count: 947
list:
- accessibility: Open access
  additionDate: '2024-03-11T23:56:26.319721Z'
  biotoolsCURIE: biotools:nonpareil
  biotoolsID: nonpareil
  collectionID: []
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: miguel@rodriguez-r.com
    fundrefid: null
    gridid: null
    name: Luis M Rodriguez-R
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: https://github.com/lmrodriguezr
  description: Estimate metagenomic coverage and sequence diversity
  documentation:
  - note: null
    type:
    - General
    url: https://nonpareil.readthedocs.io
  download:
  - note: null
    type: Source code
    url: https://github.com/lmrodriguezr/nonpareil/archive/refs/tags/v3.4.1.zip
    version: 3.4.1
  editPermission:
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  function:
  - cmd: 'nonpareil -s reads.fa -T kmer -f fastq -b output # fastq is recommended
      for kmer algorithm'
    input:
    - data:
        term: RNA sequence
        uri: http://edamontology.org/data_3495
      format:
      - term: FASTQ
        uri: http://edamontology.org/format_1930
    note: null
    operation:
    - term: Operation
      uri: http://edamontology.org/operation_0004
    output:
    - data:
        term: Evidence
        uri: http://edamontology.org/data_2042
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: http://enve-omics.ce.gatech.edu/nonpareil/
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T23:56:26.323108Z'
  license: Artistic-2.0
  link:
  - note: null
    type:
    - Issue tracker
    url: https://github.com/lmrodriguezr/nonpareil
  maturity: Mature
  name: nonpareil
  operatingSystem: []
  otherID: []
  owner: klemmp
  publication:
  - doi: 10.1093/bioinformatics/btt584
    metadata:
      abstract: 'Motivation: Determining the fraction of the diversity within a microbial
        community sampled and the amount of sequencing required to cover the total
        diversity represent challenging issues for metagenomics studies. Owing to
        these limitations, central ecological questions with respect to the global
        distribution of microbes and the functional diversity of their communities
        cannot be robustly assessed.Results: We introduce Nonpareil, a method to estimate
        and project coverage in metagenomes. Nonpareil does not rely on high-quality
        assemblies, operational taxonomic unit calling or comprehensive reference
        databases; thus, it is broadly applicable to metagenomic studies. Application
        of Nonpareil on available metagenomic datasets provided estimates on the relative
        complexity of soil, freshwater and human microbiome communities, and suggested
        that ∼200 Gb of sequencing data are required for 95% abundance-weighted average
        coverage of the soil communities analyzed. © 2013 The Author 2013. Published
        by Oxford University Press. All rights reserved.'
      authors:
      - name: Rodriguez-R L.M.
      - name: Konstantinidis K.T.
      citationCount: 147
      date: '2014-03-01T00:00:00Z'
      journal: Bioinformatics
      title: 'Nonpareil: A redundancy-based approach to assess the level of coverage
        in metagenomic datasets'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType: []
  topic: []
  validated: 0
  version:
  - 3.4.1
- accessibility: Open access
  additionDate: '2016-01-22T15:53:12Z'
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  collectionID:
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  confidence_flag: tool
  cost: Free of charge
  credit:
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    name: ELIXIR-ITA-BOLOGNA
    note: null
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    typeEntity: Institute
    typeRole:
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    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
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    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: rita.casadio@unibo.it
    fundrefid: null
    gridid: null
    name: Rita Casadio
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: piero.fariselli@unito.it
    fundrefid: null
    gridid: null
    name: Piero Fariselli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Mitochondrial targeting peptide prediction.
  documentation:
  - note: null
    type:
    - General
    url: https://tppred3.biocomp.unibo.it/tppred3/default/help
  - note: Installation instructions
    type:
    - Command-line options
    url: https://tppred2.biocomp.unibo.it/tppred2/default/software
  download:
  - note: null
    type: Source code
    url: http://biocomp.unibo.it/savojard/tppred2.tar.gz
    version: null
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  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Prediction
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    - term: Sequence feature detection
      uri: http://edamontology.org/operation_0253
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://tppred2.biocomp.unibo.it
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-03-11T16:57:44.699185Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: TPpred 2.0
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btu411
    metadata: null
    note: null
    pmcid: null
    pmid: '24974200'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btt089
    metadata:
      abstract: 'Motivation: Targeting peptides are the most important signal controlling
        the import of nuclear encoded proteins into mitochondria and plastids. In
        the lack of experimental information, their prediction is an essential step
        when proteomes are annotated for inferring both the localization and the sequence
        of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting
        peptides based on Grammatical-Restrained Hidden Conditional Random Fields.
        TPpred is trained on a non-redundant dataset of proteins where the presence
        of a target peptide was experimentally validated, comprising 297 sequences.
        When tested on the 297 positive and some other 8010 negative examples, TPpred
        outperformed available methods in both accuracy and Matthews correlation index
        (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%),
        TPpred is, therefore, well suited for large-scale analyses at the proteome
        level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis
        thaliana and yeast proteomes contain targeting peptides and are, therefore,
        likely to be localized in mitochondria and plastids. TPpred predictions correlate
        to a good extent with the experimental annotation of the subcellular localization,
        when available. TPpred was also trained and tested to predict the cleavage
        site of the organelle-targeting peptide: on this task, the average error of
        TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively.
        This value is lower than the error reported by other methods currently available.
        © 2013 The Author.'
      authors:
      - name: Indio V.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 14
      date: '2013-04-15T00:00:00Z'
      journal: Bioinformatics
      title: The prediction of organelle-targeting peptides in eukaryotic proteins
        with Grammatical-Restrained Hidden Conditional Random Fields
    note: null
    pmcid: null
    pmid: '23428638'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: tppred_1.0
    type: isNewVersionOf
  - biotoolsID: tppred_3.0
    type: hasNewVersion
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Protein targeting and localisation
    uri: http://edamontology.org/topic_0140
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2015-11-18T13:30:48Z'
  biotoolsCURIE: biotools:tppred_1.0
  biotoolsID: tppred_1.0
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: rita.casadio@unibo.it
    fundrefid: null
    gridid: null
    name: Rita Casadio
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: piero.fariselli@unito.it
    fundrefid: null
    gridid: null
    name: Piero Fariselli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: null
  description: Organelle targeting peptide prediction.
  documentation:
  - note: null
    type:
    - General
    url: https://tppred.biocomp.unibo.it/tppred/default/about
  download: []
  editPermission:
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    - savo
    type: group
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  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Prediction
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    - term: Sequence feature detection
      uri: http://edamontology.org/operation_0253
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://tppred.biocomp.unibo.it/tppred/default/index
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:56:24.696925Z'
  license: null
  link: []
  maturity: Mature
  name: TPpred 1.0
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btt089
    metadata:
      abstract: 'Motivation: Targeting peptides are the most important signal controlling
        the import of nuclear encoded proteins into mitochondria and plastids. In
        the lack of experimental information, their prediction is an essential step
        when proteomes are annotated for inferring both the localization and the sequence
        of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting
        peptides based on Grammatical-Restrained Hidden Conditional Random Fields.
        TPpred is trained on a non-redundant dataset of proteins where the presence
        of a target peptide was experimentally validated, comprising 297 sequences.
        When tested on the 297 positive and some other 8010 negative examples, TPpred
        outperformed available methods in both accuracy and Matthews correlation index
        (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%),
        TPpred is, therefore, well suited for large-scale analyses at the proteome
        level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis
        thaliana and yeast proteomes contain targeting peptides and are, therefore,
        likely to be localized in mitochondria and plastids. TPpred predictions correlate
        to a good extent with the experimental annotation of the subcellular localization,
        when available. TPpred was also trained and tested to predict the cleavage
        site of the organelle-targeting peptide: on this task, the average error of
        TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively.
        This value is lower than the error reported by other methods currently available.
        © 2013 The Author.'
      authors:
      - name: Indio V.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 14
      date: '2013-04-15T00:00:00Z'
      journal: Bioinformatics
      title: The prediction of organelle-targeting peptides in eukaryotic proteins
        with Grammatical-Restrained Hidden Conditional Random Fields
    note: null
    pmcid: null
    pmid: '23428638'
    type:
    - Primary
    version: null
  relation:
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    type: hasNewVersion
  - biotoolsID: tppred_3.0
    type: hasNewVersion
  toolType:
  - Web application
  topic:
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Protein targeting and localisation
    uri: http://edamontology.org/topic_0140
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2016-05-04T15:36:53Z'
  biotoolsCURIE: biotools:inps-md
  biotoolsID: inps-md
  collectionID:
  - Rare Disease
  - Bologna Biocomputing Group
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
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    url: http://www.biocomp.unibo.it
  - email: savojard@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: piero.fariselli@unito.it
    fundrefid: null
    gridid: null
    name: Piero Fariselli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
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    url: null
  - email: savojard@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
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    url: http://biocomp.unibo.it/savojard/
  description: Predicting the impact of mutations on protein stability from sequence
    and structure.
  documentation:
  - note: null
    type:
    - General
    url: https://inpsmd.biocomp.unibo.it/inpsSuite
  download: []
  editPermission:
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  function:
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    input:
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        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
      from sequence.
    operation:
    - term: Protein sequence analysis
      uri: http://edamontology.org/operation_2479
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    - data:
        term: Polypeptide chain ID
        uri: http://edamontology.org/data_1008
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
      from structure.
    operation:
    - term: Protein structure analysis
      uri: http://edamontology.org/operation_2406
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
  homepage: http://inpsmd.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:56:00.844792Z'
  license: null
  link: []
  maturity: Mature
  name: INPS-MD
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btv291
    metadata:
      abstract: 'Motivation: A tool for reliably predicting the impact of variations
        on protein stability is extremely important for both protein engineering and
        for understanding the effects of Mendelian and somatic mutations in the genome.
        Next Generation Sequencing studies are constantly increasing the number of
        protein sequences. Given the huge disproportion between protein sequences
        and structures, there is a need for tools suited to annotate the effect of
        mutations starting from protein sequence without relying on the structure.
        Here, we describe INPS, a novel approach for annotating the effect of non-synonymous
        mutations on the protein stability from its sequence. INPS is based on SVM
        regression and it is trained to predict the thermodynamic free energy change
        upon single-point variations in protein sequences. Results: We show that INPS
        performs similarly to the state-of-the-art methods based on protein structure
        when tested in cross-validation on a non-redundant dataset. INPS performs
        very well also on a newly generated dataset consisting of a number of variations
        occurring in the tumor suppressor protein p53. Our results suggest that INPS
        is a tool suited for computing the effect of non-synonymous polymorphisms
        on protein stability when the protein structure is not available. We also
        show that INPS predictions are complementary to those of the state-of-the-art,
        structure-based method mCSM. When the two methods are combined, the overall
        prediction on the p53 set scores significantly higher than those of the single
        methods.'
      authors:
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Casadio R.
      citationCount: 94
      date: '2015-02-06T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS: Predicting the impact of non-synonymous variations on protein
        stability from sequence'
    note: null
    pmcid: null
    pmid: '25957347'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btw192
    metadata:
      abstract: 'Motivation: Protein function depends on its structural stability.
        The effects of single point variations on protein stability can elucidate
        the molecular mechanisms of human diseases and help in developing new drugs.
        Recently, we introduced INPS, a method suited to predict the effect of variations
        on protein stability from protein sequence and whose performance is competitive
        with the available state-of-the-art tools. Results: In this article, we describe
        INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension),
        a web server for the prediction of protein stability changes upon single point
        variation from protein sequence and/or structure. Here, we complement INPS
        with a new predictor (INPS3D) that exploits features derived from protein
        3D structure. INPS3D scores with Pearson''s correlation to experimental ΔΔG
        values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based
        INPS scores slightly lower than the structure-based INPS3D and both on the
        same blind test sets well compare with the state-of-the-art methods.'
      authors:
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 147
      date: '2016-08-15T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS-MD: A web server to predict stability of protein variants from
        sequence and structure'
    note: null
    pmcid: null
    pmid: '27153629'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2016-05-04T15:36:53Z'
  biotoolsCURIE: biotools:inps
  biotoolsID: inps
  collectionID:
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  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
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    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
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    typeEntity: Division
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    url: http://www.biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
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    typeEntity: Person
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    - Primary contact
    url: null
  - email: piero.fariselli@unito.it
    fundrefid: null
    gridid: null
    name: Piero Fariselli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Predicting the impact of mutations on protein stability from sequence
  documentation:
  - note: null
    type:
    - General
    url: https://inpsmd.biocomp.unibo.it/inpsSuite
  download: []
  editPermission:
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    - savo
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  function:
  - cmd: null
    input:
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        term: Protein sequence (raw)
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      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Prediction of the impact of non-synonymous polymorphisms on protein stability
    operation:
    - term: Protein structure analysis
      uri: http://edamontology.org/operation_2406
    - term: Genetic variation analysis
      uri: http://edamontology.org/operation_3197
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://inps.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:55:05.187460Z'
  license: null
  link: []
  maturity: Mature
  name: INPS
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btv291
    metadata:
      abstract: 'Motivation: A tool for reliably predicting the impact of variations
        on protein stability is extremely important for both protein engineering and
        for understanding the effects of Mendelian and somatic mutations in the genome.
        Next Generation Sequencing studies are constantly increasing the number of
        protein sequences. Given the huge disproportion between protein sequences
        and structures, there is a need for tools suited to annotate the effect of
        mutations starting from protein sequence without relying on the structure.
        Here, we describe INPS, a novel approach for annotating the effect of non-synonymous
        mutations on the protein stability from its sequence. INPS is based on SVM
        regression and it is trained to predict the thermodynamic free energy change
        upon single-point variations in protein sequences. Results: We show that INPS
        performs similarly to the state-of-the-art methods based on protein structure
        when tested in cross-validation on a non-redundant dataset. INPS performs
        very well also on a newly generated dataset consisting of a number of variations
        occurring in the tumor suppressor protein p53. Our results suggest that INPS
        is a tool suited for computing the effect of non-synonymous polymorphisms
        on protein stability when the protein structure is not available. We also
        show that INPS predictions are complementary to those of the state-of-the-art,
        structure-based method mCSM. When the two methods are combined, the overall
        prediction on the p53 set scores significantly higher than those of the single
        methods.'
      authors:
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Casadio R.
      citationCount: 94
      date: '2015-02-06T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS: Predicting the impact of non-synonymous variations on protein
        stability from sequence'
    note: null
    pmcid: null
    pmid: '25957347'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btw192
    metadata:
      abstract: 'Motivation: Protein function depends on its structural stability.
        The effects of single point variations on protein stability can elucidate
        the molecular mechanisms of human diseases and help in developing new drugs.
        Recently, we introduced INPS, a method suited to predict the effect of variations
        on protein stability from protein sequence and whose performance is competitive
        with the available state-of-the-art tools. Results: In this article, we describe
        INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension),
        a web server for the prediction of protein stability changes upon single point
        variation from protein sequence and/or structure. Here, we complement INPS
        with a new predictor (INPS3D) that exploits features derived from protein
        3D structure. INPS3D scores with Pearson''s correlation to experimental ΔΔG
        values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based
        INPS scores slightly lower than the structure-based INPS3D and both on the
        same blind test sets well compare with the state-of-the-art methods.'
      authors:
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 147
      date: '2016-08-15T00:00:00Z'
      journal: Bioinformatics
      title: 'INPS-MD: A web server to predict stability of protein variants from
        sequence and structure'
    note: null
    pmcid: null
    pmid: '27153629'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2015-01-22T11:31:39Z'
  biotoolsCURIE: biotools:ispred
  biotoolsID: ispred
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: http://www.biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Prediction of interaction sites in proteins.
  documentation:
  - note: null
    type:
    - General
    url: https://ispred4.biocomp.unibo.it/ispred
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity:
  - 3D-BioInfo
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
    - data:
        term: Polypeptide chain ID
        uri: http://edamontology.org/data_1008
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: Prediction
    operation:
    - term: Protein interaction prediction
      uri: http://edamontology.org/operation_2492
    output:
    - data:
        term: Protein structure report
        uri: http://edamontology.org/data_1537
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
  homepage: http://ispred4.biocomp.unibo.it/
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:54:42.923202Z'
  license: null
  link: []
  maturity: Mature
  name: ISPRED
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btx044
    metadata:
      abstract: 'Motivation: The identification of protein-protein interaction (PPI)
        sites is an important step towards the characterization of protein functional
        integration in the cell complexity. Experimental methods are costly and time-consuming
        and computational tools for predicting PPI sites can fill the gaps of PPI
        present knowledge. Results: We present ISPRED4, an improved structure-based
        predictor of PPI sites on unbound monomer surfaces. ISPRED4 relies on machine-learning
        methods and it incorporates features extracted from protein sequence and structure.
        Cross-validation experiments are carried out on a new dataset that includes
        151 high-resolution protein complexes and indicate that ISPRED4 achieves a
        per-residue Matthew Correlation Coefficient of 0.48 and an overall accuracy
        of 0.85. Benchmarking results show that ISPRED4 is one of the top-performing
        PPI site predictors developed so far.'
      authors:
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 25
      date: '2017-06-01T00:00:00Z'
      journal: Bioinformatics
      title: 'ISPRED4: Interaction sites PREDiction in protein structures with a refining
        grammar model'
    note: null
    pmcid: null
    pmid: '28130235'
    type:
    - Primary
    version: null
  - doi: 10.1007/978-3-642-35686-5_11
    metadata:
      abstract: A reliable predictor of protein-protein interaction sites is necessary
        to investigate and model protein functional interaction networks. Hidden Markov
        Support Vector Machines (HM-SVM) have been shown to be among the best performing
        methods on this task. Furthermore, it has been noted that the performance
        of a predictor improves when its input takes advantage of the difference between
        observed and predicted residue solvent accessibility. In this paper, for first
        time, we combine these elements and we present ISPRED2, a new HM-SVMbased
        method that overpasses the state of the art performance (Q2=0.71 and correlation=0.43).
        ISPRED2 consists of a sets of Python scripts aimed at integrating the different
        third-party software to obtain the final prediction. © Springer-Verlag 2012.
      authors:
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Piovesan D.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 3
      date: '2012-12-31T00:00:00Z'
      journal: Lecture Notes in Computer Science (including subseries Lecture Notes
        in Artificial Intelligence and Lecture Notes in Bioinformatics)
      title: Machine-learning methods to predict protein interaction sites in folded
        proteins
    note: null
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  validated: 1
  version:
  - '4'
- accessibility: Open access
  additionDate: '2016-01-22T15:50:02Z'
  biotoolsCURIE: biotools:tppred_3.0
  biotoolsID: tppred_3.0
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    url: http://biocomp.unibo.it/savojard/
  description: Organelle-targeting peptide detection and cleavage-site prediction.
  documentation:
  - note: null
    type:
    - General
    url: https://tppred3.biocomp.unibo.it/tppred3/default/help
  - note: null
    type:
    - Command-line options
    url: https://github.com/BolognaBiocomp/tppred3
  download:
  - note: null
    type: Source code
    url: https://github.com/BolognaBiocomp/tppred3
    version: null
  - note: null
    type: Container file
    url: https://hub.docker.com/r/bolognabiocomp/tppred3
    version: null
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Predictio Protein sequence in FASTA format
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://tppred3.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:48:08.106769Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: TPpred 3.0
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btv367
    metadata:
      abstract: 'Motivation: Molecular recognition of N-terminal targeting peptides
        is the most common mechanism controlling the import of nuclear-encoded proteins
        into mitochondria and chloroplasts. When experimental information is lacking,
        computational methods can annotate targeting peptides, and determine their
        cleavage sites for characterizing protein localization, function, and mature
        protein sequences. The problem of discriminating mitochondrial from chloroplastic
        propeptides is particularly relevant when annotating proteomes of photosynthetic
        Eukaryotes, endowed with both types of sequences. Results: Here, we introduce
        TPpred3, a computational method that given any Eukaryotic protein sequence
        performs three different tasks: (i) the detection of targeting peptides; (ii)
        their classification as mitochondrial or chloroplastic and (iii) the precise
        localization of the cleavage sites in an organelle-specific framework. Our
        implementation is based on our TPpred previously introduced. Here, we integrate
        a new N-to-1 Extreme Learning Machine specifically designed for the classification
        task (ii). For the last task, we introduce an organelle-specific Support Vector
        Machine that exploits sequence motifs retrieved with an extensive motif-discovery
        analysis of a large set of mitochondrial and chloroplastic proteins. We show
        that TPpred3 outperforms the state-of-the-art methods in all the three tasks.'
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 38
      date: '2015-03-25T00:00:00Z'
      journal: Bioinformatics
      title: TPpred3 detects and discriminates mitochondrial and chloroplastic targeting
        peptides in eukaryotic proteins
    note: null
    pmcid: null
    pmid: '26079349'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btu411
    metadata:
      abstract: 'CONTACT: gigi@biocomp.unibo.it SUPPLEMENTARY INFORMATION: Supplementary
        data are available at Bioinformatics online. SUMMARY: Targeting peptides are
        N-terminal sorting signals in proteins that promote their translocation to
        mitochondria through the interaction with different protein machineries. We
        recently developed TPpred, a machine learning-based method scoring among the
        best ones available to predict the presence of a targeting peptide into a
        protein sequence and its cleavage site. Here we introduce TPpred2 that improves
        TPpred performances in the task of identifying the cleavage site of the targeting
        peptides. TPpred2 is now available as a web interface and as a stand-alone
        version for users who can freely download and adopt it for processing large
        volumes of sequences. Availability and implementaion: TPpred2 is available
        both as web server and stand-alone version at http://tppred2.biocomp.unibo.it.'
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 31
      date: '2014-10-15T00:00:00Z'
      journal: Bioinformatics (Oxford, England)
      title: 'TPpred2: improving the prediction of mitochondrial targeting peptide
        cleavage sites by exploiting sequence motifs'
    note: null
    pmcid: null
    pmid: '24974200'
    type:
    - Other
    version: null
  - doi: 10.1093/bioinformatics/btt089
    metadata:
      abstract: 'Motivation: Targeting peptides are the most important signal controlling
        the import of nuclear encoded proteins into mitochondria and plastids. In
        the lack of experimental information, their prediction is an essential step
        when proteomes are annotated for inferring both the localization and the sequence
        of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting
        peptides based on Grammatical-Restrained Hidden Conditional Random Fields.
        TPpred is trained on a non-redundant dataset of proteins where the presence
        of a target peptide was experimentally validated, comprising 297 sequences.
        When tested on the 297 positive and some other 8010 negative examples, TPpred
        outperformed available methods in both accuracy and Matthews correlation index
        (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%),
        TPpred is, therefore, well suited for large-scale analyses at the proteome
        level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis
        thaliana and yeast proteomes contain targeting peptides and are, therefore,
        likely to be localized in mitochondria and plastids. TPpred predictions correlate
        to a good extent with the experimental annotation of the subcellular localization,
        when available. TPpred was also trained and tested to predict the cleavage
        site of the organelle-targeting peptide: on this task, the average error of
        TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively.
        This value is lower than the error reported by other methods currently available.
        © 2013 The Author.'
      authors:
      - name: Indio V.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 14
      date: '2013-04-15T00:00:00Z'
      journal: Bioinformatics
      title: The prediction of organelle-targeting peptides in eukaryotic proteins
        with Grammatical-Restrained Hidden Conditional Random Fields
    note: null
    pmcid: null
    pmid: '23428638'
    type:
    - Other
    version: null
  - doi: 10.1007/978-1-0716-1262-0_28
    metadata:
      abstract: Protein sequences, directly translated from genomic data, need functional
        and structural annotation. Together with molecular function and biological
        process, subcellular localization is an important feature necessary for understanding
        the protein role and the compartment where the mature protein is active. In
        the case of mitochondrial proteins, their precursor sequences translated by
        the ribosome machinery include specific patterns from which it is possible
        not only to recognize their final destination within the organelle but also
        which of the mitochondrial subcompartments the protein is intended for. Four
        compartments are routinely discriminated, including the inner and the outer
        membranes, the intermembrane space, and the matrix. Here we discuss to which
        extent it is feasible to develop computational methods for detecting mitochondrial
        targeting peptides in the precursor sequence and to discriminate their final
        destination in the organelle. We benchmark two of our methods on the general
        task of recognizing human mitochondrial proteins endowed with an experimentally
        characterized targeting peptide (TPpred3) and predicting which submitochondrial
        compartment is the final destination (DeepMito). We describe how to adopt
        our web servers in order to discriminate which human proteins are endowed
        with mitochondrial targeting peptides, the position of cleavage sites, and
        which submitochondrial compartment are intended for. By this, we add some
        other 1788 human proteins to the 450 ones already manually annotated in UniProt
        with a mitochondrial targeting peptide, providing for each of them also the
        characterization of the suborganellar localization.
      authors:
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Tartari G.
      - name: Casadio R.
      citationCount: 2
      date: '2021-01-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: Computer-Aided Prediction of Protein Mitochondrial Localization
    note: null
    pmcid: null
    pmid: '34118055'
    type:
    - Review
    - Benchmarking study
    version: null
  relation:
  - biotoolsID: tppred_2.0
    type: isNewVersionOf
  - biotoolsID: tppred_1.0
    type: isNewVersionOf
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Protein targeting and localisation
    uri: http://edamontology.org/topic_0140
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  validated: 1
  version:
  - '3'
- accessibility: Open access
  additionDate: '2016-01-22T15:51:37Z'
  biotoolsCURIE: biotools:bcov
  biotoolsID: bcov
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: http://biocomp.unibo.it/savojard
  description: Prediction of β-sheet topology.
  documentation:
  - note: null
    type:
    - General
    url: http://biocomp.unibo.it/savojard/bcov/index.html
  download:
  - note: null
    type: Source code
    url: http://biocomp.unibo.it/savojard/bcov/index.html
    version: null
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity:
  - 3D-BioInfo
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    - data:
        term: Sequence alignment (protein)
        uri: http://edamontology.org/data_1384
      format:
      - term: FASTA-aln
        uri: http://edamontology.org/format_1984
    note: Prediction Predicted or real secondary structure of the input protein Protein
      residue contact map
    operation:
    - term: Ab initio structure prediction
      uri: http://edamontology.org/operation_0476
    output:
    - data:
        term: Protein interaction data
        uri: http://edamontology.org/data_0906
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://biocomp.unibo.it/savojard/bcov/index.html
  homepage_status: 0
  language:
  - C
  lastUpdate: '2024-03-11T16:47:48.087425Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: BCov
  operatingSystem:
  - Linux
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btt555
    metadata:
      abstract: 'Motivation: Prediction of protein residue contacts, even at the coarsegrain
        level, can help in finding solutions to the protein structure prediction problem.
        Unlike α-helices that are locally stabilized, β-sheets result from pairwise
        hydrogen bonding of two or more disjoint regions of the protein backbone.
        The problem of predicting contacts among β-strands in proteins has been addressed
        by several supervised computational approaches. Recently, prediction of residue
        contacts based on correlated mutations has been greatly improved and finally
        allows the prediction of 3D structures of the proteins. Results: In this article,
        we describe BCov, which is the first unsuper vised method to predict the β-sheet
        topology starting from the protein sequence and its secondary structure. BCov
        takes advantage of the sparse inverse covariance estimation to define β-strand
        partner scores. Then an optimization based on integer programming is carried
        out to predict the β-sheet connectivity. When tested on the prediction of
        β-strand pairing, BCov scores with average values of Matthews Correlation
        Coefficient (MCC) and F1 equal to 0.56 and 0.61, respectively, on a non-redundant
        dataset of 916 protein chains known with atomic resolution. Our approach well
        compares with the state-of-the art methods trained so far for this specific
        task.'
      authors:
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 18
      date: '2013-12-15T00:00:00Z'
      journal: Bioinformatics
      title: 'BCov: A method for predicting β-sheet topology using sparse inverse
        covariance estimation and integer programming'
    note: null
    pmcid: PMC5994943
    pmid: '24064422'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  validated: 1
  version:
  - '1'
- accessibility: Open access
  additionDate: '2021-01-20T10:58:38Z'
  biotoolsCURIE: biotools:phenpath
  biotoolsID: phenpath
  collectionID:
  - Rare Disease
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: giulia.babbi@unibo.it
    fundrefid: null
    gridid: null
    name: Giulia Babbi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  description: 'A tool for characterizing biological functions underlying different
    phenotypes.


    a web server for associating phenotypes with molecular functional annotations.


    PhenPath includes a database and a tool:.

    '''
  documentation:
  - note: null
    type:
    - User manual
    url: http://phenpath.biocomp.unibo.it/phenpath/help_page.html
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Disease ID
        uri: http://edamontology.org/data_1150
      format: []
    - data:
        term: Disease name
        uri: http://edamontology.org/data_3668
      format: []
    - data:
        term: Phenotype name
        uri: http://edamontology.org/data_3275
      format: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Enrichment analysis
      uri: http://edamontology.org/operation_3501
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    output:
    - data:
        term: Disease report
        uri: http://edamontology.org/data_1622
      format: []
  homepage: http://phenpath.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:45:53.823541Z'
  license: CC-BY-NC-4.0
  link: []
  maturity: Mature
  name: PhenPath
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1186/S12864-019-5868-X
    metadata:
      abstract: 'Background: Many diseases are associated with complex patterns of
        symptoms and phenotypic manifestations. Parsimonious explanations aim at reconciling
        the multiplicity of phenotypic traits with the perturbation of one or few
        biological functions. For this, it is necessary to characterize human phenotypes
        at the molecular and functional levels, by exploiting gene annotations and
        known relations among genes, diseases and phenotypes. This characterization
        makes it possible to implement tools for retrieving functions shared among
        phenotypes, co-occurring in the same patient and facilitating the formulation
        of hypotheses about the molecular causes of the disease. Results: We introduce
        PhenPath, a new resource consisting of two parts: PhenPathDB and PhenPathTOOL.
        The former is a database collecting the human genes associated with the phenotypes
        described in Human Phenotype Ontology (HPO) and OMIM Clinical Synopses. Phenotypes
        are then associated with biological functions and pathways by means of NET-GE,
        a network-based method for functional enrichment of sets of genes. The present
        version considers only phenotypes related to diseases. PhenPathDB collects
        information for 18 OMIM Clinical synopses and 7137 HPO phenotypes, related
        to 4292 diseases and 3446 genes. Enrichment of Gene Ontology annotations endows
        some 87.7, 86.9 and 73.6% of HPO phenotypes with Biological Process, Molecular
        Function and Cellular Component terms, respectively. Furthermore, 58.8 and
        77.8% of HPO phenotypes are also enriched for KEGG and Reactome pathways,
        respectively. Based on PhenPathDB, PhenPathTOOL analyzes user-defined sets
        of phenotypes retrieving diseases, genes and functional terms which they share.
        This information can provide clues for interpreting the co-occurrence of phenotypes
        in a patient. Conclusions: The resource allows finding molecular features
        useful to investigate diseases characterized by multiple phenotypes, and by
        this, it can help researchers and physicians in identifying molecular mechanisms
        and biological functions underlying the concomitant manifestation of phenotypes.
        The resource is freely available at http://phenpath.biocomp.unibo.it.'
      authors:
      - name: Babbi G.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 8
      date: '2019-07-16T00:00:00Z'
      journal: BMC Genomics
      title: 'PhenPath: A tool for characterizing biological functions underlying
        different phenotypes'
    note: null
    pmcid: PMC6631446
    pmid: '31307376'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Pathology
    uri: http://edamontology.org/topic_0634
  - term: Public health and epidemiology
    uri: http://edamontology.org/topic_3305
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2022-09-01T11:34:17.973845Z'
  biotoolsCURIE: biotools:svmyr
  biotoolsID: svmyr
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    url: null
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: https://orcid.org/0000-0002-0274-5669
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  description: A Web Server Detecting Co- and Post-translational Myristoylation in
    Proteins.
  documentation:
  - note: null
    type:
    - User manual
    url: http://busca.biocomp.unibo.it/lipipred/about/
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  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: PTM site prediction
      uri: http://edamontology.org/operation_0417
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: https://busca.biocomp.unibo.it/lipipred
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:39:58.647418Z'
  license: null
  link: []
  maturity: Mature
  name: SVMyr
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1016/J.JMB.2022.167605
    metadata:
      abstract: Myristoylation (MYR) is a protein modification where a myristoyl group
        is covalently attached to an exposed (N-terminal) glycine residue. Glycine
        myristoylation occurs during protein translation (co-translation) or after
        (post-translation). Myristoylated proteins have a role in signal transduction,
        apoptosis, and pathogen-mediated processes and their prediction can help in
        functionally annotating the fraction of proteins undergoing MYR in different
        proteomes. Here we present SVMyr, a web server allowing the detection of both
        co- and post-translational myristoylation sites, based on Support Vector Machines
        (SVM). The input encodes composition and physicochemical features of the octapeptides,
        known to act as substrates and to physically interact with N-myristoyltransferases
        (NMTs), the enzymes catalyzing the myristoylation reaction. The method, adopting
        a cross validation procedure, scores with values of Area Under the Curve (AUC)
        and Matthews Correlation Coefficient (MCC) of 0.92 and 0.61, respectively.
        When benchmarked on an independent dataset including experimentally detected
        88 medium/high confidence co-translational myristoylation sites and 528 negative
        examples, SVMyr outperforms available methods, with AUC and MCC equal to 0.91
        and 0.58, respectively. A unique feature of SVMyr is the ability to predict
        post-translational myristoylation sites by coupling the trained SVMs with
        the detection of caspase cleavage sites, identified by searching regular motifs
        matching upstream caspase cleavage sites, as reported in literature. Finally,
        SVMyr confirms 96% of the UniProt set of the electronically annotated myristoylated
        proteins (31,048) and identifies putative myristoylomes in eight different
        proteomes, highlighting also new putative NMT substrates. SVMyr is freely
        available through a user-friendly web server at https://busca.biocomp.unibo.it/lipipred.
      authors:
      - name: Madeo G.
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 5
      date: '2022-06-15T00:00:00Z'
      journal: Journal of Molecular Biology
      title: 'SVMyr: A Web Server Detecting Co- and Post-translational Myristoylation
        in Proteins'
    note: null
    pmcid: null
    pmid: '35662454'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  - term: Lipids
    uri: http://edamontology.org/topic_0153
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  validated: 1
  version:
  - '1.0'
next: ?page=2
previous: null
