count: 29521
list:
- accessibility: Open access
  additionDate: '2024-04-16T14:45:34.140169Z'
  biotoolsCURIE: biotools:scpdb
  biotoolsID: scpdb
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: jianhuayao@tencent.com
    fundrefid: null
    gridid: null
    name: Jianhua Yao
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: ghwang@hit.edu.cn
    fundrefid: null
    gridid: null
    name: Guohua Wang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Comprehensive resource and knowledgebase for proteomic data at the
    single-cell resolution.
  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: Database search
      uri: http://edamontology.org/operation_2421
    - term: Protein quantification
      uri: http://edamontology.org/operation_3630
    - term: Gene expression profiling
      uri: http://edamontology.org/operation_0314
    - term: Essential dynamics
      uri: http://edamontology.org/operation_3891
    output: []
  homepage: https://scproteomicsdb.com/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-16T14:45:34.142458Z'
  license: null
  link: []
  maturity: null
  name: Single-cell Proteomic DataBase
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD1018
    metadata:
      abstract: The single-cell proteomics enables the direct quantification of proteinãbundanceãt
        the single-cell resolution, providing valuable insights into cellular phenotypes
        be y ond what can be inferred from transcriptomeãnalysisãlone. However, insufficient
        large-scale integrated databases hinder researchers fromãccessingãnd exploring
        single-cell proteomics, impeding theãdvancement of this field. To fill this
        deficiency, we presentã comprehensive database, namely Single-cell Proteomic
        DataBase (SPDB, https:// scproteomicsdb.com/ ), for general single-cell proteomic
        data, includingãntibody-based or mass spectrometry-based single-cell proteomics.
        Equipped with stãndardized datã processãndã user-friendly web interfãce, SPDB
        pro vides unified data formats for convenient interaction with downstreamãnalysis,ãnd
        offers not only dataset-level butãlso protein-le v el data searchãnd exploration
        capabilities. To enable detailed exhibition of single-cell proteomic data,
        SPDBãlso providesã module for visualizing data from the perspectives of cell
        metãdatã or protein features. The current version of SPDB encompasses 133ãntibody-based
        single-cell proteomic datasets in v olving more than 300 million cellsãnd
        o v er 800 mark er / surfãce proteins,ãnd 10 mass spectrometry-based single-cell
        proteomic datasets in v olving more than 40 0 0 cellsãnd o v er 70 0 0 proteins.
        Ov erall, SPDB is en visioned to be exploredãsã useful resource that will
        facilitate the wider research communities by providing detailed insights into
        proteomics from the single-cell perspective.
      authors:
      - name: Wang F.
      - name: Liu C.
      - name: Li J.
      - name: Yang F.
      - name: Song J.
      - name: Zang T.
      - name: Yao J.
      - name: Wang G.
      citationCount: 1
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: SPDB:ã comprehensive resourceãnd knowledgebase for proteomic dataãt the
        single-cell resolution
    note: null
    pmcid: PMC10767837
    pmid: '37953313'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Membrane and lipoproteins
    uri: http://edamontology.org/topic_0820
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:15:59.343761Z'
  biotoolsCURIE: biotools:phenosv
  biotoolsID: phenosv
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: wangk@chop.edu
    fundrefid: null
    gridid: null
    name: Kai Wang
    note: null
    orcidid: https://orcid.org/0000-0002-5585-982X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Zhuoran Xu
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: PhenoSV is a phenotype-aware structural variants interpretation and
    prioritization tool.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant prioritisation
      uri: http://edamontology.org/operation_3226
    - term: Formatting
      uri: http://edamontology.org/operation_0335
    - term: cis-regulatory element prediction
      uri: http://edamontology.org/operation_0441
    output: []
  homepage: https://phenosv.wglab.org
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-16T14:15:59.346819Z'
  license: null
  link: []
  maturity: null
  name: PhenoSV
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1038/S41467-023-43651-Y
    metadata:
      abstract: Structural variants (SVs) represent a major source of genetic variation
        associated with phenotypic diversity and disease susceptibility. While long-read
        sequencing can discover over 20,000 SVs per human genome, interpreting their
        functional consequences remains challenging. Existing methods for identifying
        disease-related SVs focus on deletion/duplication only and cannot prioritize
        individual genes affected by SVs, especially for noncoding SVs. Here, we introduce
        PhenoSV, a phenotype-aware machine-learning model that interprets all major
        types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse
        genomic features and employs a transformer-based architecture to predict their
        impacts under a multiple-instance learning framework. With phenotype information,
        PhenoSV further utilizes gene-phenotype associations to prioritize phenotype-related
        SVs. Evaluation on extensive human SV datasets covering all SV types demonstrates
        PhenoSV’s superior performance over competing methods. Applications in diseases
        suggest that PhenoSV can determine disease-related genes from SVs. A web server
        and a command-line tool for PhenoSV are available at https://phenosv.wglab.org
        .
      authors:
      - name: Xu Z.
      - name: Li Q.
      - name: Marchionni L.
      - name: Wang K.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: Nature Communications
      title: 'PhenoSV: interpretable phenotype-aware model for the prioritization
        of genes affected by structural variants'
    note: null
    pmcid: PMC10684511
    pmid: '38016949'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Structural variation
    uri: http://edamontology.org/topic_3175
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Epigenetics
    uri: http://edamontology.org/topic_3295
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:14:05.166974Z'
  biotoolsCURIE: biotools:mudoger
  biotoolsID: mudoger
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: ulisses.rocha@ufz.de
    fundrefid: null
    gridid: null
    name: Ulisses Rocha
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: The Multi-Domain Genome Recovery v1.0 (MuDoGeR v1.0) framework is a
    tool developed to help users to recover Metagenome-Assembled Genomes and Uncultivated
    Viral Genomes from whole-genome sequence (WGS) samples simultaneously. The MuDoGeR
    v1.0 framework acts as a wrapper for several tools. It was designed to be an easy-to-use
    tool that outputs ready-to-use comprehensive files.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Sequence assembly
      uri: http://edamontology.org/operation_0310
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    output: []
  homepage: https://github.com/mdsufz/MuDoGeR
  homepage_status: 0
  language:
  - Shell
  - R
  - Python
  lastUpdate: '2024-04-16T14:14:05.169036Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: MuDoGeR
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1111/1755-0998.13904
    metadata:
      abstract: Several computational frameworks and workflows that recover genomes
        from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is
        difficult for scientists with little bioinformatics experience to evaluate
        quality, annotate genes, dereplicate, assign taxonomy and calculate relative
        abundance and coverage of genomes belonging to different domains. MuDoGeR
        is a user-friendly tool tailored for those familiar with Unix command-line
        environment that makes it easy to recover genomes of prokaryotes, eukaryotes
        and viruses from metagenomes, either alone or in combination. We tested MuDoGeR
        using 24 individual-isolated genomes and 574 metagenomes, demonstrating the
        applicability for a few samples and high throughput. While MuDoGeR can recover
        eukaryotic viral sequences, its characterization is predominantly skewed towards
        bacterial and archaeal viruses, reflecting the field's current state. However,
        acting as a dynamic wrapper, the MuDoGeR is designed to constantly incorporate
        updates and integrate new tools, ensuring its ongoing relevance in the rapidly
        evolving field. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR.
        Additionally, MuDoGeR is also available as a Singularity container.
      authors:
      - name: Rocha U.
      - name: CoelhoKasmanas J.
      - name: Kallies R.
      - name: Saraiva J.P.
      - name: Toscan R.B.
      - name: Stefanic P.
      - name: Bicalho M.F.
      - name: BorimCorrea F.
      - name: Basturk M.N.
      - name: Fousekis E.
      - name: VianaBarbosa L.M.
      - name: Plewka J.
      - name: Probst A.J.
      - name: Baldrian P.
      - name: Stadler P.F.
      citationCount: 1
      date: '2024-02-01T00:00:00Z'
      journal: Molecular Ecology Resources
      title: 'MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy'
    note: null
    pmcid: null
    pmid: '37994269'
    type: []
    version: null
  relation: []
  toolType:
  - Workflow
  topic:
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:10:00.623929Z'
  biotoolsCURIE: biotools:oggmap
  biotoolsID: oggmap
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: ullrich@evolbio.mpg.de
    fundrefid: null
    gridid: null
    name: Kristian K Ullrich
    note: null
    orcidid: https://orcid.org/0000-0003-4308-9626
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Nikoleta E Glytnasi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Python package to extract gene ages per orthogroup and link them with
    single-cell RNA data.
  documentation:
  - note: null
    type:
    - User manual
    url: https://oggmap.readthedocs.io/en/latest/
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Gene expression profiling
      uri: http://edamontology.org/operation_0314
    - term: Dimensionality reduction
      uri: http://edamontology.org/operation_3935
    output: []
  homepage: https://github.com/kullrich/oggmap
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-16T14:10:00.646441Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: oggmap
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOINFORMATICS/BTAD657
    metadata:
      abstract: 'Summary: For model species, single-cell RNA-based cell atlases are
        available. A good cell atlas includes all major stages in a species’ ontogeny,
        and soon, they will be standard even for nonmodel species. Here, we propose
        a Python package called oggmap, which allows for the easy extraction of an
        orthomap (gene ages per orthogroup) for any given query species from OrthoFinder
        and other gene family data resources, like homologous groups from eggNOG or
        PLAZA. oggmap provides extracted gene ages for more than thousand eukaryotic
        species which can be further used to calculate gene age-weighted expression
        data from scRNA sequencing objects using the Python Scanpy toolkit. Not limited
        to one transcriptome evolutionary index, oggmap can visualize the individual
        gene category (e.g. age class, nucleotide diversity bin) and their corresponding
        expression profiles to investigate scRNA-based cell type assignments in an
        evolutionary context.'
      authors:
      - name: Ullrich K.K.
      - name: Glytnasi N.E.
      citationCount: 1
      date: '2023-11-01T00:00:00Z'
      journal: Bioinformatics
      title: 'oggmap: a Python package to extract gene ages per orthogroup and link
        them with single-cell RNA data'
    note: null
    pmcid: PMC10663984
    pmid: '37952198'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Gene and protein families
    uri: http://edamontology.org/topic_0623
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:03:57.384386Z'
  biotoolsCURIE: biotools:cirscan
  biotoolsID: cirscan
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: yuna.blum@univ-rennes.fr
    fundrefid: null
    gridid: null
    name: Yuna Blum
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Rose-Marie Fraboulet
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Shiny application to identify differentially active sponge mechanisms
    and visualize circRNA-miRNA-mRNA networks.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene-set enrichment analysis
      uri: http://edamontology.org/operation_2436
    - term: Network visualisation
      uri: http://edamontology.org/operation_3925
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output: []
  homepage: https://gitlab.com/geobioinfo/cirscan_Rshiny
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-16T14:05:09.344226Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: Cirscan
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/s12859-024-05668-y
    metadata:
      abstract: 'Background: Non-coding RNAs represent a large part of the human transcriptome
        and have been shown to play an important role in disease such as cancer. However,
        their biological functions are still incompletely understood. Among non-coding
        RNAs, circular RNAs (circRNAs) have recently been identified for their microRNA
        (miRNA) sponge function which allows them to modulate the expression of miRNA
        target genes by taking on the role of competitive endogenous RNAs (ce-circRNAs).
        Today, most computational tools are not adapted to the search for ce-circRNAs
        or have not been developed for the search for ce-circRNAs from user’s transcriptomic
        data. Results: In this study, we present Cirscan (CIRcular RNA Sponge CANdidates),
        an interactive Shiny application that automatically infers circRNA–miRNA–mRNA
        networks from human multi-level transcript expression data from two biological
        conditions (e.g. tumor versus normal conditions in the case of cancer study)
        in order to identify on a large scale, potential sponge mechanisms active
        in a specific condition. Cirscan ranks each circRNA–miRNA–mRNA subnetwork
        according to a sponge score that integrates multiple criteria based on interaction
        reliability and expression level. Finally, the top ranked sponge mechanisms
        can be visualized as networks and an enrichment analysis is performed to help
        its biological interpretation. We showed on two real case studies that Cirscan
        is capable of retrieving sponge mechanisms previously described, as well as
        identifying potential novel circRNA sponge candidates. Conclusions: Cirscan
        can be considered as a companion tool for biologists, facilitating their ability
        to prioritize sponge mechanisms for experimental validations and identifying
        potential therapeutic targets. Cirscan is implemented in R, released under
        the license GPL-3 and accessible on GitLab (https://gitlab.com/geobioinfo/cirscan_Rshiny
        ). The scripts used in this paper are also provided on Gitlab (https://gitlab.com/geobioinfo/cirscan_paper
        ).'
      authors:
      - name: Fraboulet R.-M.
      - name: Si Ahmed Y.
      - name: Aubry M.
      - name: Corre S.
      - name: Galibert M.-D.
      - name: Blum Y.
      citationCount: 0
      date: '2024-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'Cirscan: a shiny application to identify differentially active sponge
        mechanisms and visualize circRNA–miRNA–mRNA networks'
    note: null
    pmcid: PMC10832221
    pmid: '38302900'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Microarray experiment
    uri: http://edamontology.org/topic_3518
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:01:37.903804Z'
  biotoolsCURIE: biotools:recview
  biotoolsID: recview
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: hongkai.zhang@biol.lu.se
    fundrefid: null
    gridid: null
    name: Hongkai Zhang
    note: null
    orcidid: https://orcid.org/0000-0001-7371-9612
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: bengt.hansson@biol.lu.se
    fundrefid: null
    gridid: null
    name: Bengt Hansson
    note: null
    orcidid: https://orcid.org/0000-0001-6694-8169
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: This R package is designed to distribute the RecView ShinyApp which
    aims at providing a user-friendly GUI for viewing and locating recombination positions
    on chromosomes using pedigree data.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Recombination detection
      uri: http://edamontology.org/operation_0451
    - term: SNP detection
      uri: http://edamontology.org/operation_0484
    - term: Genetic mapping
      uri: http://edamontology.org/operation_0282
    output: []
  homepage: https://github.com/HKyleZhang/RecView.git
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-16T14:01:37.906051Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: RecView
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/S12864-023-09807-2
    metadata:
      abstract: 'Background: Recombination reshuffles alleles at linked loci, allowing
        genes to evolve independently and consequently enhancing the efficiency of
        selection. This makes quantifying recombination along chromosomes an important
        goal for understanding how selection and drift are acting on genes and chromosomes.
        Results: We present RecView, an interactive R application and its homonymous
        R package, to facilitate locating recombination positions along chromosomes
        or scaffolds using whole-genome genotype data of a three-generation pedigree.
        RecView analyses and plots the grandparent-of-origin of all informative alleles
        along each chromosome of the offspring in the pedigree, and infers recombination
        positions with either of two built-in algorithms: one based on change in the
        proportion of the alleles with specific grandparent-of-origin, and one on
        the degree of continuity of alleles with the same grandparent-of-origin. RecView
        handles multiple offspring and chromosomes simultaneously, and all putative
        recombination positions are reported in base pairs together with an estimated
        precision based on the local density of informative alleles. We demonstrate
        RecView using genotype data of a passerine bird with an available reference
        genome, the great reed warbler (Acrocephalus arundinaceus), and show that
        recombination events can be located to specific positions. Conclusions: RecView
        is an easy-to-use and highly effective application for locating recombination
        positions with high precision. RecView is available on GitHub (https://github.com/HKyleZhang/RecView.git).'
      authors:
      - name: Zhang H.
      - name: Hansson B.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: BMC Genomics
      title: 'RecView: an interactive R application for locating recombination positions
        using pedigree data'
    note: null
    pmcid: PMC10676570
    pmid: '38007417'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  - Web application
  topic:
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: DNA replication and recombination
    uri: http://edamontology.org/topic_3127
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T10:56:15.558018Z'
  biotoolsCURIE: biotools:stomicsdb
  biotoolsID: stomicsdb
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: makailong@cngb.org
    fundrefid: null
    gridid: null
    name: Kailong Ma
    note: null
    orcidid: https://orcid.org/0000-0002-9351-104X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: xuxun@genomics.cn
    fundrefid: null
    gridid: null
    name: Xun Xu
    note: null
    orcidid: https://orcid.org/0000-0002-5338-5173
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: weixiaofeng@cngb.org
    fundrefid: null
    gridid: null
    name: Xiaofeng Wei
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Comprehensive database for spatial transcriptomics data sharing, analysis
    and visualization.
  documentation:
  - note: null
    type:
    - User manual
    url: https://stomics-data-archive.readthedocs.io/en/latest/
  download: []
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  function:
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    input: []
    note: null
    operation:
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    - term: Deposition
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    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Essential dynamics
      uri: http://edamontology.org/operation_3891
    output: []
  homepage: https://db.cngb.org/stomics/
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-16T10:56:15.560225Z'
  license: null
  link: []
  maturity: null
  name: STOmicsDB
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  - Mac
  - Linux
  - Windows
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  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD933
    metadata:
      abstract: Recent technological developments in spatial transcriptomics allow
        researchers to measure gene expression of cells and their spatial locations
        at the single-cell level, generating detailed biological insight into biological
        processes. A comprehensive database could facilitate the sharing of spatial
        transcriptomic data and streamline the data acquisition process for researchers.
        Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database
        that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates
        218 manually curated datasets representing 17 species. We annotated cell types,
        identified spatial regions and genes, and performed cell-cell interaction
        analysis for these datasets. STOmicsDB features a user-friendly interface
        for the rapid visualization of millions of cells. To further facilitate the
        reusability and interoperability of spatial transcriptomic data, we developed
        standards for spatial transcriptomic data archiving and constructed a spatial
        transcriptomic data archiving system. Additionally, we offer a distinctive
        capability of customizing dedicated sub-databases in STOmicsDB for researchers,
        assisting them in visualizing their spatial transcriptomic analyses. We believe
        that STOmicsDB could contribute to research insights in the spatial transcriptomics
        field, including data archiving, sharing, visualization and analysis. STOmicsDB
        is freely accessible at https://db.cngb.org/stomics/.
      authors:
      - name: Xu Z.
      - name: Wang W.
      - name: Yang T.
      - name: Li L.
      - name: Ma X.
      - name: Chen J.
      - name: Wang J.
      - name: Huang Y.
      - name: Gould J.
      - name: Lu H.
      - name: Du W.
      - name: Sahu S.K.
      - name: Yang F.
      - name: Li Z.
      - name: Hu Q.
      - name: Hua C.
      - name: Hu S.
      - name: Liu Y.
      - name: Cai J.
      - name: You L.
      - name: Zhang Y.
      - name: Li Y.
      - name: Zeng W.
      - name: Chen A.
      - name: Wang B.
      - name: Liu L.
      - name: Chen F.
      - name: Ma K.
      - name: Xu X.
      - name: Wei X.
      citationCount: 6
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'STOmicsDB: a comprehensive database for spatial transcriptomics data
        sharing, analysis and visualization'
    note: null
    pmcid: PMC10767841
    pmid: '37953328'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Literature and language
    uri: http://edamontology.org/topic_3068
  - term: Biological databases
    uri: http://edamontology.org/topic_3071
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T10:54:23.655728Z'
  biotoolsCURIE: biotools:mlpronto
  biotoolsID: mlpronto
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: btjaden@wellesley.edu
    fundrefid: null
    gridid: null
    name: Brian Tjaden
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jacob Tjaden
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: MLpronto is a tool for rapid, robust, and reproducible machine learning.
    No ML background is needed whatsoever.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Regression analysis
      uri: http://edamontology.org/operation_3659
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: https://mlpronto.org/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-16T10:54:23.657995Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/btjaden/MLpronto
  maturity: null
  name: MLpronto
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1371/JOURNAL.PONE.0294924
    metadata:
      abstract: The democratization of machine learning is a popular and growing movement.
        In a world with a wealth of publicly available data, it is important that
        algorithms for analysis of data are accessible and usable by everyone. We
        present MLpronto, a system for machine learning analysis that is designed
        to be easy to use so as to facilitate engagement with machine learning algorithms.
        With its web interface, MLpronto requires no computer programming or machine
        learning background, and it normally returns results in a matter of seconds.
        As input, MLpronto takes a file of data to be analyzed. MLpronto then executes
        some of the more commonly used supervised machine learning algorithms on the
        data and reports the results of the analyses. As part of its execution, MLpronto
        generates computer programming code corresponding to its machine learning
        analysis, which it also supplies as output. Thus, MLpronto can be used as
        a no-code solution for citizen data scientists with no machine learning or
        programming background, as an educational tool for those learning about machine
        learning, and as a first step for those who prefer to engage with programming
        code in order to facilitate rapid development of machine learning projects.
        MLpronto is freely available for use at https://mlpronto.org/.
      authors:
      - name: Tjaden J.
      - name: Tjaden B.
      citationCount: 0
      date: '2023-11-01T00:00:00Z'
      journal: PLoS ONE
      title: 'MLpronto: A tool for democratizing machine learning'
    note: null
    pmcid: PMC10688639
    pmid: '38032968'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Software engineering
    uri: http://edamontology.org/topic_3372
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2015-08-24T08:35:55Z'
  biotoolsCURIE: biotools:bcftools
  biotoolsID: bcftools
  collectionID:
  - BCFtools
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: https://www.sanger.ac.uk/
  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Support
    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: BCFtools is a set of utilities that manipulate variant calls in the
    Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently
    with both VCFs and BCFs, both uncompressed and BGZF-compressed.
  documentation:
  - note: null
    type:
    - General
    url: http://www.htslib.org/doc/bcftools.html
  - note: A workflow for BCFtools.
    type:
    - Other
    url: http://www.htslib.org/workflow/#mapping_to_variant
  download:
  - note: null
    type: Downloads page
    url: http://www.htslib.org/download/
    version: null
  editPermission:
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence variations
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      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
    note: Multiple data munging operations.
    operation:
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    output:
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: VCF
        uri: http://edamontology.org/format_3016
      - term: BCF
        uri: http://edamontology.org/format_3020
  homepage: http://www.htslib.org/
  homepage_status: 0
  language:
  - C
  lastUpdate: '2024-04-16T10:25:06.228249Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/samtools/bcftools
  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
  - note: null
    type:
    - Issue tracker
    url: https://github.com/samtools/bcftools/issues
  maturity: Mature
  name: BCFtools
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/bioinformatics/btp352
    metadata:
      abstract: 'Summary: The Sequence Alignment/Map (SAM) format is a generic alignment
        format for storing read alignments against reference sequences, supporting
        short and long reads (up to 128 Mbp) produced by different sequencing platforms.
        It is flexible in style, compact in size, efficient in random access and is
        the format in which alignments from the 1000 Genomes Project are released.
        SAM tools implements various utilities for post-processing alignments in the
        SAM format, such as indexing, variant caller and alignment viewer, and thus
        provides universal tools for processing read alignments. © 2009 The Author(s).'
      authors:
      - name: Li H.
      - name: Handsaker B.
      - name: Wysoker A.
      - name: Fennell T.
      - name: Ruan J.
      - name: Homer N.
      - name: Marth G.
      - name: Abecasis G.
      - name: Durbin R.
      citationCount: 37126
      date: '2009-08-01T00:00:00Z'
      journal: Bioinformatics
      title: The Sequence Alignment/Map format and SAMtools
    note: The Sequence Alignment/Map format and SAMtools.
    pmcid: PMC2723002
    pmid: '19505943'
    type:
    - Primary
    version: null
  - doi: 10.1093/gigascience/giab008
    metadata:
      abstract: 'Background: SAMtools and BCFtools are widely used programs for processing
        and analysing high-throughput sequencing data. They include tools for file
        format conversion and manipulation, sorting, querying, statistics, variant
        calling, and effect analysis amongst other methods. Findings: The first version
        appeared online 12 years ago and has been maintained and further developed
        ever since, with many new features and improvements added over the years.
        The SAMtools and BCFtools packages represent a unique collection of tools
        that have been used in numerous other software projects and countless genomic
        pipelines. Conclusion: Both SAMtools and BCFtools are freely available on
        GitHub under the permissive MIT licence, free for both non-commercial and
        commercial use. Both packages have been installed >1 million times via Bioconda.
        The source code and documentation are available from https://www.htslib.org.'
      authors:
      - name: Danecek P.
      - name: Bonfield J.K.
      - name: Liddle J.
      - name: Marshall J.
      - name: Ohan V.
      - name: Pollard M.O.
      - name: Whitwham A.
      - name: Keane T.
      - name: McCarthy S.A.
      - name: Davies R.M.
      citationCount: 3084
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: Twelve years of SAMtools and BCFtools
    note: Twelve years of SAMtools and BCFtools.
    pmcid: PMC7931819
    pmid: '33590861'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: htslib
    type: uses
  toolType:
  - Command-line tool
  - Suite
  topic:
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  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: GWAS study
    uri: http://edamontology.org/topic_3517
  - term: Genotyping experiment
    uri: http://edamontology.org/topic_3516
  validated: 1
  version:
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  - '1.1'
  - '1.2'
  - '1.3'
  - 1.3.1
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - 1.10.1
  - 1.10.2
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - '1.17'
  - '1.18'
  - '1.19'
  - '1.20'
- accessibility: Open access
  additionDate: '2017-01-13T13:16:12Z'
  biotoolsCURIE: biotools:samtools
  biotoolsID: samtools
  collectionID:
  - Rare Disease
  - Animal and Crop Genomics
  - SAMtools
  community:
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      author_name: SAMtools
      author_username: samtools
  confidence_flag: null
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: Richard Durbin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
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    url: null
  - email: samtools@sanger.ac.uk
    fundrefid: null
    gridid: null
    name: Wellcome Sanger Institute
    note: null
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    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
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  - email: null
    fundrefid: null
    gridid: null
    name: Samtools Help mailing list
    note: null
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Support
    url: https://lists.sourceforge.net/lists/listinfo/samtools-help
  description: SAMtools and BCFtools are widely used programs for processing and analysing
    high-throughput sequencing data. They include tools for file format conversion
    and manipulation, sorting, querying, statistics, variant calling, and effect analysis
    amongst other methods.
  documentation:
  - note: HowTos for samtools
    type:
    - Other
    url: http://www.htslib.org/doc/#howtos
  - note: null
    type:
    - User manual
    url: http://www.htslib.org/doc/#manual-pages
  - note: null
    type:
    - Installation instructions
    url: http://www.htslib.org/download/
  download:
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    type: Downloads page
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    version: null
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  function:
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    input:
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    - term: Filtering
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    output:
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      - term: BAM
        uri: http://edamontology.org/format_2572
      - term: SAM
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  homepage: http://www.htslib.org/
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  language:
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  lastUpdate: '2024-04-16T10:22:46.809965Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
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  - note: null
    type:
    - Mailing list
    url: http://www.htslib.org/support/#lists
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    type:
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  maturity: Mature
  name: SAMtools
  operatingSystem:
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  - Windows
  - Mac
  otherID: []
  owner: awhitwham
  publication:
  - doi: 10.1093/bioinformatics/btp352
    metadata:
      abstract: 'Summary: The Sequence Alignment/Map (SAM) format is a generic alignment
        format for storing read alignments against reference sequences, supporting
        short and long reads (up to 128 Mbp) produced by different sequencing platforms.
        It is flexible in style, compact in size, efficient in random access and is
        the format in which alignments from the 1000 Genomes Project are released.
        SAM tools implements various utilities for post-processing alignments in the
        SAM format, such as indexing, variant caller and alignment viewer, and thus
        provides universal tools for processing read alignments. © 2009 The Author(s).'
      authors:
      - name: Li H.
      - name: Handsaker B.
      - name: Wysoker A.
      - name: Fennell T.
      - name: Ruan J.
      - name: Homer N.
      - name: Marth G.
      - name: Abecasis G.
      - name: Durbin R.
      citationCount: 37126
      date: '2009-08-01T00:00:00Z'
      journal: Bioinformatics
      title: The Sequence Alignment/Map format and SAMtools
    note: The Sequence Alignment/Map format and SAMtools.
    pmcid: PMC2723002
    pmid: '19505943'
    type:
    - Primary
    version: null
  - doi: 10.1093/gigascience/giab008
    metadata:
      abstract: 'Background: SAMtools and BCFtools are widely used programs for processing
        and analysing high-throughput sequencing data. They include tools for file
        format conversion and manipulation, sorting, querying, statistics, variant
        calling, and effect analysis amongst other methods. Findings: The first version
        appeared online 12 years ago and has been maintained and further developed
        ever since, with many new features and improvements added over the years.
        The SAMtools and BCFtools packages represent a unique collection of tools
        that have been used in numerous other software projects and countless genomic
        pipelines. Conclusion: Both SAMtools and BCFtools are freely available on
        GitHub under the permissive MIT licence, free for both non-commercial and
        commercial use. Both packages have been installed >1 million times via Bioconda.
        The source code and documentation are available from https://www.htslib.org.'
      authors:
      - name: Danecek P.
      - name: Bonfield J.K.
      - name: Liddle J.
      - name: Marshall J.
      - name: Ohan V.
      - name: Pollard M.O.
      - name: Whitwham A.
      - name: Keane T.
      - name: McCarthy S.A.
      - name: Davies R.M.
      citationCount: 3084
      date: '2021-02-01T00:00:00Z'
      journal: GigaScience
      title: Twelve years of SAMtools and BCFtools
    note: Twelve years of SAMtools and BCFtools.
    pmcid: PMC7931819
    pmid: '33590861'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btr509
    metadata:
      abstract: 'Motivation: Most existing methods for DNA sequence analysis rely
        on accurate sequences or genotypes. However, in applications of the next-generation
        sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample
        low-coverage sequencing or somatic mutation discovery). These applications
        press for the development of new methods for analyzing sequence data with
        uncertainty. Results: We present a statistical framework for calling SNPs,
        discovering somatic mutations, inferring population genetical parameters and
        performing association tests directly based on sequencing data without explicit
        genotyping or linkage-based imputation. On real data, we demonstrate that
        our method achieves comparable accuracy to alternative methods for estimating
        site allele count, for inferring allele frequency spectrum and for association
        mapping. We also highlight the necessity of using symmetric datasets for finding
        somatic mutations and confirm that for discovering rare events, mismapping
        is frequently the leading source of errors. © The Author 2011. Published by
        Oxford University Press. All rights reserved.'
      authors:
      - name: Li H.
      citationCount: 3826
      date: '2011-11-01T00:00:00Z'
      journal: Bioinformatics
      title: A statistical framework for SNP calling, mutation discovery, association
        mapping and population genetical parameter estimation from sequencing data
    note: null
    pmcid: PMC3198575
    pmid: '21903627'
    type: []
    version: null
  relation:
  - biotoolsID: htslib
    type: uses
  toolType:
  - Suite
  - Command-line tool
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
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  - '1.1'
  - '1.2'
  - '1.3'
  - 1.3.1
  - '1.4'
  - 1.4.1
  - '1.5'
  - '1.6'
  - '1.7'
  - '1.8'
  - '1.9'
  - '1.10'
  - '1.11'
  - '1.12'
  - '1.13'
  - '1.14'
  - '1.15'
  - 1.15.1
  - '1.16'
  - 1.16.1
  - '1.17'
  - '1.18'
  - '1.19'
  - 1.19.1
  - 1.19.2
  - '1.20'
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