count: 11
list:
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  additionDate: '2015-05-04T12:18:09Z'
  biotoolsCURIE: biotools:featurep
  biotoolsID: featurep
  collectionID: []
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: CBS
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: hnielsen@cbs.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Comparative analysis of protein sequences derived from the same transcript
    by alternative splicing, genome variation data (SNPs) or protein families from
    one or more organisms.
  documentation:
  - note: null
    type:
    - General
    url: http://www.cbs.dtu.dk/services/EPipe/instructions.php
  download: []
  editPermission:
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  function:
  - cmd: null
    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Sequence comparison
      uri: http://edamontology.org/operation_2451
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Sequence report
        uri: http://edamontology.org/data_2955
      format: []
  homepage: http://cbs.dtu.dk/services/EPipe/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-25T16:03:40.141479Z'
  license: Other
  link:
  - note: null
    type:
    - Software catalogue
    url: http://cbs.dtu.dk/services
  maturity: Emerging
  name: FeatureP
  operatingSystem:
  - Linux
  otherID: []
  owner: CBS
  publication:
  - doi: 10.1016/j.sbi.2010.03.008
    metadata: null
    note: null
    pmcid: null
    pmid: '20403684'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Web service
  topic:
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  validated: 1
  version:
  - '0.983'
- accessibility: null
  additionDate: '2015-10-01T21:33:09Z'
  biotoolsCURIE: biotools:loctree3
  biotoolsID: loctree3
  collectionID:
  - Rostlab tools
  - PredictProtein
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Maximilian Hecht
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Guy Yachdav
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Timothy Karl
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Tobias Hamp
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Tatyana Goldberg
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Burkhard Rost
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Technische Universität München
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Alexander von Humboldt Foundation through German Federal Ministry for Education
      and Research
    note: null
    orcidid: null
    rorid: null
    typeEntity: Funding agency
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ernst Ludwig Ehrlich Studienwerk
    note: null
    orcidid: null
    rorid: null
    typeEntity: Funding agency
    typeRole:
    - Contributor
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: RostLab
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: localization@rostlab.org
    fundrefid: null
    gridid: null
    name: null
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://rostlab.org/services/loctree3/
  description: Prediction of protein subcellular localization in 18 classes for eukaryota,
    6 for bacteria and 3 for archaea.
  documentation:
  - note: null
    type:
    - General
    url: https://rostlab.org/owiki/index.php/Loctree3
  download:
  - note: null
    type: Source code
    url: https://rostlab.org/owiki/index.php/Packages
    version: null
  - note: null
    type: Binaries
    url: https://rostlab.org/owiki/index.php/Packages
    version: null
  editPermission:
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: 'Prediction of protein sucellular localization in 18 classes for eukaryota,
      6 classes for bacteria and 3 for archaea using homology searches (PSI-BLAST)
      and machine learning (SVM) User can provide one or more sequences in FASTA format
      The prediction output contains: prediction score, from 1 (weak prediction) to
      100 (strong prediction); one of 18 localization classes for eukaryota, 6 for
      bacteria and 3 for archaea; GO identifier; GO term; prediction source (PSI-BLAST
      or SVM)'
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: https://rostlab.org/services/loctree3/
  homepage_status: 0
  language:
  - PHP
  - Java
  - Perl
  - JavaScript
  lastUpdate: '2024-11-25T15:45:41.322065Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://rostlab.org/owiki/index.php/Packages
  maturity: Mature
  name: LocTree3
  operatingSystem:
  - Linux
  otherID: []
  owner: RostLab
  publication:
  - doi: 10.1093/nar/gku396
    metadata: null
    note: null
    pmcid: PMC4086075
    pmid: '24848019'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/bts390
    metadata: null
    note: null
    pmcid: PMC3436817
    pmid: '22962467'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  validated: 1
  version:
  - 1.0.8
- accessibility: null
  additionDate: '2015-01-21T13:29:24Z'
  biotoolsCURIE: biotools:secretomep
  biotoolsID: secretomep
  collectionID: []
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: CBS
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: hnielsen@cbs.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: nblom@kt.dtu.dk
    fundrefid: null
    gridid: null
    name: Nikolaj Sorgenfrei Blom
    note: null
    orcidid: http://orcid.org/0000-0001-7787-7853
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Predictions of non-classical (i.e. not signal peptide triggered) protein
    secretion.
  documentation:
  - note: null
    type:
    - General
    url: http://www.cbs.dtu.dk/services/SecretomeP/instructions.php
  download: []
  editPermission:
    authors: []
    type: private
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: predicts of non-classical protein secretion
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://cbs.dtu.dk/services/SecretomeP/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-25T14:25:42.955245Z'
  license: Other
  link:
  - note: null
    type:
    - Software catalogue
    url: http://cbs.dtu.dk/services
  maturity: Emerging
  name: SecretomeP
  operatingSystem:
  - Linux
  otherID: []
  owner: CBS
  publication:
  - doi: 10.1093/protein/gzh037
    metadata:
      abstract: We present a sequence-based method, SecretomeP, for the prediction
        of mammalian secretory proteins targeted to the non-classical secretory pathway,
        i.e. proteins without an N-terminal signal peptide. So far only a limited
        number of proteins have been shown experimentally to enter the non-classical
        secretory pathway. These are mainly fibroblast growth factors, interleukins
        and galectins found in the extracellular matrix. We have discovered that certain
        pathway-independent features are shared among secreted proteins. The method
        presented here is also capable of predicting (signal peptide-containing) secretory
        proteins where only the mature part of the protein has been annotated or cases
        where the signal peptide remains uncleaved. By scanning the entire human proteome
        we identified new proteins potentially undergoing non-classical secretion.
        Predictions can be made at http://www.cbs.dtu.dk/services/SecretomeP.
      authors:
      - name: Bendtsen J.D.
      - name: Jensen L.J.
      - name: Blom N.
      - name: Von Heijne G.
      - name: Brunak S.
      citationCount: 1010
      date: '2004-04-01T00:00:00Z'
      journal: Protein Engineering, Design and Selection
      title: Feature-based prediction of non-classical and leaderless protein secretion
    note: null
    pmcid: null
    pmid: '15115854'
    type:
    - Other
    version: null
  - doi: 10.1186/1471-2180-5-58
    metadata:
      abstract: 'Background: We present an overview of bacterial non-classical secretion
        and a prediction method for identification of proteins following signal peptide
        independent secretion pathways. We have compiled a list of proteins found
        extracellularly despite the absence of a signal peptide. Some of these proteins
        also have known roles in the cytoplasm, which means they could be so-called
        "moon-lightning" proteins having more than one function. Results: A thorough
        literature search was conducted to compile a list of currently known bacterial
        non-classically secreted proteins. Pattern finding methods were applied to
        the sequences in order to identify putative signal sequences or motifs responsible
        for their secretion. We have found no signal or motif characteristic to any
        majority of the proteins in the compiled list of non-classically secreted
        proteins, and conclude that these proteins, indeed, seem to be secreted in
        a novel fashion. However, we also show that the apparently non-classically
        secreted proteins are still distinguished from cellular proteins by properties
        such as amino acid composition, secondary structure and disordered regions.
        Specifically, prediction of disorder reveals that bacterial secretory proteins
        are more structurally disordered than their cytoplasmic counterparts. Finally,
        artificial neural networks were used to construct protein feature based methods
        for identification of non-classically secreted proteins in both Gram-positive
        and Gram-negative bacteria. Conclusion: We present a publicly available prediction
        method capable of discriminating between this group of proteins and other
        proteins, thus allowing for the identification of novel non-classically secreted
        proteins. We suggest candidates for non-classically secreted proteins in Escherichia
        coli and Bacillus subtilis. The prediction method is available online. © 2005
        Bendtsen et al; licensee BioMed Central Ltd.'
      authors:
      - name: Bendtsen J.D.
      - name: Kiemer L.
      - name: Fausboll A.
      - name: Brunak S.
      citationCount: 570
      date: '2005-10-07T00:00:00Z'
      journal: BMC Microbiology
      title: Non-classical protein secretion in bacteria
    note: null
    pmcid: PMC1266369
    pmid: '16212653'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2022-05-19T21:53:05.338767Z'
  biotoolsCURIE: biotools:netsolp
  biotoolsID: netsolp
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Alexander Rosenberg Johansen
    note: null
    orcidid: https://orcid.org/0000-0002-4993-7916
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: https://orcid.org/0000-0002-9412-9643
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jose J Almagro Armenteros
    note: null
    orcidid: https://orcid.org/0000-0003-0111-1362
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Prediction of solubility and usability of proteins expressed in E.
    coli
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Protein solubility prediction
      uri: http://edamontology.org/operation_0409
    output: []
  homepage: https://services.healthtech.dtu.dk/service.php?NetSolP
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T20:09:15.780950Z'
  license: null
  link: []
  maturity: null
  name: NetSolP
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1093/bioinformatics/btab801
    metadata:
      abstract: 'Motivation: Solubility and expression levels of proteins can be a
        limiting factor for large-scale studies and industrial production. By determining
        the solubility and expression directly from the protein sequence, the success
        rate of wet-lab experiments can be increased. Results: In this study, we focus
        on predicting the solubility and usability for purification of proteins expressed
        in Escherichia coli directly from the sequence. Our model NetSolP is based
        on deep learning protein language models called transformers and we show that
        it achieves state-of-the-art performance and improves extrapolation across
        datasets. As we find current methods are built on biased datasets, we curate
        existing datasets by using strict sequence-identity partitioning and ensure
        that there is minimal bias in the sequences.'
      authors:
      - name: Thumuluri V.
      - name: Martiny H.-M.
      - name: Almagro Armenteros J.J.
      - name: Salomon J.
      - name: Nielsen H.
      - name: Johansen A.R.
      citationCount: 22
      date: '2022-02-15T00:00:00Z'
      journal: Bioinformatics
      title: 'NetSolP: predicting protein solubility in Escherichia coli using language
        models'
    note: null
    pmcid: null
    pmid: '35088833'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Protein expression
    uri: http://edamontology.org/topic_0108
  - term: Literature and language
    uri: http://edamontology.org/topic_3068
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2022-09-05T20:29:10.393972Z'
  biotoolsCURIE: biotools:netsurfp-3.0
  biotoolsID: netsurfp-3.0
  collectionID: []
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: paolo.marcatili@gmail.com
    fundrefid: null
    gridid: null
    name: Paolo Marcatili
    note: null
    orcidid: https://orcid.org/0000-0003-2615-5695
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Jeppe Hallgren
    note: null
    orcidid: https://orcid.org/0000-0001-5377-2643
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: https://orcid.org/0000-0002-9412-9643
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Ole Winther
    note: null
    orcidid: https://orcid.org/0000-0002-1966-3205
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Morten Nielsen
    note: null
    orcidid: https://orcid.org/0000-0001-7885-4311
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Bent Petersen
    note: null
    orcidid: https://orcid.org/0000-0002-2472-8317
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Magnus Haraldson Høie
    note: null
    orcidid: https://orcid.org/0000-0003-2567-758X
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Erik Nicolas Kiehl
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Accurate and fast prediction of protein structural features by protein
    language models and deep learning.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Protein secondary structure comparison
      uri: http://edamontology.org/operation_2488
    - term: Protein secondary structure prediction
      uri: http://edamontology.org/operation_0267
    - term: Protein geometry calculation
      uri: http://edamontology.org/operation_0249
    output: []
  homepage: https://services.healthtech.dtu.dk/service.php?NetSurfP-3.0
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T15:42:12.857950Z'
  license: null
  link:
  - note: null
    type:
    - Other
    url: https://dtu.biolib.com/nsp3
  maturity: null
  name: NetSurfP-3.0
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Jennifer
  publication:
  - doi: 10.1093/nar/gkac439
    metadata:
      abstract: Recent advances in machine learning and natural language processing
        have made it possible to profoundly advance our ability to accurately predict
        protein structures and their functions. While such improvements are significantly
        impacting the fields of biology and biotechnology at large, such methods have
        the downside of high demands in terms of computing power and runtime, hampering
        their applicability to large datasets. Here, we present NetSurfP-3.0, a tool
        for predicting solvent accessibility, secondary structure, structural disorder
        and backbone dihedral angles for each residue of an amino acid sequence. This
        NetSurfP update exploits recent advances in pre-trained protein language models
        to drastically improve the runtime of its predecessor by two orders of magnitude,
        while displaying similar prediction performance. We assessed the accuracy
        of NetSurfP-3.0 on several independent test datasets and found it to consistently
        produce state-of-the-art predictions for each of its output features, with
        a runtime that is up to to 600 times faster than the most commonly available
        methods performing the same tasks. The tool is freely available as a web server
        with a user-friendly interface to navigate the results, as well as a standalone
        downloadable package.
      authors:
      - name: Hoie M.H.
      - name: Kiehl E.N.
      - name: Petersen B.
      - name: Nielsen M.
      - name: Winther O.
      - name: Nielsen H.
      - name: Hallgren J.
      - name: Marcatili P.
      citationCount: 106
      date: '2022-07-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'NetSurfP-3.0: accurate and fast prediction of protein structural features
        by protein language models and deep learning'
    note: null
    pmcid: PMC9252760
    pmid: '35648435'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein secondary structure
    uri: http://edamontology.org/topic_3542
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Biotechnology
    uri: http://edamontology.org/topic_3297
  - term: Protein structural motifs and surfaces
    uri: http://edamontology.org/topic_0166
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  validated: 1
  version: []
- accessibility: null
  additionDate: '2017-08-25T13:20:00Z'
  biotoolsCURIE: biotools:deeploc
  biotoolsID: deeploc
  collectionID: []
  confidence_flag: null
  cost: null
  credit:
  - email: hnielsen@cbs.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Prediction of eukaryotic protein subcellular localization using deep
    learning.
  documentation:
  - note: null
    type:
    - User manual
    url: http://www.cbs.dtu.dk/services/DeepLoc/instructions.php
  download: []
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    input:
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      format:
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        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format:
      - term: PNG
        uri: http://edamontology.org/format_3603
  homepage: http://www.cbs.dtu.dk/services/DeepLoc/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T15:23:53.929740Z'
  license: Other
  link: []
  maturity: Mature
  name: DeepLoc
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  - Linux
  - Windows
  - Mac
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  owner: lidiamorrone
  publication:
  - doi: 10.1093/bioinformatics/btx431
    metadata:
      abstract: 'Motivation: The prediction of eukaryotic protein subcellular localization
        is a well-studied topic in bioinformatics due to its relevance in proteomics
        research. Many machine learning methods have been successfully applied in
        this task, but in most of them, predictions rely on annotation of homologues
        from knowledge databases. For novel proteins where no annotated homologues
        exist, and for predicting the effects of sequence variants, it is desirable
        to have methods for predicting protein properties from sequence information
        only. Results: Here, we present a prediction algorithm using deep neural networks
        to predict protein subcellular localization relying only on sequence information.
        At its core, the prediction model uses a recurrent neural network that processes
        the entire protein sequence and an attention mechanism identifying protein
        regions important for the subcellular localization. The model was trained
        and tested on a protein dataset extracted from one of the latest UniProt releases,
        in which experimentally annotated proteins follow more stringent criteria
        than previously. We demonstrate that our model achieves a good accuracy (78%
        for 10 categories; 92% for membrane-bound or soluble), outperforming current
        state-of-the-art algorithms, including those relying on homology information.
        Availability and implementation: The method is available as a web server at
        http://www.cbs.dtu.dk/services/DeepLoc. Example code is available at https://github.com/JJAlmagro/subcellular_localization.
        The dataset is available at http://www.cbs.dtu.dk/services/DeepLoc/data.php.
        Contact: jjalma@dtu.dk.'
      authors:
      - name: Almagro Armenteros J.J.
      - name: Sonderby C.K.
      - name: Sonderby S.K.
      - name: Nielsen H.
      - name: Winther O.
      citationCount: 757
      date: '2017-11-01T00:00:00Z'
      journal: Bioinformatics (Oxford, England)
      title: 'DeepLoc: prediction of protein subcellular localization using deep learning'
    note: null
    pmcid: null
    pmid: '29036616'
    type: []
    version: null
  relation: []
  toolType:
  - Web service
  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2015-12-17T14:23:00Z'
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  cost: Free of charge (with restrictions)
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    gridid: null
    name: TN Petersen
    note: null
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    typeRole:
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    url: null
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    typeEntity: Institute
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    url: null
  - email: hnielsen@cbs.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: http://orcid.org/0000-0002-9412-9643
    rorid: null
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    typeRole:
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    url: null
  - email: hnielsen@cbs.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: http://orcid.org/0000-0002-9412-9643
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Prediction of the presence and location of signal peptide cleavage
    sites in amino acid sequences from different organisms.
  documentation:
  - note: null
    type:
    - General
    url: http://www.cbs.dtu.dk/services/SignalP
  download:
  - note: null
    type: Source code
    url: http://www.cbs.dtu.dk/cgi-bin/sw_request?signalp
    version: null
  - note: null
    type: Binaries
    url: http://www.cbs.dtu.dk/cgi-bin/sw_request?signalp
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    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: predicts the presence and location of signal peptide cleavage sites in amino
      acid sequences from different organisms
    operation:
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: GFF
        uri: http://edamontology.org/format_2305
    - data:
        term: Sequence report
        uri: http://edamontology.org/data_2955
      format: []
  homepage: http://cbs.dtu.dk/services/SignalP/
  homepage_status: 0
  language: []
  lastUpdate: '2024-11-24T14:37:27.222979Z'
  license: Other
  link:
  - note: null
    type:
    - Repository
    url: http://www.cbs.dtu.dk/cgi-bin/sw_request?signalp
  maturity: Mature
  name: SignalP
  operatingSystem:
  - Linux
  - Mac
  otherID:
  - type: rrid
    value: rrid:SCR_015644
    version: null
  owner: lidiamorrone
  publication:
  - doi: 10.1038/nmeth.1701
    metadata:
      abstract: ''
      authors:
      - name: Petersen T.N.
      - name: Brunak S.
      - name: Von Heijne G.
      - name: Nielsen H.
      citationCount: 7535
      date: '2011-10-01T00:00:00Z'
      journal: Nature Methods
      title: 'SignalP 4.0: Discriminating signal peptides from transmembrane regions'
    note: null
    pmcid: null
    pmid: '21959131'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  validated: 1
  version:
  - '4.1'
- accessibility: null
  additionDate: '2015-01-21T13:29:25Z'
  biotoolsCURIE: biotools:tatp
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  collectionID: []
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  cost: Free of charge (with restrictions)
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    note: null
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    url: null
  - email: hnielsen@cbs.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: http://orcid.org/0000-0002-9412-9643
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  description: Prediction of the presence and location of Twin-arginine signal peptide
    cleavage sites in bacteria.
  documentation:
  - note: null
    type:
    - General
    url: http://www.cbs.dtu.dk/services/TatP/instructions.php
  download: []
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  function:
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    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: predicts the presence and location of Twin-arginine signal peptide cleavage
      sites in bacteria. Signal peptide/non-signal peptide prediction based on a combination
      of two artificial neural networks
    operation:
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    output:
    - data:
        term: Sequence report
        uri: http://edamontology.org/data_2955
      format:
      - term: Binary format
        uri: http://edamontology.org/format_2333
  homepage: http://cbs.dtu.dk/services/TatP/
  homepage_status: 0
  language: []
  lastUpdate: '2018-12-16T14:15:31Z'
  license: Other
  link:
  - note: null
    type:
    - Software catalogue
    url: http://cbs.dtu.dk/services
  maturity: Emerging
  name: TatP
  operatingSystem:
  - Linux
  otherID: []
  owner: CBS
  publication:
  - doi: 10.1186/1471-2105-6-167
    metadata:
      abstract: 'Background: Proteins carrying twin-arginine (Tat) signal peptides
        are exported into the periplasmic compartment or extracellular environment
        independently of the classical Sec-dependent translocation pathway. To complement
        other methods for classical signal peptide prediction we here present a publicly
        available method, TatP, for prediction of bacterial Tat signal peptides. Results:
        We have retrieved sequence data for Tat substrates in order to train a computational
        method for discrimination of Sec and Tat signal peptides. The TatP method
        is able to positively classify 91% of 35 known Tat signal peptides and 84%
        of the annotated cleavage sites of these Tat signal peptides were correctly
        predicted. This method generates far less false positive predictions on various
        datasets than using simple pattern matching. Moreover, on the same datasets
        TatP generates less false positive predictions than a complementary rule based
        prediction method. Conclusion: The method developed here is able to discriminate
        Tat signal peptides from cytoplasmic proteins carrying a similar motif, as
        well as from Sec signal peptides, with high accuracy. The method allows filtering
        of input sequences based on Perl syntax regular expressions, whereas hydrophobicity
        discrimination of Tat- and Sec-signal peptides is carried out by an artificial
        neural network. A potential cleavage site of the predicted Tat signal peptide
        is also reported. The TatP prediction server is available as a public web
        server at http://www.cbs.dtu.dk/ services/TatP/. © 2005 Bendtsen et al; licensee
        BioMed Central Ltd.'
      authors:
      - name: Bendtsen J.D.
      - name: Nielsen H.
      - name: Widdick D.
      - name: Palmer T.
      - name: Brunak S.
      citationCount: 377
      date: '2005-07-02T00:00:00Z'
      journal: BMC Bioinformatics
      title: Prediction of twin-arginine signal peptides
    note: null
    pmcid: PMC1182353
    pmid: '15992409'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Web service
  topic:
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2015-01-21T13:29:15Z'
  biotoolsCURIE: biotools:netacet
  biotoolsID: netacet
  collectionID: []
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    fundrefid: null
    gridid: null
    name: CBS
    note: null
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    typeEntity: Institute
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    url: null
  - email: hnielsen@cbs.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: nblom@kt.dtu.dk
    fundrefid: null
    gridid: null
    name: Nicolaj Sorgenfrei Blom
    note: null
    orcidid: http://orcid.org/0000-0001-7787-7853
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Prediction of substrates of N-acetyltransferase A (NatA).
  documentation:
  - note: null
    type:
    - General
    url: http://www.cbs.dtu.dk/services/NetAcet/
  download: []
  editPermission:
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  function:
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    input:
    - data:
        term: Sequence
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      format:
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        uri: http://edamontology.org/format_1929
    note: predicts substrates of N-acetyltransferase A (NatA)
    operation:
    - term: Prediction and recognition
      uri: http://edamontology.org/operation_2423
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://cbs.dtu.dk/services/NetAcet/
  homepage_status: 0
  language: []
  lastUpdate: '2018-12-16T13:48:08Z'
  license: Other
  link:
  - note: null
    type:
    - Software catalogue
    url: http://cbs.dtu.dk/services
  maturity: Emerging
  name: NetAcet
  operatingSystem:
  - Linux
  otherID: []
  owner: CBS
  publication:
  - doi: 10.1093/bioinformatics/bti130
    metadata:
      abstract: 'Summary: We present here a neural network based method for prediction
        of N-terminal acetylation - by far the most abundant post-translational modification
        in eukaryotes. The method was developed on a yeast dataset for N-acetyltransferase
        A (NatA) acetylation, which is the type of N-acetylation for which most examples
        are known and for which orthologs have been found in several eukaryotes. We
        obtain correlation coefficients close to 0.7 on yeast data and a sensitivity
        up to 74% on mammalian data, suggesting that the method is valid for eukaryotic
        NatA orthologs. © The Author 2004. Published by Oxford University Press. All
        rights reserved.'
      authors:
      - name: Kiemer L.
      - name: Bendtsen J.D.
      - name: Blom N.
      citationCount: 103
      date: '2005-04-01T00:00:00Z'
      journal: Bioinformatics
      title: 'NetAcet: Prediction of N-terminal acetylation sites'
    note: null
    pmcid: null
    pmid: '15539450'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2015-06-29T10:27:52Z'
  biotoolsCURIE: biotools:targetp
  biotoolsID: targetp
  collectionID: []
  confidence_flag: null
  cost: Free of charge (with restrictions)
  credit:
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    name: CBS
    note: null
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    typeEntity: Institute
    typeRole:
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    url: null
  - email: hnielsen@cbs.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: http://orcid.org/0000-0002-9412-9643
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Prediction of the subcellular location of eukaryotic proteins.
  documentation:
  - note: null
    type:
    - General
    url: http://www.cbs.dtu.dk/services/TargetP/instructions.php
  download: []
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  function:
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    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Predicts the subcellular location of eukaryotic proteins
    operation:
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://cbs.dtu.dk/services/TargetP/
  homepage_status: 0
  language: []
  lastUpdate: '2018-12-14T09:42:48Z'
  license: Other
  link:
  - note: null
    type:
    - Software catalogue
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  maturity: Emerging
  name: TargetP
  operatingSystem:
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  owner: CBS
  publication:
  - doi: 10.1006/jmbi.2000.3903
    metadata:
      abstract: A neural network-based tool, TargetP, for large-scale subcellular
        location prediction of newly identified proteins has been developed. Using
        N-terminal sequence information only, it discriminates between proteins destined
        for the mitochondrion, the chloroplast, the secretory pathway, and 'other'
        localizations with a success rate of 85% (plant) or 90% (non-plant) on redundancy-reduced
        test sets. From a TargetP analysis of the recently sequenced Arabidopsis thaliana
        chromosomes 2 and 4 and the Ensembl Homo sapiens protein set, we estimate
        that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and
        that the abundance of secretory proteins, in both Arabidopsis and Homo, is
        around 10%. TargetP also predicts cleavage sites with levels of correctly
        predicted sites ranging from approximately 40% to 50% (chloroplastic and mitochondrial
        presequences) to above 70% (secretory signal peptides). TargetP is available
        as a web-server at http://www.cbs.dtu.dk/services/TargetP/. (C) 2000 Academic
        Press.
      authors:
      - name: Emanuelsson O.
      - name: Nielsen H.
      - name: Brunak S.
      - name: Von Heijne G.
      citationCount: 3470
      date: '2000-07-21T00:00:00Z'
      journal: Journal of Molecular Biology
      title: Predicting subcellular localization of proteins based on their N-terminal
        amino acid sequence
    note: null
    pmcid: null
    pmid: '10891285'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Web application
  - Web service
  topic:
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  validated: 1
  version:
  - '1.1'
- accessibility: null
  additionDate: '2015-09-12T12:50:16Z'
  biotoolsCURIE: biotools:chlorop
  biotoolsID: chlorop
  collectionID: []
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  cost: Free of charge (with restrictions)
  credit:
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    fundrefid: null
    gridid: null
    name: CBS
    note: null
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    rorid: null
    typeEntity: Institute
    typeRole:
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    url: null
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    note: null
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    typeEntity: Person
    typeRole:
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    url: http://www.bioinformatics.dtu.dk/english/Service/Contact
  - email: hnielsen@bioinformatics.dtu.dk
    fundrefid: null
    gridid: null
    name: Henrik Nielsen
    note: null
    orcidid: http://orcid.org/0000-0002-9412-9643
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Prediction of presence of chloroplast transit peptides and their cleavage
    sites in plant proteins.
  documentation:
  - note: null
    type:
    - Citation instructions
    url: http://www.cbs.dtu.dk/services/ChloroP
  - note: null
    type:
    - General
    url: http://www.cbs.dtu.dk/services/ChloroP/pages/instr.php
  download:
  - note: null
    type: Source code
    url: http://www.cbs.dtu.dk/services/doc/chlorop-1.1.readme
    version: null
  - note: null
    type: Binaries
    url: http://www.cbs.dtu.dk/services/doc/chlorop-1.1.readme
    version: null
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  function:
  - cmd: null
    input:
    - data:
        term: Sequence
        uri: http://edamontology.org/data_2044
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: predicts the presence of chloroplast transit peptides (cTP)
    operation:
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://cbs.dtu.dk/services/ChloroP/
  homepage_status: 0
  language: []
  lastUpdate: '2018-12-14T09:40:07Z'
  license: Other
  link:
  - note: null
    type:
    - Repository
    url: http://www.cbs.dtu.dk/services/doc/chlorop-1.1.readme
  maturity: Mature
  name: ChloroP
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: CBS
  publication:
  - doi: 10.1110/ps.8.5.978
    metadata:
      abstract: We present a neural network based method (ChloroP) for identifying
        chloroplast transit peptides and their cleavage sites. Using cross- validation,
        88% of the sequences in our homology reduced training set were correctly classified
        as transit peptides or nontransit peptides. This performance level is well
        above that of the publicly available chloroplast localization predictor PSORT.
        Cleavage sites are predicted using a scoring matrix derived by an automatic
        motif-finding algorithm. Approximately 60% of the known cleavage sites in
        our sequence collection were predicted to within ±2 residues from the cleavage
        sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences
        from SWISS-PROT suggests that the ChloroP method should be useful for the
        identification of putative transit peptides in genome-wide sequence data.
        The ChloroP predictor is available as a web- server at http://www.cbs.dtu.dk/services/ChloroP/.
      authors:
      - name: Emanuelsson O.
      - name: Nielsen H.
      - name: Von Heijne G.
      citationCount: 1467
      date: '1999-01-01T00:00:00Z'
      journal: Protein Science
      title: ChloroP, a neural network-based method for predicting chloroplast transit
        peptides and their cleavage sites
    note: null
    pmcid: PMC2144330
    pmid: '10338008'
    type:
    - Primary
    version: null
  - doi: 10.1110/ps.8.5.978
    metadata:
      abstract: We present a neural network based method (ChloroP) for identifying
        chloroplast transit peptides and their cleavage sites. Using cross- validation,
        88% of the sequences in our homology reduced training set were correctly classified
        as transit peptides or nontransit peptides. This performance level is well
        above that of the publicly available chloroplast localization predictor PSORT.
        Cleavage sites are predicted using a scoring matrix derived by an automatic
        motif-finding algorithm. Approximately 60% of the known cleavage sites in
        our sequence collection were predicted to within ±2 residues from the cleavage
        sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences
        from SWISS-PROT suggests that the ChloroP method should be useful for the
        identification of putative transit peptides in genome-wide sequence data.
        The ChloroP predictor is available as a web- server at http://www.cbs.dtu.dk/services/ChloroP/.
      authors:
      - name: Emanuelsson O.
      - name: Nielsen H.
      - name: Von Heijne G.
      citationCount: 1467
      date: '1999-01-01T00:00:00Z'
      journal: Protein Science
      title: ChloroP, a neural network-based method for predicting chloroplast transit
        peptides and their cleavage sites
    note: null
    pmcid: PMC2144330
    pmid: '10338008'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  validated: 1
  version:
  - '1.1'
next: null
previous: null
