count: 2216
list:
- accessibility: Open access
  additionDate: '2024-04-19T09:48:49.564191Z'
  biotoolsCURIE: biotools:refmetaplant
  biotoolsID: refmetaplant
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: pchen@cemps.ac.cn
    fundrefid: null
    gridid: null
    name: Ping Chen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: lixuan@sippe.ac.cn
    fundrefid: null
    gridid: null
    name: Xuan Li
    note: null
    orcidid: https://orcid.org/0000-0002-7435-9652
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Reference metabolome database for plants across five major phyla.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Natural product identification
      uri: http://edamontology.org/operation_3803
    - term: Spectral library search
      uri: http://edamontology.org/operation_3801
    - term: Database search
      uri: http://edamontology.org/operation_2421
    output: []
  homepage: https://www.biosino.org/RefMetaDB/
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-19T09:48:49.566259Z'
  license: null
  link: []
  maturity: null
  name: RefMetaPlant
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD980
    metadata:
      abstract: Plants are unique with tremendous chemical diversity and metabolic
        complexity, which is highlighted by estimates that green plants collectively
        produce metabolites numbering in the millions. Plant metabolites play crucial
        roles in all aspects of plant biology, like growth, development, stress responses,
        etc. However, the lack of a reference metabolome for plants, and paucity of
        high-quality standard compound spectral libraries and related analytical tools,
        have hindered the discovery and functional study of phytochemicals in plants.
        Here, by leveraging an advanced LC–MS platform, we generated untargeted mass
        spectral data from >150 plant species collected across the five major phyla.
        Using a self-developed computation protocol, we constructed reference metabolome
        for 153 plant species. A ‘Reference Metabolome Database for Plants’ (RefMetaPlant)
        was built to encompass the reference metabolome, integrated standard compound
        mass spectral libraries for annotation, and related query and analytical tools
        like ‘LC–MS/MS Query’, ‘RefMetaBlast’ and ‘CompoundLibBlast’ for searches
        and profiling of plant metabolome and metabolite identification. Analogous
        to a reference genome in genomic research, RefMetaPlant provides a powerful
        platform to support plant genome-scale metabolite analysis to promote knowledge/data
        sharing and collaboration in the field of metabolomics. RefMetaPlant is freely
        available at https://www.biosino.org/RefMetaDB/.
      authors:
      - name: Shi H.
      - name: Wu X.
      - name: Zhu Y.
      - name: Jiang T.
      - name: Wang Z.
      - name: Li X.
      - name: Liu J.
      - name: Zhang Y.
      - name: Chen F.
      - name: Gao J.
      - name: Xu X.
      - name: Zhang G.
      - name: Xiao N.
      - name: Feng X.
      - name: Zhang P.
      - name: Wu Y.
      - name: Li A.
      - name: Chen P.
      - name: Li X.
      citationCount: 1
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'RefMetaPlant: a reference metabolome database for plants across five
        major phyla'
    note: null
    pmcid: PMC10767953
    pmid: '37953341'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Metabolomics
    uri: http://edamontology.org/topic_3172
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Agricultural science
    uri: http://edamontology.org/topic_3810
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T09:47:27.591034Z'
  biotoolsCURIE: biotools:marferret
  biotoolsID: marferret
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: rgrous83@uw.edu
    fundrefid: null
    gridid: null
    name: R. D. Groussman
    note: null
    orcidid: https://orcid.org/0000-0001-7874-7217
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: armbrust@uw.edu
    fundrefid: null
    gridid: null
    name: E. V. Armbrust
    note: null
    orcidid: https://orcid.org/0000-0001-7865-5101
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Open-source, version-controlled reference library of marine microbial
    eukaryote functional genes.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    - term: Genome annotation
      uri: http://edamontology.org/operation_0362
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output: []
  homepage: https://github.com/armbrustlab/marferret
  homepage_status: 0
  language:
  - Python
  - Shell
  - R
  lastUpdate: '2024-04-19T09:47:27.593586Z'
  license: MIT
  link: []
  maturity: null
  name: MarFERReT
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1038/S41597-023-02842-4
    metadata:
      abstract: Metatranscriptomics generates large volumes of sequence data about
        transcribed genes in natural environments. Taxonomic annotation of these datasets
        depends on availability of curated reference sequences. For marine microbial
        eukaryotes, current reference libraries are limited by gaps in sequenced organism
        diversity and barriers to updating libraries with new sequence data, resulting
        in taxonomic annotation of about half of eukaryotic environmental transcripts.
        Here, we introduce Marine Functional EukaRyotic Reference Taxa (MarFERReT),
        a marine microbial eukaryotic sequence library designed for use with taxonomic
        annotation of eukaryotic metatranscriptomes. We gathered 902 publicly accessible
        marine eukaryote genomes and transcriptomes and assessed their sequence quality
        and cross-contamination issues, selecting 800 validated entries for inclusion
        in MarFERReT. Version 1.1 of MarFERReT contains reference sequences from 800
        marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level
        taxa, totaling nearly 28 million protein sequences with associated NCBI and
        PR2 Taxonomy identifiers and Pfam functional annotations. The MarFERReT project
        repository hosts containerized build scripts, documentation on installation
        and use case examples, and information on new versions of MarFERReT.
      authors:
      - name: Groussman R.D.
      - name: Blaskowski S.
      - name: Coesel S.N.
      - name: Armbrust E.V.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: Scientific Data
      title: MarFERReT, an open-source, version-controlled reference library of marine
        microbial eukaryote functional genes
    note: null
    pmcid: PMC10739892
    pmid: '38129449'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Metatranscriptomics
    uri: http://edamontology.org/topic_3941
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T09:45:45.211711Z'
  biotoolsCURIE: biotools:nps_ms
  biotoolsID: nps_ms
  collectionID: []
  community: null
  confidence_flag: high
  cost: null
  credit:
  - email: dwishart@ualberta.ca
    fundrefid: null
    gridid: null
    name: David S. Wishart
    note: null
    orcidid: https://orcid.org/0000-0002-3207-2434
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Deep learning-enabled MS/MS spectrum prediction facilitates automated
    identification of novel psychoactive substances.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Spectrum calculation
      uri: http://edamontology.org/operation_3860
    - term: de Novo sequencing
      uri: http://edamontology.org/operation_3644
    - term: Peptide database search
      uri: http://edamontology.org/operation_3646
    output: []
  homepage: https://nps-ms.ca/
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-19T09:45:45.213961Z'
  license: null
  link: []
  maturity: null
  name: NPS-MS
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1021/ACS.ANALCHEM.3C02413
    metadata:
      abstract: The market for illicit drugs has been reshaped by the emergence of
        more than 1100 new psychoactive substances (NPS) over the past decade, posing
        a major challenge to the forensic and toxicological laboratories tasked with
        detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary
        method used to screen for NPS within seized materials or biological samples.
        The most contemporary workflows necessitate labor-intensive and expensive
        MS/MS reference standards, which may not be available for recently emerged
        NPS on the illicit market. Here, we present NPS-MS, a deep learning method
        capable of accurately predicting the MS/MS spectra of known and hypothesized
        NPS from their chemical structures alone. NPS-MS is trained by transfer learning
        from a generic MS/MS prediction model on a large data set of MS/MS spectra.
        We show that this approach enables a more accurate identification of NPS from
        experimentally acquired MS/MS spectra than any existing method. We demonstrate
        the application of NPS-MS to identify a novel derivative of phencyclidine
        (PCP) within an unknown powder seized in Denmark without the use of any reference
        standards. We anticipate that NPS-MS will allow forensic laboratories to identify
        more rapidly both known and newly emerging NPS. NPS-MS is available as a web
        server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities
        for given NPS compounds. Additionally, it offers MS/MS spectra identification
        against a vast database comprising approximately 8.7 million predicted NPS
        compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for
        these compounds.
      authors:
      - name: Wang F.
      - name: Pasin D.
      - name: Skinnider M.A.
      - name: Liigand J.
      - name: Kleis J.-N.
      - name: Brown D.
      - name: Oler E.
      - name: Sajed T.
      - name: Gautam V.
      - name: Harrison S.
      - name: Greiner R.
      - name: Foster L.J.
      - name: Dalsgaard P.W.
      - name: Wishart D.S.
      citationCount: 1
      date: '2023-12-19T00:00:00Z'
      journal: Analytical Chemistry
      title: Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated
        Identification Of Novel Psychoactive Substances
    note: null
    pmcid: PMC10733899
    pmid: '38048435'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Toxicology
    uri: http://edamontology.org/topic_2840
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Chemistry
    uri: http://edamontology.org/topic_3314
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T09:11:25.967125Z'
  biotoolsCURIE: biotools:pepmatch
  biotoolsID: pepmatch
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: bpeters@lji.org
    fundrefid: null
    gridid: null
    name: Bjoern Peters
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: A tool to identify short peptide sequence matches in large sets of
    proteins.
  documentation: []
  download:
  - note: null
    type: Source code
    url: https://github.com/IEDB/PEPMatch/tree/master/benchmarking
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Peptide immunogenicity prediction
      uri: http://edamontology.org/operation_0252
    - term: Epitope mapping
      uri: http://edamontology.org/operation_0416
    - term: Database search
      uri: http://edamontology.org/operation_2421
    output: []
  homepage: https://nextgen-tools.iedb.org
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T09:11:25.970036Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/IEDB/PEPMatch
  maturity: null
  name: PEPMatch
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/S12859-023-05606-4
    metadata:
      abstract: 'Background: Numerous tools exist for biological sequence comparisons
        and search. One case of particular interest for immunologists is finding matches
        for linear peptide T cell epitopes, typically between 8 and 15 residues in
        length, in a large set of protein sequences. Both to find exact matches or
        matches that account for residue substitutions. The utility of such tools
        is critical in applications ranging from identifying conservation across viral
        epitopes, identifying putative epitope targets for allergens, and finding
        matches for cancer-associated neoepitopes to examine the role of tolerance
        in tumor recognition. Results: We defined a set of benchmarks that reflect
        the different practical applications of short peptide sequence matching. We
        evaluated a suite of existing methods for speed and recall and developed a
        new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm
        that preprocesses proteomes before searching, achieving a 50-fold increase
        in speed over methods such as the Basic Local Alignment Search Tool (BLAST)
        without compromising recall. PEPMatch’s code and benchmark datasets are publicly
        available. Conclusions: PEPMatch offers significant speed and recall advantages
        for peptide sequence matching. While it is of immediate utility for immunologists,
        the developed benchmarking framework also provides a standard against which
        future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org
        , and the source code can be found at https://github.com/IEDB/PEPMatch .'
      authors:
      - name: Marrama D.
      - name: Chronister W.D.
      - name: Westernberg L.
      - name: Vita R.
      - name: Kosaloglu-Yalcin Z.
      - name: Sette A.
      - name: Nielsen M.
      - name: Greenbaum J.A.
      - name: Peters B.
      citationCount: 1
      date: '2023-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'PEPMatch: a tool to identify short peptide sequence matches in large
        sets of proteins'
    note: null
    pmcid: PMC10726511
    pmid: '38110863'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Immunology
    uri: http://edamontology.org/topic_0804
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T07:51:14.003906Z'
  biotoolsCURIE: biotools:stabjgl
  biotoolsID: stabjgl
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: camilla.lingjaerde@mrc-bsu.cam.ac.uk
    fundrefid: null
    gridid: null
    name: Camilla Lingjærde
    note: null
    orcidid: https://orcid.org/0000-0003-2701-5686
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: A stability approach to sparsity and similarity selection in multiple-network
    reconstruction.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Network analysis
      uri: http://edamontology.org/operation_3927
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    - term: Expression correlation analysis
      uri: http://edamontology.org/operation_3463
    output: []
  homepage: https://github.com/Camiling/stabJGL
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-19T07:51:14.005771Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/Camiling/stabJGL_simulations
  - note: null
    type:
    - Repository
    url: https://github.com/Camiling/stabJGL_analysis
  maturity: null
  name: stabJGL
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOADV/VBAD185
    metadata:
      abstract: 'Motivation: In recent years, network models have gained prominence
        for their ability to capture complex associations. In statistical omics, networks
        can be used to model and study the functional relationships between genes,
        proteins, and other types of omics data. If a Gaussian graphical model is
        assumed, a gene association network can be determined from the non-zero entries
        of the inverse covariance matrix of the data. Due to the high-dimensional
        nature of such problems, integrative methods that leverage similarities between
        multiple graphical structures have become increasingly popular. The joint
        graphical lasso is a powerful tool for this purpose, however, the current
        AIC-based selection criterion used to tune the network sparsities and similarities
        leads to poor performance in high-dimensional settings. Results: We propose
        stabJGL, which equips the joint graphical lasso with a stable and well-performing
        penalty parameter selection approach that combines the notion of model stability
        with likelihood-based similarity selection. The resulting method makes the
        powerful joint graphical lasso available for use in omics settings, and outperforms
        the standard joint graphical lasso, as well as state-of-the-art joint methods,
        in terms of all performance measures we consider. Applying stabJGL to proteomic
        data from a pan-cancer study, we demonstrate the potential for novel discoveries
        the method brings.'
      authors:
      - name: Lingjaerde C.
      - name: Richardson S.
      citationCount: 0
      date: '2023-01-01T00:00:00Z'
      journal: Bioinformatics Advances
      title: 'StabJGL: a stability approach to sparsity and similarity selection in
        multiple-network reconstruction'
    note: null
    pmcid: PMC10751232
    pmid: '38152341'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Experimental design and studies
    uri: http://edamontology.org/topic_3678
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-18T13:34:15.434348Z'
  biotoolsCURIE: biotools:ursa
  biotoolsID: ursa
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: hongweifeng413@163.com
    fundrefid: null
    gridid: null
    name: Weifeng Hong
    note: null
    orcidid: https://orcid.org/0000-0001-6640-2537
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: yumin@gdph.org.cn
    fundrefid: null
    gridid: null
    name: Min Yu
    note: null
    orcidid: https://orcid.org/0000-0003-1875-740X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: xuexin.li@ki.se
    fundrefid: null
    gridid: null
    name: Xuexin Li
    note: null
    orcidid: https://orcid.org/0000-0001-5824-9720
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: A comprehensive multiomics toolbox for high-throughput single-cell
    analysis.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  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: Dimensionality reduction
      uri: http://edamontology.org/operation_3935
    - term: Essential dynamics
      uri: http://edamontology.org/operation_3891
    output: []
  homepage: https://github.com/singlecellomics/ursa
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-18T13:34:15.437534Z'
  license: MIT
  link: []
  maturity: null
  name: Ursa
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/MOLBEV/MSAD267
    metadata:
      abstract: The burgeoning amount of single-cell data has been accompanied by
        revolutionary changes to computational methods to map, quantify, and analyze
        the outputs of these cutting-edge technologies. Many are still unable to reap
        the benefits of these advancements due to the lack of bioinformatics expertise.
        To address this issue, we present Ursa, an automated single-cell multiomics
        R package containing 6 automated single-cell omics and spatial transcriptomics
        workflows. Ursa allows scientists to carry out post-quantification single
        or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics,
        and immunomics at the single-cell level. It serves as a 1-stop analytic solution
        by providing users with outcomes to quality control assessments, multidimensional
        analyses such as dimension reduction and clustering, and extended analyses
        such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims
        bridge the gap between those with bioinformatics expertise and those without
        by providing an easy-To-use bioinformatics package for scientists in hoping
        to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.
      authors:
      - name: Pan L.
      - name: Mou T.
      - name: Huang Y.
      - name: Hong W.
      - name: Yu M.
      - name: Li X.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: Molecular Biology and Evolution
      title: 'Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell
        Analysis'
    note: null
    pmcid: PMC10752348
    pmid: '38091963'
    type: []
    version: null
  relation: []
  toolType:
  - Suite
  topic:
  - term: Immunomics
    uri: http://edamontology.org/topic_3967
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Cytometry
    uri: http://edamontology.org/topic_3934
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-18T12:58:03.200346Z'
  biotoolsCURIE: biotools:iprophos
  biotoolsID: iprophos
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: ranli1993@zju.edu.cn
    fundrefid: null
    gridid: null
    name: Ran Li
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: zhoufangfang@suda.edu.cn
    fundrefid: null
    gridid: null
    name: Fangfang Zhou
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: L_Zhang@zju.edu.cn
    fundrefid: null
    gridid: null
    name: Long Zhang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Web-based interactive platform for integrated proteome and phosphoproteome
    analysis.
  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: Enrichment analysis
      uri: http://edamontology.org/operation_3501
    - term: Expression correlation analysis
      uri: http://edamontology.org/operation_3463
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    - term: Weighted correlation network analysis
      uri: http://edamontology.org/operation_3766
    output: []
  homepage: http://longlab-zju.cn/iProPhos
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-18T12:58:03.202996Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/longLabzj/iProPhos/
  maturity: null
  name: iProPhos
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1016/J.MCPRO.2023.100693
    metadata:
      abstract: Large-scale omics studies have generated a wealth of mass spectrometry–based
        proteomics data, which provide additional insights into disease biology spanning
        genomic boundaries. However, there is a notable lack of web-based analysis
        and visualization tools that facilitate the reutilization of these data. Given
        this challenge, we present iProPhos, a user-friendly web server to deliver
        interactive and customizable functionalities. iProPhos incorporates a large
        number of samples, including 1444 tumor samples and 746 normal samples across
        12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium.
        Additionally, users can also upload their own proteomics/phosphoproteomics
        data for analysis and visualization. In iProPhos, users can perform profiling
        plotting and differential expression, patient survival, clinical feature–related,
        and correlation analyses, including protein–protein, mRNA-protein, and kinase-substrate
        correlations. Furthermore, functional enrichment, protein–protein interaction
        network, and kinase-substrate enrichment analyses are accessible. iProPhos
        displays the analytical results in interactive figures and tables with various
        selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos
        without login requirement. We present two case studies to demonstrate that
        iProPhos can identify potential drug targets and upstream kinases contributing
        to site-specific phosphorylation. Ultimately, iProPhos allows end-users to
        leverage the value of big data in cancer proteomics more effectively and accelerates
        the discovery of novel therapeutic targets.
      authors:
      - name: Zou J.
      - name: Qin Z.
      - name: Li R.
      - name: Yan X.
      - name: Huang H.
      - name: Yang B.
      - name: Zhou F.
      - name: Zhang L.
      citationCount: 0
      date: '2024-01-01T00:00:00Z'
      journal: Molecular and Cellular Proteomics
      title: 'iProPhos: A Web-Based Interactive Platform for Integrated Proteome and
        Phosphoproteome Analysis'
    note: null
    pmcid: PMC10828474
    pmid: '38097182'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-18T12:51:11.920346Z'
  biotoolsCURIE: biotools:pooe
  biotoolsID: pooe
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: shipingyang@cau.edu.cn
    fundrefid: null
    gridid: null
    name: Shiping Yang
    note: null
    orcidid: https://orcid.org/0009-0002-0464-4279
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: zidingzhang@cau.edu.cn
    fundrefid: null
    gridid: null
    name: Ziding Zhang
    note: null
    orcidid: https://orcid.org/0000-0002-9296-571X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Predicting oomycete effectors based on a pre-trained large protein
    language model.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein modelling
      uri: http://edamontology.org/operation_0477
    - term: Protein secondary structure prediction
      uri: http://edamontology.org/operation_0267
    - term: Virulence prediction
      uri: http://edamontology.org/operation_3461
    output: []
  homepage: http://zzdlab.com/pooe/index.php
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-18T12:51:11.923773Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/zzdlabzm/POOE
  maturity: null
  name: POOE
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1128/MSYSTEMS.01004-23
    metadata:
      abstract: Oomycetes are fungus-like eukaryotic microorganisms which can cause
        catastrophic diseases in many plants. Successful infection of oomycetes depends
        highly on their effector proteins that are secreted into plant cells to subvert
        plant immunity. Thus, systematic identification of effectors from the oomycete
        proteomes remains an initial but crucial step in understanding plant–pathogen
        relationships. However, the number of experimentally identified oomycete effectors
        is still limited. Currently, only a few bioinformatics predictors exist to
        detect potential effectors, and their prediction performance needs to be improved.
        Here, we used the sequence embeddings from a pre-trained large protein language
        model (ProtTrans) as input and developed a support vector machine-based method
        called POOE for predicting oomycete effectors. POOE could achieve a highly
        accurate performance with an area under the precision-recall curve of 0.804
        (area under the receiver operating characteristic curve = 0.893, accuracy
        = 0.874, precision = 0.777, recall = 0.684, and specificity = 0.936) in the
        fivefold cross-validation, considerably outperforming various combinations
        of popular machine learning algorithms and other commonly used sequence encoding
        schemes. A similar prediction performance was also observed in the independent
        test. Compared with the existing oomycete effector prediction methods, POOE
        provided very competitive and promising performance, suggesting that ProtTrans
        effectively captures rich protein semantic information and dramatically improves
        the prediction task. We anticipate that POOE can accelerate the identification
        of oomycete effectors and provide new hints to systematically understand the
        functional roles of effectors in plant–pathogen interactions. The web server
        of POOE is freely accessible at http://zzdlab.com/pooe/index.php. The corresponding
        source codes and data sets are also available at https://github.com/zzdlabzm/POOE.
        IMPORTANCE In this work, we use the sequence representations from a pre-trained
        large protein language model (ProtTrans) as input and develop a Support Vector
        Machine-based method called POOE for predicting oomycete effectors. POOE could
        achieve a highly accurate performance in the independent test set, considerably
        outperforming existing oomycete effector prediction methods. We expect that
        this new bioinformatics tool will accelerate the identification of oomycete
        effectors and further guide the experimental efforts to interrogate the functional
        roles of effectors in plant-pathogen interaction.
      authors:
      - name: Zhao M.
      - name: Lei C.
      - name: Zhou K.
      - name: Huang Y.
      - name: Fu C.
      - name: Yang S.
      - name: Zhang Z.
      citationCount: 0
      date: '2024-01-01T00:00:00Z'
      journal: mSystems
      title: 'POOE: predicting oomycete effectors based on a pre-trained large protein
        language model'
    note: null
    pmcid: PMC10804963
    pmid: '38078741'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Natural language processing
    uri: http://edamontology.org/topic_0218
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  validated: 0
  version: []
- 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: null
  additionDate: '2019-11-14T19:27:27Z'
  biotoolsCURIE: biotools:PolySTest
  biotoolsID: PolySTest
  collectionID: []
  community: null
  confidence_flag: tool
  cost: null
  credit: []
  description: Robust statistical testing of quantitative proteomics data. The tool
    comprises and combines different tests and includes a new method for combined
    statistics of quantitative and missing values.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Expression data
        uri: http://edamontology.org/data_2603
      format:
      - term: DSV
        uri: http://edamontology.org/format_3751
      - term: CSV
        uri: http://edamontology.org/format_3752
    note: null
    operation:
    - term: Statistical modelling
      uri: http://edamontology.org/operation_3664
    - term: Differential protein expression profiling
      uri: http://edamontology.org/operation_3741
    output:
    - data:
        term: Q-value
        uri: http://edamontology.org/data_3932
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: P-value
        uri: http://edamontology.org/data_1669
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
    - data:
        term: Heat map
        uri: http://edamontology.org/data_1636
      format:
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Histogram
        uri: http://edamontology.org/data_3905
      format:
      - term: SVG
        uri: http://edamontology.org/format_3604
    - data:
        term: Gene expression profile
        uri: http://edamontology.org/data_0928
      format:
      - term: CSV
        uri: http://edamontology.org/format_3752
  homepage: http://computproteomics.bmb.sdu.dk/Apps/PolySTest
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-18T08:55:10.802514Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://bitbucket.org/veitveit/polystest
  maturity: null
  name: PolySTest
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: veits@bmb.sdu.dk
  publication:
  - doi: 10.1074/mcp.RA119.001777
    metadata:
      abstract: Statistical testing remains one of the main challenges for high-confidence
        detection of differentially regulated proteins or peptides in large-scale
        quantitative proteomics experiments by mass spectrometry. Statistical tests
        need to be sufficiently robust to deal with experiment intrinsic data structures
        and variations and often also reduced feature coverage across different biological
        samples due to ubiquitous missing values. A robust statistical test provides
        accurate confidence scores of large-scale proteomics results, regardless of
        instrument platform, experimental protocol and software tools. However, the
        multitude of different combinations of experimental strategies, mass spectrometry
        techniques and informatics methods complicate the decision of choosing appropriate
        statistical approaches. We address this challenge by introducing PolySTest,
        a user-friendly web service for statistical testing, data browsing and data
        visualization. We introduce a new method, Miss test, that simultaneously tests
        for missingness and feature abundance, thereby complementing common statistical
        tests by rescuing otherwise discarded data features. We demonstrate that PolySTest
        with integrated Miss test achieves higher confidence and higher sensitivity
        for artificial and experimental proteomics data sets with known ground truth.
        Application of PolySTest to mass spectrometry based large-scale proteomics
        data obtained from differentiating muscle cells resulted in the rescue of
        10%-20% additional proteins in the identified molecular networks relevant
        to muscle differentiation. We conclude that PolySTest is a valuable addition
        to existing tools and instrument enhancements that improve coverage and depth
        of large-scale proteomics experiments. A fully functional demo version of
        PolySTest and Miss test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.
      authors:
      - name: Schwammle V.
      - name: Hagensen C.E.
      - name: Rogowska-Wrzesinska A.
      - name: Jensen O.N.
      citationCount: 14
      date: '2020-08-01T00:00:00Z'
      journal: Molecular and Cellular Proteomics
      title: 'PolySTest: Robust statistical testing of proteomics data with missing
        values improves detection of biologically relevant features'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  validated: 1
  version: []
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