count: 5824
list:
- accessibility: null
  additionDate: '2024-04-18T13:41:16.316985Z'
  biotoolsCURIE: biotools:twnbiome
  biotoolsID: twnbiome
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: tplu@ntu.edu.tw
    fundrefid: null
    gridid: null
    name: Tzu-Pin Lu
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: chuangey@ntu.edu.tw
    fundrefid: null
    gridid: null
    name: Eric Y. Chuang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: A public database of the healthy taiwanese gut microbiome.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Database search
      uri: http://edamontology.org/operation_2421
    output: []
  homepage: http://twnbiome.cgm.ntu.edu.tw/
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-18T13:41:16.319370Z'
  license: null
  link: []
  maturity: null
  name: Twnbiome
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/S12859-023-05585-6
    metadata:
      abstract: 'With new advances in next generation sequencing (NGS) technology
        at reduced costs, research on bacterial genomes in the environment has become
        affordable. Compared to traditional methods, NGS provides high-throughput
        sequencing reads and the ability to identify many species in the microbiome
        that were previously unknown. Numerous bioinformatics tools and algorithms
        have been developed to conduct such analyses. However, in order to obtain
        biologically meaningful results, the researcher must select the proper tools
        and combine them to construct an efficient pipeline. This complex procedure
        may include tens of tools, each of which require correct parameter settings.
        Furthermore, an NGS data analysis involves multiple series of command-line
        tools and requires extensive computational resources, which imposes a high
        barrier for biologists and clinicians to conduct NGS analysis and even interpret
        their own data. Therefore, we established a public gut microbiome database,
        which we call Twnbiome, created using healthy subjects from Taiwan, with the
        goal of enabling microbiota research for the Taiwanese population. Twnbiome
        provides users with a baseline gut microbiome panel from a healthy Taiwanese
        cohort, which can be utilized as a reference for conducting case-control studies
        for a variety of diseases. It is an interactive, informative, and user-friendly
        database. Twnbiome additionally offers an analysis pipeline, where users can
        upload their data and download analyzed results. Twnbiome offers an online
        database which non-bioinformatics users such as clinicians and doctors can
        not only utilize to access a control set of data, but also analyze raw data
        with a few easy clicks. All results are customizable with ready-made plots
        and easily downloadable tables. Database URL: http://twnbiome.cgm.ntu.edu.tw/
        .'
      authors:
      - name: Chattopadhyay A.
      - name: Lee C.-Y.
      - name: Lee Y.-C.
      - name: Liu C.-L.
      - name: Chen H.-K.
      - name: Li Y.-H.
      - name: Lai L.-C.
      - name: Tsai M.-H.
      - name: Ni Y.-H.
      - name: Chiu H.-M.
      - name: Lu T.-P.
      - name: Chuang E.Y.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'Twnbiome: a public database of the healthy Taiwanese gut microbiome'
    note: null
    pmcid: PMC10722848
    pmid: '38097965'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Biological databases
    uri: http://edamontology.org/topic_3071
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-18T13:09:52.425416Z'
  biotoolsCURIE: biotools:open_combind
  biotoolsID: open_combind
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: dkoes@pitt.edu
    fundrefid: null
    gridid: null
    name: David Ryan Koes
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Harnessing unlabeled data for improved binding pose prediction.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein-ligand docking
      uri: http://edamontology.org/operation_0482
    - term: Small molecule design
      uri: http://edamontology.org/operation_4009
    - term: Protein-protein docking
      uri: http://edamontology.org/operation_3899
    output: []
  homepage: http://www.github.com/drewnutt/open_combind
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-18T13:09:52.428019Z'
  license: null
  link: []
  maturity: null
  name: Open-ComBind
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1007/S10822-023-00544-Y
    metadata:
      abstract: Determination of the bound pose of a ligand is a critical first step
        in many in silico drug discovery tasks. Molecular docking is the main tool
        for the prediction of non-covalent binding of a protein and ligand system.
        Molecular docking pipelines often only utilize the information of one ligand
        binding to the protein despite the commonly held hypothesis that different
        ligands share binding interactions when bound to the same receptor. Here we
        describe Open-ComBind, an easy-to-use, open-source version of the ComBind
        molecular docking pipeline that leverages information from multiple ligands
        without known bound structures to enhance pose selection. We first create
        distributions of feature similarities between ligand pose pairs, comparing
        near-native poses with all sampled docked poses. These distributions capture
        the likelihood of observing similar features, such as hydrogen bonds or hydrophobic
        contacts, in different pose configurations. These similarity distributions
        are then combined with a per-ligand docking score to enhance overall pose
        selection by 5% and 4.5% for high-affinity and congeneric series helper ligands,
        respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark
        dataset by 9.0%. We provide Open-ComBind as an easy-to-use command line and
        Python API to increase pose prediction performance at www.github.com/drewnutt/open_combind
        .
      authors:
      - name: McNutt A.T.
      - name: Koes D.R.
      citationCount: 0
      date: '2024-12-01T00:00:00Z'
      journal: Journal of Computer-Aided Molecular Design
      title: 'Open-ComBind: harnessing unlabeled data for improved binding pose prediction'
    note: null
    pmcid: PMC10703974
    pmid: '38062207'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: Medicinal chemistry
    uri: http://edamontology.org/topic_0209
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:15:59.343761Z'
  biotoolsCURIE: biotools:phenosv
  biotoolsID: phenosv
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: wangk@chop.edu
    fundrefid: null
    gridid: null
    name: Kai Wang
    note: null
    orcidid: https://orcid.org/0000-0002-5585-982X
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Zhuoran Xu
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: PhenoSV is a phenotype-aware structural variants interpretation and
    prioritization tool.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant prioritisation
      uri: http://edamontology.org/operation_3226
    - term: Formatting
      uri: http://edamontology.org/operation_0335
    - term: cis-regulatory element prediction
      uri: http://edamontology.org/operation_0441
    output: []
  homepage: https://phenosv.wglab.org
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-16T14:15:59.346819Z'
  license: null
  link: []
  maturity: null
  name: PhenoSV
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1038/S41467-023-43651-Y
    metadata:
      abstract: Structural variants (SVs) represent a major source of genetic variation
        associated with phenotypic diversity and disease susceptibility. While long-read
        sequencing can discover over 20,000 SVs per human genome, interpreting their
        functional consequences remains challenging. Existing methods for identifying
        disease-related SVs focus on deletion/duplication only and cannot prioritize
        individual genes affected by SVs, especially for noncoding SVs. Here, we introduce
        PhenoSV, a phenotype-aware machine-learning model that interprets all major
        types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse
        genomic features and employs a transformer-based architecture to predict their
        impacts under a multiple-instance learning framework. With phenotype information,
        PhenoSV further utilizes gene-phenotype associations to prioritize phenotype-related
        SVs. Evaluation on extensive human SV datasets covering all SV types demonstrates
        PhenoSV’s superior performance over competing methods. Applications in diseases
        suggest that PhenoSV can determine disease-related genes from SVs. A web server
        and a command-line tool for PhenoSV are available at https://phenosv.wglab.org
        .
      authors:
      - name: Xu Z.
      - name: Li Q.
      - name: Marchionni L.
      - name: Wang K.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: Nature Communications
      title: 'PhenoSV: interpretable phenotype-aware model for the prioritization
        of genes affected by structural variants'
    note: null
    pmcid: PMC10684511
    pmid: '38016949'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Structural variation
    uri: http://edamontology.org/topic_3175
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Epigenetics
    uri: http://edamontology.org/topic_3295
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:03:57.384386Z'
  biotoolsCURIE: biotools:cirscan
  biotoolsID: cirscan
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: yuna.blum@univ-rennes.fr
    fundrefid: null
    gridid: null
    name: Yuna Blum
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Rose-Marie Fraboulet
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Shiny application to identify differentially active sponge mechanisms
    and visualize circRNA-miRNA-mRNA networks.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene-set enrichment analysis
      uri: http://edamontology.org/operation_2436
    - term: Network visualisation
      uri: http://edamontology.org/operation_3925
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    output: []
  homepage: https://gitlab.com/geobioinfo/cirscan_Rshiny
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-16T14:05:09.344226Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: Cirscan
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/s12859-024-05668-y
    metadata:
      abstract: 'Background: Non-coding RNAs represent a large part of the human transcriptome
        and have been shown to play an important role in disease such as cancer. However,
        their biological functions are still incompletely understood. Among non-coding
        RNAs, circular RNAs (circRNAs) have recently been identified for their microRNA
        (miRNA) sponge function which allows them to modulate the expression of miRNA
        target genes by taking on the role of competitive endogenous RNAs (ce-circRNAs).
        Today, most computational tools are not adapted to the search for ce-circRNAs
        or have not been developed for the search for ce-circRNAs from user’s transcriptomic
        data. Results: In this study, we present Cirscan (CIRcular RNA Sponge CANdidates),
        an interactive Shiny application that automatically infers circRNA–miRNA–mRNA
        networks from human multi-level transcript expression data from two biological
        conditions (e.g. tumor versus normal conditions in the case of cancer study)
        in order to identify on a large scale, potential sponge mechanisms active
        in a specific condition. Cirscan ranks each circRNA–miRNA–mRNA subnetwork
        according to a sponge score that integrates multiple criteria based on interaction
        reliability and expression level. Finally, the top ranked sponge mechanisms
        can be visualized as networks and an enrichment analysis is performed to help
        its biological interpretation. We showed on two real case studies that Cirscan
        is capable of retrieving sponge mechanisms previously described, as well as
        identifying potential novel circRNA sponge candidates. Conclusions: Cirscan
        can be considered as a companion tool for biologists, facilitating their ability
        to prioritize sponge mechanisms for experimental validations and identifying
        potential therapeutic targets. Cirscan is implemented in R, released under
        the license GPL-3 and accessible on GitLab (https://gitlab.com/geobioinfo/cirscan_Rshiny
        ). The scripts used in this paper are also provided on Gitlab (https://gitlab.com/geobioinfo/cirscan_paper
        ).'
      authors:
      - name: Fraboulet R.-M.
      - name: Si Ahmed Y.
      - name: Aubry M.
      - name: Corre S.
      - name: Galibert M.-D.
      - name: Blum Y.
      citationCount: 0
      date: '2024-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'Cirscan: a shiny application to identify differentially active sponge
        mechanisms and visualize circRNA–miRNA–mRNA networks'
    note: null
    pmcid: PMC10832221
    pmid: '38302900'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Microarray experiment
    uri: http://edamontology.org/topic_3518
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-16T14:01:37.903804Z'
  biotoolsCURIE: biotools:recview
  biotoolsID: recview
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: hongkai.zhang@biol.lu.se
    fundrefid: null
    gridid: null
    name: Hongkai Zhang
    note: null
    orcidid: https://orcid.org/0000-0001-7371-9612
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: bengt.hansson@biol.lu.se
    fundrefid: null
    gridid: null
    name: Bengt Hansson
    note: null
    orcidid: https://orcid.org/0000-0001-6694-8169
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: This R package is designed to distribute the RecView ShinyApp which
    aims at providing a user-friendly GUI for viewing and locating recombination positions
    on chromosomes using pedigree data.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Recombination detection
      uri: http://edamontology.org/operation_0451
    - term: SNP detection
      uri: http://edamontology.org/operation_0484
    - term: Genetic mapping
      uri: http://edamontology.org/operation_0282
    output: []
  homepage: https://github.com/HKyleZhang/RecView.git
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-16T14:01:37.906051Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: RecView
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/S12864-023-09807-2
    metadata:
      abstract: 'Background: Recombination reshuffles alleles at linked loci, allowing
        genes to evolve independently and consequently enhancing the efficiency of
        selection. This makes quantifying recombination along chromosomes an important
        goal for understanding how selection and drift are acting on genes and chromosomes.
        Results: We present RecView, an interactive R application and its homonymous
        R package, to facilitate locating recombination positions along chromosomes
        or scaffolds using whole-genome genotype data of a three-generation pedigree.
        RecView analyses and plots the grandparent-of-origin of all informative alleles
        along each chromosome of the offspring in the pedigree, and infers recombination
        positions with either of two built-in algorithms: one based on change in the
        proportion of the alleles with specific grandparent-of-origin, and one on
        the degree of continuity of alleles with the same grandparent-of-origin. RecView
        handles multiple offspring and chromosomes simultaneously, and all putative
        recombination positions are reported in base pairs together with an estimated
        precision based on the local density of informative alleles. We demonstrate
        RecView using genotype data of a passerine bird with an available reference
        genome, the great reed warbler (Acrocephalus arundinaceus), and show that
        recombination events can be located to specific positions. Conclusions: RecView
        is an easy-to-use and highly effective application for locating recombination
        positions with high precision. RecView is available on GitHub (https://github.com/HKyleZhang/RecView.git).'
      authors:
      - name: Zhang H.
      - name: Hansson B.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: BMC Genomics
      title: 'RecView: an interactive R application for locating recombination positions
        using pedigree data'
    note: null
    pmcid: PMC10676570
    pmid: '38007417'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  - Web application
  topic:
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: DNA replication and recombination
    uri: http://edamontology.org/topic_3127
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  validated: 0
  version: []
- accessibility: null
  additionDate: '2020-01-09T17:51:12Z'
  biotoolsCURIE: biotools:PlasmidFinder
  biotoolsID: PlasmidFinder
  collectionID: []
  community: null
  confidence_flag: tool
  cost: null
  credit: []
  description: PlasmidFinder is a tool for the identification and typing of Plasmid
    Replicons in Whole-Genome Sequencing (WGS).
  documentation: []
  download: []
  editPermission:
    authors:
    - ELIXIR-CZ
    - hakimul80
    - Imran_24
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: Multilocus sequence typing
      uri: http://edamontology.org/operation_3840
    output: []
  homepage: https://cge.cbs.dtu.dk/services/PlasmidFinder/
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-12T09:10:37.322150Z'
  license: null
  link: []
  maturity: null
  name: PlasmidFinder
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1007/978-1-4939-9877-7_20
    metadata:
      abstract: © Springer Science+Business Media, LLC, part of Springer Nature 2020.PlasmidFinder
        and in silico plasmid multiLocus sequence typing (pMLST) are two easy-to-use
        web tools for detection and characterization of plasmid sequences in whole-genome
        sequencing (WGS) data from Enterobacteriaceae. These tools have been adopted
        worldwide and facilitate plasmid detection and typing based on draft genomes
        of multi-drug-resistant Enterobacteriaceae. The PlasmidFinder database currently
        includes 133 unique plasmid replicon sequences. It was built starting with
        126 sequences devised on fully sequenced plasmids available at the NCBI nucleotide
        database in 2014 and has been continuously updated to include novel replicons
        detected in more recently sequenced plasmids associated with the family Enterobacteriaceae.
        PlasmidFinder is usable for replicon sequence analysis of raw as well as assembled
        sequencing data. For pMLST analysis, a weekly updated database was generated
        from www.pubmlst.org and integrated into a web tool called in silico pMLST.
      authors:
      - name: Carattoli A.
      - name: Hasman H.
      citationCount: 33
      date: '2020-01-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: 'PlasmidFinder and In Silico pMLST: Identification and Typing of Plasmid
        Replicons in Whole-Genome Sequencing (WGS)'
    note: null
    pmcid: null
    pmid: '31584170'
    type: []
    version: null
  relation: []
  toolType: []
  topic:
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Probes and primers
    uri: http://edamontology.org/topic_0632
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2022-03-22T18:57:49.937151Z'
  biotoolsCURIE: biotools:proteinortho
  biotoolsID: proteinortho
  collectionID: []
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Marcus Lechner
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Sonja J Prohaska
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Proteinortho is a tool to detect orthologous genes within different
    species
  documentation: []
  download:
  - note: null
    type: Source code
    url: https://gitlab.com/paulklemm_PHD/proteinortho/-/archive/master/proteinortho-master.zip
    version: null
  - note: Installation with dpkg (root privileges are required)
    type: Downloads page
    url: https://packages.debian.org/unstable/proteinortho
    version: null
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Homology-based gene prediction
      uri: http://edamontology.org/operation_3663
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: XML
        uri: http://edamontology.org/format_2332
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://www.bioinf.uni-leipzig.de/Software/proteinortho/
  homepage_status: 0
  language:
  - Perl
  - C++
  - Python
  lastUpdate: '2024-03-12T09:10:35.206389Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://gitlab.com/paulklemm_PHD/proteinortho
  - note: null
    type:
    - Issue tracker
    url: https://gitlab.com/paulklemm_PHD/proteinortho/-/issues?sort=created_date&state=opened
  - note: null
    type:
    - Galaxy service
    url: https://toolshed.g2.bx.psu.edu/repository?repository_id=584d8accff31aefe&changeset_revision=4850f0d15f01
  maturity: null
  name: Proteinortho
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: klemmp
  publication:
  - doi: 10.1186/1471-2105-12-124
    metadata:
      abstract: 'Background: Orthology analysis is an important part of data analysis
        in many areas of bioinformatics such as comparative genomics and molecular
        phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly
        increasing number of genomes that can be compared simultaneously, calls for
        efficient software tools as brute-force approaches with quadratic memory requirements
        become infeasible in practise. The rapid pace at which new data become available,
        furthermore, makes it desirable to compute genome-wide orthology relations
        for a given dataset rather than relying on relations listed in databases.Results:
        The program Proteinortho described here is a stand-alone tool that is geared
        towards large datasets and makes use of distributed computing techniques when
        run on multi-core hardware. It implements an extended version of the reciprocal
        best alignment heuristic. We apply Proteinortho to compute orthologous proteins
        in the complete set of all 717 eubacterial genomes available at NCBI at the
        beginning of 2009. We identified thirty proteins present in 99% of all bacterial
        proteomes.Conclusions: Proteinortho significantly reduces the required amount
        of memory for orthology analysis compared to existing tools, allowing such
        computations to be performed on off-the-shelf hardware. © 2011 Lechner et
        al; licensee BioMed Central Ltd.'
      authors:
      - name: Lechner M.
      - name: Findeiss S.
      - name: Steiner L.
      - name: Marz M.
      - name: Stadler P.F.
      - name: Prohaska S.J.
      citationCount: 656
      date: '2011-04-28T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'Proteinortho: Detection of (Co-)orthologs in large-scale analysis'
    note: null
    pmcid: PMC3114741
    pmid: '21526987'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  validated: 1
  version:
  - 6.0.14
- accessibility: Open access
  additionDate: '2015-01-22T11:31:41Z'
  biotoolsCURIE: biotools:predgpi
  biotoolsID: predgpi
  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: null
  - email: andrea@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Andrea Pierleoni
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: casadio@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Rita Casadio
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    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: 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:
    - Maintainer
    url: null
  description: Prediction system for GPI-anchored proteins.
  documentation:
  - note: null
    type:
    - General
    url: http://gpcr.biocomp.unibo.it/predgpi
  download: []
  editPermission:
    authors:
    - Nimna
    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: Prediction
    operation:
    - term: Molecular surface analysis
      uri: http://edamontology.org/operation_3351
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://gpcr.biocomp.unibo.it/predgpi
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:58:28.865213Z'
  license: null
  link: []
  maturity: Mature
  name: PredGPI
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1186/1471-2105-9-392
    metadata:
      abstract: 'Background: Several eukaryotic proteins associated to the extracellular
        leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI)
        anchor, which is linked to the C-terminal residue after a proteolytic cleavage
        occurring at the so called ω-site. Computational methods were developed to
        discriminate proteins that undergo this post-translational modification starting
        from their aminoacidic sequences. However more accurate methods are needed
        for a reliable annotation of whole proteomes. Results: Here we present PredGPI,
        a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support
        Vector Machine (SVM), is able to efficiently predict both the presence of
        the GPI-anchor and the position of the ω-site. PredGPI is trained on a non-redundant
        dataset of experimentally characterized GPI-anchored proteins whose annotation
        was carefully checked in the literature. Conclusion: PredGPI outperforms all
        the other previously described methods and is able to correctly replicate
        the results of previously published high-throughput experiments. PredGPI reaches
        a lower rate of false positive predictions with respect to other available
        methods and it is therefore a costless, rapid and accurate method for screening
        whole proteomes. © 2008 Pierleoni et al; licensee BioMed Central Ltd.'
      authors:
      - name: Pierleoni A.
      - name: Martelli P.
      - name: Casadio R.
      citationCount: 463
      date: '2008-09-23T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'PredGPI: A GPI-anchor predictor'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein secondary structure
    uri: http://edamontology.org/topic_3542
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  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: null
  additionDate: '2015-01-22T11:31:36Z'
  biotoolsCURIE: biotools:ft-comar
  biotoolsID: ft-comar
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: null
  cost: null
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: vassura@cs.unibo.it
    fundrefid: null
    gridid: null
    name: Marco Vassura
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Fault Tolerance Reconstruction of 3D Structure from Protein Contact
    Maps.
  documentation:
  - note: null
    type:
    - General
    url: http://bioinformatics.cs.unibo.it/FT-COMAR/index.html#usage
  download: []
  editPermission:
    authors:
    - OrangeOcean
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein contact map
        uri: http://edamontology.org/data_1547
      format:
      - term: Matrix format
        uri: http://edamontology.org/format_3033
    note: Reconstruction
    operation:
    - term: Protein structure prediction
      uri: http://edamontology.org/operation_0474
    output:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format:
      - term: PDB
        uri: http://edamontology.org/format_1476
  homepage: http://bioinformatics.cs.unibo.it/FT-COMAR/index.html
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:48:57.601250Z'
  license: null
  link: []
  maturity: null
  name: FT-COMAR
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1007/978-3-540-74126-8_4
    metadata:
      abstract: In this paper we describe FT-COMAR an algorithm that improves fault
        tolerance of our heuristic algorithm (COMAR) previously described for protein
        reconstruction [10]. The algorithm [COMAR-Contact Map Reconstruction] can
        reconstruct the three-dimensional (3D) structure of the real protein from
        its contact map with 100% efficiency when tested on 1760 proteins from different
        structural classes. Here we test the performances of COMAR on native contact
        maps when a perturbation with random errors is introduced. This is done in
        order to simulate possible scenarios of reconstruction from predicted (and
        therefore highly noised) contact maps. From our analysis we obtain that our
        algorithm performs better reconstructions on blurred contact maps when contacts
        are under predicted than over predicted. Moreover we modify the algorithm
        into FT-COMAR [Fault Tolerant-COMAR] in order to use it with incomplete contact
        maps. FT-COMAR can ignore up to 75% of the contact map and still recover from
        the remaining 25% entries a 3D structure whose root mean square deviation
        (RMSD) from the native one is less then 4 Å. Our results indicate that the
        quality more than the quantity of predicted contacts is relevant to the protein
        3D reconstruction and that some hints about "unsafe" areas in the predicted
        contact maps can be useful to improve reconstruction quality. For this, we
        implement a very simple filtering procedure to detect unsafe areas in contact
        maps and we show that by this and in the presences of errors the performance
        of the algorithm can be significantly improved. Furthermore, we show that
        both COMAR and FT-COMAR overcome a previous state-of-the-art algorithm for
        the same task [13]. © Springer-Verlag Berlin Heidelberg 2007.
      authors:
      - name: Vassura M.
      - name: Margara L.
      - name: Di Lena P.
      - name: Medri F.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 5
      date: '2007-01-01T00:00:00Z'
      journal: Lecture Notes in Computer Science (including subseries Lecture Notes
        in Artificial Intelligence and Lecture Notes in Bioinformatics)
      title: Fault tolerance for large scale protein 3D reconstruction from contact
        maps
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  - doi: null
    metadata:
      abstract: The prediction of the protein tertiary structure from solely its residue
        sequence (the so-called Protein Folding Problem) is one of the most challenging
        problems in Structural Bioinformatics. We focus on the protein residue contact
        map. When this map is assigned, it is possible to reconstruct the 3D structure
        of the protein backbone. The general problem of recovering a set of 3D coordinates
        consistent with some given contact map is known as a unit-disk-graph realization
        problem, and it has been recently proven to be NP-hard. In this paper, we
        describe a heuristic method (COMAR) that is able to reconstruct with an unprecedented
        rate (3-15 seconds) a 3D model that exactly matches the target contact map
        of a protein. Working with a nonredundant set of 1,760 proteins, we find that
        the scoring efficiency of finding a 3D model very close to the protein native
        structure depends on the threshold value adopted to compute the protein residue
        contact map. Contact maps whose threshold values range from 10 to 18 Angstroms
        allow reconstructing 3D models that are very similar to the proteins' native
        structure. © 2008 IEEE.
      authors:
      - name: Vassura M.
      - name: Margara L.
      - name: Di Lena P.
      - name: Medri F.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 71
      date: '2008-07-01T00:00:00Z'
      journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
      title: Reconstruction of 3D structures from protein contact maps
    note: null
    pmcid: null
    pmid: '18670040'
    type:
    - Other
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  - term: Protein secondary structure
    uri: http://edamontology.org/topic_3542
  validated: 1
  version:
  - '1.0'
next: ?page=2
previous: null
