization, and
        other customization options concerning loss functions or atom/bond features.
        We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level,
        and spectra functionality on a variety of property prediction data sets, including
        MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction
        of water-octanol partition coefficients, reaction barrier heights, atomic
        partial charges, and absorption spectra. Chemprop enables out-of-the-box training
        of D-MPNN models for a variety of problem settings in fast, user-friendly,
        and open-source software.
      authors:
      - name: Heid E.
      - name: Greenman K.P.
      - name: Chung Y.
      - name: Li S.-C.
      - name: Graff D.E.
      - name: Vermeire F.H.
      - name: Wu H.
      - name: Green W.H.
      - name: McGill C.J.
      citationCount: 7
      date: '2024-01-08T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: 'Chemprop: A Machine Learning Package for Chemical Property Prediction'
    note: null
    pmcid: PMC10777403
    pmid: '38147829'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: NMR
    uri: http://edamontology.org/topic_0593
  - term: Chemistry
    uri: http://edamontology.org/topic_3314
  - term: Endocrinology and metabolism
    uri: http://edamontology.org/topic_3407
  - term: Molecular biology
    uri: http://edamontology.org/topic_3047
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:26:21.921850Z'
  biotoolsCURIE: biotools:mcptaggr
  biotoolsID: mcptaggr
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: f.tomoyuki@okayama-u.ac.jp
    fundrefid: null
    gridid: null
    name: Tomoyuki Furuta
    note: null
    orcidid: https://orcid.org/0000-0002-0869-6626
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: R package for accurate genotype calling in reduced representation sequencing
    data by eliminating error-prone markers based on genome comparison.
  documentation:
  - note: null
    type:
    - User manual
    url: https://tomoyukif.github.io/MCPtaggR/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Demultiplexing
      uri: http://edamontology.org/operation_3933
    - term: SNP detection
      uri: http://edamontology.org/operation_0484
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    output: []
  homepage: https://github.com/tomoyukif/MCPtaggR
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-19T10:26:21.924007Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: MCPtaggR
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/DNARES/DSAD027
    metadata:
      abstract: Reduced representation sequencing (RRS) offers cost-effective, high-throughput
        genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are
        typically mapped onto a reference genome. However, mapping reads harbouring
        mismatches against the reference can potentially result in mismapping and
        biased mapping, leading to the detection of error-prone markers that provide
        incorrect genotype information. We established a genotype-calling pipeline
        named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate
        genotyping by eliminating error-prone markers. MCPtagg was designed for the
        RRS-based genotyping of a population derived from a biparental cross. The
        MCPtagg pipeline filters out error-prone markers prior to genotype calling
        based on marker collinearity information obtained by comparing the genome
        sequences of the parents of a population to be genotyped. A performance evaluation
        on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore,
        our performance test using a genome assembly that was obtained by genome sequence
        polishing on an available genome assembly suggests that our pipeline performs
        well with converted genomes, rather than necessitating de novo assembly. This
        demonstrates its flexibility and scalability. The R package, MCPtaggR, was
        developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.
      authors:
      - name: Furuta T.
      - name: Yamamoto T.
      citationCount: 0
      date: '2024-02-01T00:00:00Z'
      journal: DNA Research
      title: 'MCPtaggR: R package for accurate genotype calling in reduced representation
        sequencing data by eliminating error-prone markers based on genome comparison'
    note: null
    pmcid: PMC10799318
    pmid: '38134958'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Structural variation
    uri: http://edamontology.org/topic_3175
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:25:58.781507Z'
  biotoolsCURIE: biotools:metaldock
  biotoolsID: metaldock
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: f.buda@lic.leidenuniv.nl
    fundrefid: null
    gridid: null
    name: Francesco Buda
    note: null
    orcidid: https://orcid.org/0000-0002-7157-7654
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: bonnet@chem.leidenuniv.nl
    fundrefid: null
    gridid: null
    name: Sylvestre Bonnet
    note: null
    orcidid: https://orcid.org/0000-0002-5810-3657
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: An open-access docking tool for easy and reproducible docking of metal
    complexes.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein-protein docking
      uri: http://edamontology.org/operation_3899
    - term: Metal-bound cysteine detection
      uri: http://edamontology.org/operation_1831
    - term: Free cysteine detection
      uri: http://edamontology.org/operation_1830
    output: []
  homepage: https://github.com/MatthijsHak/MetalDock
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T10:25:58.784175Z'
  license: MIT
  link: []
  maturity: null
  name: MetalDock
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1021/ACS.JCIM.3C01582
    metadata:
      abstract: Despite the proven potential of metal complexes as therapeutics, the
        lack of computational tools available for the high-throughput screening of
        their interactions with proteins is a limiting factor toward clinical developments.
        To address this challenge, we introduce MetalDock, an easy-to-use, open access
        docking software for docking metal complexes to proteins. Our tool integrates
        the AutoDock docking engine with three well-known quantum software packages
        to automate the docking of metal-organic complexes to proteins. We used a
        Monte Carlo sampling scheme to obtain the missing Lennard-Jones parameters
        for 12 metal atom types and demonstrated that these parameters generalize
        exceptionally well. Our results show that the poses obtained by MetalDock
        are highly accurate, as they predict the binding geometries experimentally
        determined by crystal structures with high spatial reproducibility. Three
        different case studies are presented that demonstrate the versatility of MetalDock
        for the docking of diverse metal-organic compounds to different biomacromolecules,
        including nucleic acids.
      authors:
      - name: Hakkennes M.L.A.
      - name: Buda F.
      - name: Bonnet S.
      citationCount: 2
      date: '2023-12-25T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: 'MetalDock: An Open Access Docking Tool for Easy and Reproducible Docking
        of Metal Complexes'
    note: null
    pmcid: PMC10751784
    pmid: '38048559'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: NMR
    uri: http://edamontology.org/topic_0593
  - term: DNA
    uri: http://edamontology.org/topic_0654
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:17:57.447656Z'
  biotoolsCURIE: biotools:clumppling
  biotoolsID: clumppling
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: xiranliu@stanford.edu
    fundrefid: null
    gridid: null
    name: Xiran Liu
    note: null
    orcidid: https://orcid.org/0000-0002-3955-9026
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: noahr@stanford.edu
    fundrefid: null
    gridid: null
    name: Noah A Rosenberg
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Cluster matching and permutation program with integer linear programming.
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  download: []
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Clustering
      uri: http://edamontology.org/operation_3432
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Pairwise sequence alignment
      uri: http://edamontology.org/operation_0491
    output: []
  homepage: https://github.com/PopGenClustering/Clumppling
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T10:17:57.449712Z'
  license: null
  link: []
  maturity: null
  name: Clumppling
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOINFORMATICS/BTAD751
    metadata:
      abstract: 'Motivation: In the mixed-membership unsupervised clustering analyses
        commonly used in population genetics, multiple replicate data analyses can
        differ in their clustering solutions. Combinatorial algorithms assist in aligning
        clustering outputs from multiple replicates so that clustering solutions can
        be interpreted and combined across replicates. Although several algorithms
        have been introduced, challenges exist in achieving optimal alignments and
        performing alignments in reasonable computation time. Results: We present
        Clumppling, a method for aligning replicate solutions in mixed-membership
        unsupervised clustering. The method uses integer linear programming for finding
        optimal alignments, embedding the cluster alignment problem in standard combinatorial
        optimization frameworks. In example analyses, we find that it achieves solutions
        with preferred values of a desired objective function relative to those achieved
        by Pong and that it proceeds with less computation time than Clumpak. It is
        also the first method to permit alignments across replicates with multiple
        arbitrary values of the number of clusters K.'
      authors:
      - name: Liu X.
      - name: Kopelman N.M.
      - name: Rosenberg N.A.
      citationCount: 0
      date: '2024-01-01T00:00:00Z'
      journal: Bioinformatics
      title: 'Clumppling: cluster matching and permutation program with integer linear
        programming'
    note: null
    pmcid: PMC10766593
    pmid: '38096585'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Structure analysis
    uri: http://edamontology.org/topic_0081
  - term: Population genetics
    uri: http://edamontology.org/topic_3056
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:15:50.618836Z'
  biotoolsCURIE: biotools:imagenomer
  biotoolsID: imagenomer
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: aorlichenko@tulane.edu
    fundrefid: null
    gridid: null
    name: Anton Orlichenko
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Description of a functional connectivity and omics analysis tool and
    case study identifying a race confound.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Essential dynamics
      uri: http://edamontology.org/operation_3891
    - term: Regression analysis
      uri: http://edamontology.org/operation_3659
    output: []
  homepage: https://aorliche.github.io/ImageNomer/live/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T10:15:50.621623Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/TulaneMBB/ImageNomer
  maturity: null
  name: ImageNomer
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1016/J.YNIRP.2023.100191
    metadata:
      abstract: 'Most packages for the analysis of fMRI-based functional connectivity
        (FC) and genomic data are used with a programming language interface, lacking
        an easy-to-navigate GUI frontend. This exacerbates two problems found in these
        types of data: demographic confounds and quality control in the face of high
        dimensionality of features. The reason is that it is too slow and cumbersome
        to use a programming interface to create all the necessary visualizations
        required to identify all correlations, confounding effects, or quality control
        problems in a dataset. FC in particular usually contains tens of thousands
        of features per subject, and can only be summarized and efficiently explored
        using visualizations. To remedy this situation, we have developed ImageNomer,
        a data visualization and analysis tool that allows inspection of both subject-level
        and cohort-level demographic, genomic, and imaging features. The software
        is Python-based, runs in a self-contained Docker image, and contains a browser-based
        GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an
        unexpected race confound when predicting achievement scores in the Philadelphia
        Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and
        single nucleotide polymorphism (SNP) data of healthy adolescents. In the past,
        many studies have attempted to use FC to identify achievement-related features
        in fMRI. Using ImageNomer to visualize trends in achievement scores between
        races, we find a clear potential for confounding effects if race can be predicted
        using FC. Using correlation analysis in the ImageNomer software, we show that
        FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more
        highly correlated with race. Investigating further, we find that whereas both
        FC and SNP (genomic) features can account for 10–15% of WRAT score variation,
        this predictive ability disappears when controlling for race. We also use
        ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia
        Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate
        the advantage of our ImageNomer GUI tool in data exploration and confound
        detection. Additionally, this work identifies race as a strong confound in
        FC data and casts doubt on the possibility of finding unbiased achievement-related
        features in fMRI and SNP data of healthy adolescents.'
      authors:
      - name: Orlichenko A.
      - name: Daly G.
      - name: Zhou Z.
      - name: Liu A.
      - name: Shen H.
      - name: Deng H.-W.
      - name: Wang Y.-P.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: 'Neuroimage: Reports'
      title: 'ImageNomer: Description of a functional connectivity and omics analysis
        tool and case study identifying a race confound'
    note: null
    pmcid: PMC10732473
    pmid: '38125823'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Medical imaging
    uri: http://edamontology.org/topic_3384
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version: []
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