count: 29626
list:
- accessibility: Open access
  additionDate: '2023-03-08T09:19:09.284884Z'
  biotoolsCURIE: biotools:odamnet
  biotoolsID: odamnet
  collectionID: []
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: morgane.terezol@univ-amu.fr
    fundrefid: null
    gridid: null
    name: Morgane Térézol
    note: null
    orcidid: https://orcid.org/0000-0002-4090-2573
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Anaïs Baudot
    note: null
    orcidid: https://orcid.org/0000-0003-0885-7933
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ozan Ozisik
    note: null
    orcidid: https://orcid.org/0000-0001-5980-8002
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: A Python package to study molecular relationship between environmental
    factors and rare diseases.
  documentation:
  - note: null
    type:
    - User manual
    url: https://odamnet.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity:
  - Rare Diseases
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Network analysis
      uri: http://edamontology.org/operation_3927
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Protein interaction network analysis
      uri: http://edamontology.org/operation_0276
    output: []
  homepage: https://pypi.org/project/ODAMNet/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-05-02T11:58:39.872174Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/MOohTus/ODAMNet
  maturity: null
  name: ODAMNet
  operatingSystem:
  - Linux
  otherID: []
  owner: mterezol
  publication:
  - doi: 10.1016/j.softx.2024.101701
    metadata:
      abstract: 'Environmental factors are external conditions that can affect the
        health of living organisms. For a number of rare genetic diseases, an interplay
        between genetic and environmental factors is known or suspected. However,
        the studies are limited by the scarcity of patients and the difficulties in
        gathering reliable exposure information. In order to aid in fostering research
        between environmental factors and rare diseases, we propose ODAMNet, a Python
        package to investigate the possible relationships between chemicals, which
        are a subset of environmental factors, and rare diseases. ODAMNet offers three
        different and complementary bioinformatics approaches for the exploration
        of relationships: overlap analysis, active module identification and random
        walk with restart. ODAMNet allows systematic analysis of chemical - rare disease
        relationships and generation of hypotheses for further investigation of effect
        mechanisms.'
      authors:
      - name: Terezol M.
      - name: Baudot A.
      - name: Ozisik O.
      citationCount: 0
      date: '2024-05-01T00:00:00Z'
      journal: SoftwareX
      title: 'ODAMNet: A Python package to identify molecular relationships between
        chemicals and rare diseases using overlap, active module and random walk approaches'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T10:02:03.465896Z'
  biotoolsCURIE: biotools:zerobind
  biotoolsID: zerobind
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: 2008xypan@sjtu.edu.cn
    fundrefid: null
    gridid: null
    name: Xiaoyong Pan
    note: null
    orcidid: https://orcid.org/0000-0001-5010-464X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Yuxuan Wang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Protein-specific zero-shot predictor with subgraph matching for drug-target
    interactions.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Virtual screening
      uri: http://edamontology.org/operation_3938
    - term: Protein interaction prediction
      uri: http://edamontology.org/operation_2492
    - term: Protein modelling
      uri: http://edamontology.org/operation_0477
    output: []
  homepage: http://www.csbio.sjtu.edu.cn/bioinf/ZeroBind/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-05-02T10:02:03.468091Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/myprecioushh/ZeroBind
  maturity: null
  name: ZeroBind
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1038/S41467-023-43597-1
    metadata:
      abstract: Existing drug-target interaction (DTI) prediction methods generally
        fail to generalize well to novel (unseen) proteins and drugs. In this study,
        we propose a protein-specific meta-learning framework ZeroBind with subgraph
        matching for predicting protein-drug interactions from their structures. During
        the meta-training process, ZeroBind formulates training a protein-specific
        model, which is also considered a learning task, and each task uses graph
        neural networks (GNNs) to learn the protein graph embedding and the molecular
        graph embedding. Inspired by the fact that molecules bind to a binding pocket
        in proteins instead of the whole protein, ZeroBind introduces a weakly supervised
        subgraph information bottleneck (SIB) module to recognize the maximally informative
        and compressive subgraphs in protein graphs as potential binding pockets.
        In addition, ZeroBind trains the models of individual proteins as multiple
        tasks, whose importance is automatically learned with a task adaptive self-attention
        module to make final predictions. The results show that ZeroBind achieves
        superior performance on DTI prediction over existing methods, especially for
        those unseen proteins and drugs, and performs well after fine-tuning for those
        proteins or drugs with a few known binding partners.
      authors:
      - name: Wang Y.
      - name: Xia Y.
      - name: Yan J.
      - name: Yuan Y.
      - name: Shen H.-B.
      - name: Pan X.
      citationCount: 3
      date: '2023-12-01T00:00:00Z'
      journal: Nature Communications
      title: 'ZeroBind: a protein-specific zero-shot predictor with subgraph matching
        for drug-target interactions'
    note: null
    pmcid: PMC10687269
    pmid: '38030641'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Molecular biology
    uri: http://edamontology.org/topic_3047
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T09:56:40.263517Z'
  biotoolsCURIE: biotools:colocdb
  biotoolsID: colocdb
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: pjia@big.ac.cn
    fundrefid: null
    gridid: null
    name: Peilin Jia
    note: null
    orcidid: https://orcid.org/0000-0003-4523-4153
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Siyu Pan
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: COLOCdb represents the most comprehensive colocalization analysis by
    integrating publicly available GWASs, 187 multi-omics datasets from 13 types of
    xQTL and three different colocalization algorithms.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    - term: Collapsing methods
      uri: http://edamontology.org/operation_3791
    - term: Genetic mapping
      uri: http://edamontology.org/operation_0282
    - term: Gene expression QTL analysis
      uri: http://edamontology.org/operation_3232
    output: []
  homepage: https://ngdc.cncb.ac.cn/colocdb
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-02T09:56:40.265959Z'
  license: null
  link: []
  maturity: null
  name: COLOCdb
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD939
    metadata:
      abstract: 'Large-scale genome-wide association studies (GWAS) have provided
        profound insights into complex traits and diseases. Yet, deciphering the fine-scale
        molecular mechanisms of how genetic variants manifest to cause the phenotypes
        remains a daunting task. Here, we present COLOCdb (https://ngdc.cncb.ac.cn/colocdb),
        a comprehensive genetic colocalization database by integrating more than 3000
        GWAS summary statistics and 13 types of xQTL to date. By employing two representative
        approaches for the colocalization analysis, COLOCdb deposits results from
        three key components: (i) GWAS-xQTL, pair-wise colocalization between GWAS
        loci and different types of xQTL, (ii) GWAS–GWAS, pair-wise colocalization
        between the trait-associated genetic loci from GWASs and (iii) xQTL–xQTL,
        pair-wise colocalization between the genetic loci associated with molecular
        phenotypes in xQTLs. These results together represent the most comprehensive
        colocalization analysis, which also greatly expands the list of shared variants
        with genetic pleiotropy. We expect that COLOCdb can serve as a unique and
        useful resource in advancing the discovery of new biological mechanisms and
        benefit future functional studies.'
      authors:
      - name: Pan S.
      - name: Kang H.
      - name: Liu X.
      - name: Li S.
      - name: Yang P.
      - name: Wu M.
      - name: Yuan N.
      - name: Lin S.
      - name: Zheng Q.
      - name: Jia P.
      citationCount: 0
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'COLOCdb: a comprehensive resource for multi-model colocalization of
        complex traits'
    note: null
    pmcid: PMC10767919
    pmid: '37941154'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: GWAS study
    uri: http://edamontology.org/topic_3517
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Epigenetics
    uri: http://edamontology.org/topic_3295
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T09:52:28.384050Z'
  biotoolsCURIE: biotools:zebra_ccb
  biotoolsID: zebra_ccb
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: fabianmichael.kern@helmholtz-hips.de
    fundrefid: null
    gridid: null
    name: Fabian Kern
    note: null
    orcidid: https://orcid.org/0000-0002-8223-3750
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Matthias Flotho
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Hierarchically integrated gene expression atlas of the murine and human
    brain at single-cell resolution.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    output: []
  homepage: https://www.ccb.uni-saarland.de/zebra
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-02T09:52:28.386397Z'
  license: null
  link: []
  maturity: null
  name: ZEBRA
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD990
    metadata:
      abstract: The molecular causes and mechanisms of neurodegenerative diseases
        remain poorly understood. A growing number of single-cell studies have implicated
        various neural, glial, and immune cell subtypes to affect the mammalian central
        nervous system in many age-related disorders. Integrating this body of transcriptomic
        evidence into a comprehensive and reproducible framework poses several computational
        challenges. Here, we introduce ZEBRA, a large single-cell and single-nucleus
        RNA-seq database. ZEBRA integrates and normalizes gene expression and metadata
        from 33 studies, encompassing 4.2 million human and mouse brain cells sampled
        from 39 brain regions. It incorporates samples from patients with neurodegenerative
        diseases like Alzheimer’s disease, Parkinson’s disease, and Multiple sclerosis,
        as well as samples from relevant mouse models. We employed scVI, a deep probabilistic
        auto-encoder model, to integrate the samples and curated both cell and sample
        metadata for downstream analysis. ZEBRA allows for cell-type and disease-specific
        markers to be explored and compared between sample conditions and brain regions,
        a cell composition analysis, and gene-wise feature mappings. Our comprehensive
        molecular database facilitates the generation of data-driven hypotheses, enhancing
        our understanding of mammalian brain function during aging and disease. The
        data sets, along with an interactive database are freely available at https://www.ccb.uni-saarland.de/zebra.
        Graphical abstract
      authors:
      - name: Flotho M.
      - name: Amand J.
      - name: Hirsch P.
      - name: Grandke F.
      - name: Wyss-Coray T.
      - name: Keller A.
      - name: Kern F.
      citationCount: 1
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'ZEBRA: a hierarchically integrated gene expression atlas of the murine
        and human brain at single-cell resolution'
    note: null
    pmcid: PMC10767845
    pmid: '37941147'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Geriatric medicine
    uri: http://edamontology.org/topic_3399
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T09:47:32.738021Z'
  biotoolsCURIE: biotools:trrosettarna
  biotoolsID: trrosettarna
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: zhangfa@ict.ac.cn
    fundrefid: null
    gridid: null
    name: Fa Zhang
    note: null
    orcidid: https://orcid.org/0000-0002-2081-9369
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: zhenling@email.sdu.edu.cn
    fundrefid: null
    gridid: null
    name: Zhenling Peng
    note: null
    orcidid: https://orcid.org/0000-0003-0303-6693
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: yangjy@sdu.edu.cn
    fundrefid: null
    gridid: null
    name: Jianyi Yang
    note: null
    orcidid: https://orcid.org/0000-0003-2912-7737
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Automated prediction of RNA 3D structure with transformer network.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: RNA structure prediction
      uri: http://edamontology.org/operation_2441
    - term: RNA secondary structure prediction
      uri: http://edamontology.org/operation_0278
    - term: Protein structure prediction
      uri: http://edamontology.org/operation_0474
    - term: Fold recognition
      uri: http://edamontology.org/operation_0303
    - term: Network analysis
      uri: http://edamontology.org/operation_3927
    output: []
  homepage: https://yanglab.qd.sdu.edu.cn/trRosettaRNA
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-02T09:47:32.740447Z'
  license: null
  link: []
  maturity: null
  name: trRosettaRNA
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1038/S41467-023-42528-4
    metadata:
      abstract: 'RNA 3D structure prediction is a long-standing challenge. Inspired
        by the recent breakthrough in protein structure prediction, we developed trRosettaRNA,
        an automated deep learning-based approach to RNA 3D structure prediction.
        The trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries
        prediction by a transformer network; and 3D structure folding by energy minimization.
        Benchmark tests suggest that trRosettaRNA outperforms traditional automated
        methods. In the blind tests of the 15th Critical Assessment of Structure Prediction
        (CASP15) and the RNA-Puzzles experiments, the automated trRosettaRNA predictions
        for the natural RNAs are competitive with the top human predictions. trRosettaRNA
        also outperforms other deep learning-based methods in CASP15 when measured
        by the Z-score of the Root-Mean-Square Deviation. Nevertheless, it remains
        challenging to predict accurate structures for synthetic RNAs with an automated
        approach. We hope this work could be a good start toward solving the hard
        problem of RNA structure prediction with deep learning.'
      authors:
      - name: Wang W.
      - name: Feng C.
      - name: Han R.
      - name: Wang Z.
      - name: Ye L.
      - name: Du Z.
      - name: Wei H.
      - name: Zhang F.
      - name: Peng Z.
      - name: Yang J.
      citationCount: 6
      date: '2023-12-01T00:00:00Z'
      journal: Nature Communications
      title: 'trRosettaRNA: automated prediction of RNA 3D structure with transformer
        network'
    note: null
    pmcid: PMC10636060
    pmid: '37945552'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Nucleic acid structure analysis
    uri: http://edamontology.org/topic_0097
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T09:45:52.183190Z'
  biotoolsCURIE: biotools:scmuffin
  biotoolsID: scmuffin
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: paride.pelucchi@itb.cnr.it
    fundrefid: null
    gridid: null
    name: Paride Pelucchi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: ettore.mosca@itb.cnr.it
    fundrefid: null
    gridid: null
    name: Ettore Mosca
    note: null
    orcidid: https://orcid.org/0000-0002-3102-5150
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: R package to disentangle solid tumor heterogeneity by single-cell gene
    expression analysis.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Expression analysis
      uri: http://edamontology.org/operation_2495
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    output: []
  homepage: https://emosca-cnr.github.io/scMuffin
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-05-02T09:45:52.185902Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/emosca-cnr/scMuffin
  maturity: null
  name: scMuffin
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/S12859-023-05563-Y
    metadata:
      abstract: 'Introduction: Single-cell (SC) gene expression analysis is crucial
        to dissect the complex cellular heterogeneity of solid tumors, which is one
        of the main obstacles for the development of effective cancer treatments.
        Such tumors typically contain a mixture of cells with aberrant genomic and
        transcriptomic profiles affecting specific sub-populations that might have
        a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing
        approaches. We present scMuffin, an R package that enables the characterization
        of cell identity in solid tumors on the basis of a various and complementary
        analyses on SC gene expression data. Results: scMuffin provides a series of
        functions to calculate qualitative and quantitative scores, such as: expression
        of marker sets for normal and tumor conditions, pathway activity, cell state
        trajectories, Copy Number Variations, transcriptional complexity and proliferation
        state. Thus, scMuffin facilitates the combination of various evidences that
        can be used to distinguish normal and tumoral cells, define cell identities,
        cluster cells in different ways, link genomic aberrations to phenotypes and
        identify subtle differences between cell subtypes or cell states. We analysed
        public SC expression datasets of human high-grade gliomas as a proof-of-concept
        to show the value of scMuffin and illustrate its user interface. Nevertheless,
        these analyses lead to interesting findings, which suggest that some chromosomal
        amplifications might underlie the invasive tumor phenotype and the presence
        of cells that possess tumor initiating cells characteristics. Conclusions:
        The analyses offered by scMuffin and the results achieved in the case study
        show that our tool helps addressing the main challenges in the bioinformatics
        analysis of SC expression data from solid tumors.'
      authors:
      - name: Nale V.
      - name: Chiodi A.
      - name: Di Nanni N.
      - name: Cifola I.
      - name: Moscatelli M.
      - name: Cocola C.
      - name: Gnocchi M.
      - name: Piscitelli E.
      - name: Sula A.
      - name: Zucchi I.
      - name: Reinbold R.
      - name: Milanesi L.
      - name: Mezzelani A.
      - name: Pelucchi P.
      - name: Mosca E.
      citationCount: 1
      date: '2023-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell
        gene expression analysis'
    note: null
    pmcid: PMC10680269
    pmid: '38012590'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T09:43:00.475332Z'
  biotoolsCURIE: biotools:veupathdb
  biotoolsID: veupathdb
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: oharb@sas.upenn.edu
    fundrefid: null
    gridid: null
    name: Omar S Harb
    note: null
    orcidid: https://orcid.org/0000-0003-4446-6200
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jorge Alvarez-Jarreta
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: VEuPathDB is packed with data, tools and visualizations that can help
    answer your research questions. We gather data from many sources, analyze according
    to standard workflows, and present the results for you to mine in a point and
    click interface. The Eukaryotic Pathogen, Vector and Host Informatics Resource
    (VEuPathDB, https://veupathdb.org) is a Bioinformatics Resource Center funded
    by the National Institutes of Health with additional funding from the Wellcome
    Trust. VEuPathDB supports >600 organisms that comprise invertebrate vectors, eukaryotic
    pathogens (protists and fungi) and relevant free-living or non-pathogenic species
    or hosts.
  documentation: []
  download: []
  editPermission:
    authors: []
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  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Homology-based gene prediction
      uri: http://edamontology.org/operation_3663
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: https://veupathdb.org
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-02T09:43:00.477868Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/VEuPathDB
  maturity: null
  name: VEuPathDB
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD1003
    metadata:
      abstract: T he Eukary otic Pathogen, Vectorãnd Host Inf ormatics R esource (VEuPathDB,
        https://v eupathdb.org ) isã Bioinf ormatics R esource Center funded by the
        National Institutes of Health withãdditional funding from the Wellcome Trust.
        VEuPathDB supports > 600 organisms that comprise in v er- tebrate v ectors,
        eukary otic pathogens (protistsãnd fungi)ãnd rele vãnt free-living or non-pathogenic
        species or hosts. Since 2004, VEuPathDB hasãnalyzed omics data from the public
        domain using contemporary bioinformatic workflows, including orthology predictions
        via OrthoMCL,ãnd integrated theãnalysis results withãnalysis tools, visualizations,ãndãdvanced
        search capabilities. The unique data mining platform coupled with > 30 0 0
        pre-analyzed datã sets facilitãtes the exploration of pertinent omics datã
        in support of h ypothesis driv en research. Comparisonsãre easily madeãcross
        data sets, data typesãnd organisms. A Galaxy workspace offers the opportunity
        for theãnalysis of private large-scale datasetsãnd for porting to VEuPathDB
        for comparisons with integrated data. The MapVEu tool providesã platform for
        exploration of spatially resolved data suchãs vector surveillanceãnd insecticide
        resistance monitoring. Toãddress the growing body of omics dataãndãdvances
        in laboratory techniques, VEuPathDB hasãdded se v eral ne w datã t ypes, searchesãnd
        features, impro v ed the Galaxy w orkspace en vironment, redesigned the MapVEu
        interfaceãnd updated the infrastr uct ure toãccommodate these changes.
      authors:
      - name: Alvarez-Jarreta J.
      - name: Amos B.
      - name: Aurrecoechea C.
      - name: Bah S.
      - name: Barba M.
      - name: Barreto A.
      - name: Basenko E.Y.
      - name: Belnap R.
      - name: Blevins A.
      - name: Bohme U.
      - name: Brestelli J.
      - name: Brown S.
      - name: Callan D.
      - name: Campbell L.I.
      - name: Christophides G.K.
      - name: Crouch K.
      - name: Davison H.R.
      - name: DeBarry J.D.
      - name: Demko R.
      - name: Doherty R.
      - name: Duan Y.
      - name: Dundore W.
      - name: Dyer S.
      - name: Falke D.
      - name: Fischer S.
      - name: Gajria B.
      - name: Galdi D.
      - name: Giraldo-Calderon G.I.
      - name: Harb O.S.
      - name: Harper E.
      - name: Helb D.
      - name: Howington C.
      - name: Hu S.
      - name: Humphrey J.
      - name: Iodice J.
      - name: Jones A.
      - name: Judkins J.
      - name: Kelly S.A.
      - name: Kissinger J.C.
      - name: Kittur N.
      - name: Kwon D.K.
      - name: Lamoureux K.
      - name: Li W.
      - name: Lodha D.
      - name: MacCallum R.M.
      - name: Maslen G.
      - name: McDowell M.A.
      - name: Myers J.
      - name: Nural M.V.
      - name: Roos D.S.
      - name: Rund S.S.C.
      - name: Shanmugasundram A.
      - name: Sitnik V.
      - name: Spruill D.
      - name: Starns D.
      - name: Tomko S.S.
      - name: Wang H.
      - name: Warrenfeltz S.
      - name: Wieck R.
      - name: Wilkinson P.A.
      - name: Zheng J.
      citationCount: 3
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'VEuPathDB: the eukaryotic pathogen, vectorãnd host bioinf ormatics resour
        ce cent er in 2023'
    note: null
    pmcid: PMC10767879
    pmid: '37953350'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T09:40:27.871566Z'
  biotoolsCURIE: biotools:deepfold
  biotoolsID: deepfold
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: nohyung@hanyang.ac.kr
    fundrefid: null
    gridid: null
    name: Yung-Kyun Noh
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Keehyoung Joo
    note: null
    orcidid: https://orcid.org/0000-0002-4612-0927
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Enhancing protein structure prediction through optimized loss functions,
    improved template features, and re-optimized energy function.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein structure prediction
      uri: http://edamontology.org/operation_0474
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    - term: Protein function prediction
      uri: http://edamontology.org/operation_1777
    output: []
  homepage: https://github.com/newtonjoo/deepfold
  homepage_status: 0
  language:
  - C
  - C++
  - Python
  lastUpdate: '2024-05-02T09:40:27.873828Z'
  license: Apache-2.0
  link: []
  maturity: null
  name: DeepFold
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/bioinformatics/btad712
    metadata:
      abstract: 'Motivation: Predicting protein structures with high accuracy is a
        critical challenge for the broad community of life sciences and industry.
        Despite progress made by deep neural networks like AlphaFold2, there is a
        need for further improvements in the quality of detailed structures, such
        as side-chains, along with protein backbone structures. Results: Building
        upon the successes of AlphaFold2, the modifications we made include changing
        the losses of side-chain torsion angles and frame aligned point error, adding
        loss functions for side chain confidence and secondary structure prediction,
        and replacing template feature generation with a new alignment method based
        on conditional random fields. We also performed re-optimization by conformational
        space annealing using a molecular mechanics energy function which integrates
        the potential energies obtained from distogram and side-chain prediction.
        In the CASP15 blind test for single protein and domain modeling (109 domains),
        DeepFold ranked fourth among 132 groups with improvements in the details of
        the structure in terms of backbone, side-chain, and Molprobity. In terms of
        protein backbone accuracy, DeepFold achieved a median GDT-TS score of 88.64
        compared with 85.88 of AlphaFold2. For TBM-easy/hard targets, DeepFold ranked
        at the top based on Z-scores for GDT-TS. This shows its practical value to
        the structural biology community, which demands highly accurate structures.
        In addition, a thorough analysis of 55 domains from 39 targets with publicly
        available structures indicates that DeepFold shows superior side-chain accuracy
        and Molprobity scores among the top-performing groups.'
      authors:
      - name: Lee J.-W.
      - name: Won J.-H.
      - name: Jeon S.
      - name: Choo Y.
      - name: Yeon Y.
      - name: Oh J.-S.
      - name: Kim M.
      - name: Kim S.
      - name: Joung I.
      - name: Jang C.
      - name: Lee S.J.
      - name: Kim T.H.
      - name: Jin K.H.
      - name: Song G.
      - name: Kim E.-S.
      - name: Yoo J.
      - name: Paek E.
      - name: Noh Y.-K.
      - name: Joo K.
      citationCount: 1
      date: '2023-12-01T00:00:00Z'
      journal: Bioinformatics
      title: 'DeepFold: Enhancing protein structure prediction through optimized loss
        functions, improved template features, and re-optimized energy function'
    note: null
    pmcid: PMC10699847
    pmid: '37995286'
    type: []
    version: null
  relation: []
  toolType:
  - Workflow
  topic:
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  - term: Protein folds and structural domains
    uri: http://edamontology.org/topic_0736
  - term: Protein structural motifs and surfaces
    uri: http://edamontology.org/topic_0166
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T09:38:05.878431Z'
  biotoolsCURIE: biotools:drmref
  biotoolsID: drmref
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: Pora.Kim@uth.tmc.edu
    fundrefid: null
    gridid: null
    name: Pora Kim
    note: null
    orcidid: https://orcid.org/0000-0002-8321-6864
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: qsong1@ufl.edu
    fundrefid: null
    gridid: null
    name: Qianqian Song
    note: null
    orcidid: https://orcid.org/0000-0002-4455-5302
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: xiaobo.zhou@uth.tmc.edu
    fundrefid: null
    gridid: null
    name: Xiaobo Zhou
    note: null
    orcidid: https://orcid.org/0000-0001-7191-6495
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Comprehensive reference map of drug resistance mechanisms in human
    cancer.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    - term: Enrichment analysis
      uri: http://edamontology.org/operation_3501
    - term: miRNA target prediction
      uri: http://edamontology.org/operation_0463
    output: []
  homepage: https://ccsm.uth.edu/DRMref/
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-02T09:38:05.880496Z'
  license: null
  link: []
  maturity: null
  name: DRMref
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD1087
    metadata:
      abstract: Drug resistance posesã significant challenge in cancer treatment.
        Despite the initial effectiveness of therapies suchãs chemotherapy, targeted
        therapyãnd immunotherapy, many patients eventually develop resistance. To
        gain deep insights into the underlying mechanisms, single-cell profiling has
        been performed to interrogate drug resistanceãt cell le v el. Herein, w e
        ha v e built the DRMref database ( https://ccsm.uth.edu/ DRMref/) to provide
        comprehensive characterization of drug resistance using single-cell data from
        drug treatment settings. The current version of DRMref includes 42 single-cell
        datasets from 30 studies, co v ering 382 samples, 13 major cancer types, 26
        cancer subtypes, 35 treatment regimensãnd 42 drugs. All datasets in DRMrefãre
        browsableãnd searchable, with detailedãnnotations pro vided. Mean while, DRMref
        includesãnalyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal
        transition, cell-cell interactionãnd differentially expressed genes in resistãnt
        cells. Notãbly, DRMref in v estigates the drug resistãnce mechanisms (e.g
        . Aberration of Drug' s Therapeutic Tãrget, Drug Inactivãtion b y Str uct
        ure Modification, etc.) in resistant cells. A dditional enrichmentãnaly sis
        of hallmark / KEGG (Ky oto Ency clopedia of Genesãnd Genomes) / GO (Gene Ontology)
        pathwã y s,ãs w ellãs the identification of microRNA, motifãnd transcription
        fãctors in v olv ed in resistant cells, is provided in DRMref for user's exploration.
        Overall, DRMref servesãsã unique single-cell-based resource for st udying
        dr ug resistance, drug combination therapyãnd discovering novel drug targets.
      authors:
      - name: Liu X.
      - name: Yi J.
      - name: Li T.
      - name: Wen J.
      - name: Huang K.
      - name: Liu J.
      - name: Wang G.
      - name: Kim P.
      - name: Song Q.
      - name: Zhou X.
      citationCount: 2
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'DRMref: compr ehensive r efer ence map of drug r esistance mechanisms
        in human cancer'
    note: null
    pmcid: PMC10767840
    pmid: '37986230'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Pharmacology
    uri: http://edamontology.org/topic_0202
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-02T09:31:27.814833Z'
  biotoolsCURIE: biotools:img_pr
  biotoolsID: img_pr
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: antoniop.camargo@lbl.gov
    fundrefid: null
    gridid: null
    name: Antonio Pedro Camargo
    note: null
    orcidid: https://orcid.org/0000-0003-3913-2484
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: nckyrpides@lbl.gov
    fundrefid: null
    gridid: null
    name: Nikos C Kyrpides
    note: null
    orcidid: https://orcid.org/0000-0002-6131-0462
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Database of plasmids from genomes and metagenomes with rich annotations
    and metadata. It provides an extensive collection of plasmids, identified across
    thousands of isolate genomes, SAGs, MAGs, metagenomes, and metatranscriptomes
    using the geNomad tool. These plasmids are presented within a structured framework
    encompassing functional annotation and extensive metadata, which includes host
    taxonomy, similarity to other plasmids, geographic coordinates, and ecosystem
    information, allowing users to investigate plasmid function and ecology thoroughly.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    - term: Plasmid map drawing
      uri: http://edamontology.org/operation_0578
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Sequence assembly
      uri: http://edamontology.org/operation_0310
    output: []
  homepage: https://img.jgi.doe.gov/pr
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-02T09:31:27.816984Z'
  license: null
  link: []
  maturity: null
  name: IMG PR
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD964
    metadata:
      abstract: Plasmids are mobile genetic elements found in many clades of Archaea
        and Bacteria. They drive horizontal gene transfer, impacting ecological and
        evolutionary processes within microbial communities, and hold substantial
        importance in human health and biotechnology. To support plasmid research
        and provide scientists with data of an unprecedented diversity of plasmid
        sequences, we introduce the IMG/PR database, a new resource encompassing 699
        973 plasmid sequences derived from genomes, metagenomes and metatranscriptomes.
        IMG/PR is the first database to provide data of plasmid that were systematically
        identified from diverse microbiome samples. IMG/PR plasmids are associated
        with rich metadata that includes geographical and ecosystem information, host
        taxonomy, similarity to other plasmids, functional annotation, presence of
        genes involved in conjugation and antibiotic resistance. The database offers
        diverse methods for exploring its extensive plasmid collection, enabling users
        to navigate plasmids through metadata-centric queries, plasmid comparisons
        and BLAST searches. The web interface for IMG/PR is accessible at https://img.jgi.doe.gov/pr.
        Plasmid metadata and sequences can be downloaded from https://genome.jgi.doe.gov/portal/IMG_PR.
      authors:
      - name: Camargo A.P.
      - name: Call L.
      - name: Roux S.
      - name: Nayfach S.
      - name: Huntemann M.
      - name: Palaniappan K.
      - name: Ratner A.
      - name: Chu K.
      - name: Mukherjeep S.
      - name: Reddy T.B.K.
      - name: Chen I.-M.A.
      - name: Ivanova N.N.
      - name: Eloe-Fadrosh E.A.
      - name: Woyke T.
      - name: Baltrus D.A.
      - name: Castaneda-Barba S.
      - name: de la Cruz F.
      - name: Funnell B.E.
      - name: Hall J.P.J.
      - name: Mukhopadhyay A.
      - name: Rocha E.P.C.
      - name: Stalder T.
      - name: Top E.
      - name: Kyrpides N.C.
      citationCount: 0
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'IMG/PR: a database of plasmids from genomes and metagenomes with rich
        annotations and metadata'
    note: null
    pmcid: PMC10767988
    pmid: '37930866'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Metatranscriptomics
    uri: http://edamontology.org/topic_3941
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Mobile genetic elements
    uri: http://edamontology.org/topic_0798
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  validated: 0
  version: []
next: ?page=2
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