count: 29617
list:
- accessibility: Open access
  additionDate: '2024-05-01T09:50:49.358425Z'
  biotoolsCURIE: biotools:te-tss
  biotoolsID: te-tss
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: zhangxiaoou@tongji.edu.cn
    fundrefid: null
    gridid: null
    name: Xiao-Ou Zhang
    note: null
    orcidid: https://orcid.org/0000-0002-2027-1313
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Xiaobing Gu
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Integrated data resource of human and mouse transposable element (TE)-derived
    transcription start site (TSS).
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: trans-regulatory element prediction
      uri: http://edamontology.org/operation_0443
    - term: Promoter prediction
      uri: http://edamontology.org/operation_0440
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output: []
  homepage: http://xozhanglab.com/TETSS
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-01T09:50:49.361046Z'
  license: null
  link: []
  maturity: null
  name: TE-TSS
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/NAR/GKAD1048
    metadata:
      abstract: Transposable elements (TEs)ãreãbundant in the genomeãnd serveãs crucial
        regulatory elements. Some TEs functionãs epigenetically regulated promoters,ãnd
        these TE-derived transcription start sites (TSSs) playã crucial role in regulating
        genesãssociated with specific functions, suchãs cancerãnd embry ogenesis.
        Ho w e v er, the lack ofãnãccessible database that sy stematically gathers
        TE-deriv ed TSS data isã current research gap. Toãddress this, we established
        TE-TSS,ãn integrated data resource of humanãnd mouse TE-derived TSSs ( http://
        xozhanglab.com/ TETSS ). TE-TSS has compiled 2681 RNA sequencing datasets,
        spanning various tissues, cell linesãnd de v elopmentãl stãges. From these,
        we identified 5768 human TE-derived TSSsãnd 2797 mouse TE-derived TSSs, with
        47%ãnd 38% being experimentally validated, respectively. TE-TSS enables comprehensiv
        e e xploration of TSS usage in div erse samples, pro viding insights into
        tissue-specific gene expression patternsãnd transcriptional regulatory elements.
        Furthermore, TE-TSS compares TE-derived TSS regionsãcross 15 mammalian species,
        enhancing our understanding of their e v olutionaryãnd functionalãspects.
        T he establishment of TE-TSS fãcilitates further in v estigations into the
        roles of TEs in shaping the transcriptomic landscapeãnd offers valuable resources
        for comprehending their involvement in diverse biological processes.
      authors:
      - name: Gu X.
      - name: Wang M.
      - name: Zhang X.-O.
      citationCount: 0
      date: '2024-01-05T00:00:00Z'
      journal: Nucleic Acids Research
      title: TE-TSS:ãn int egrãt ed data resour ce of humanãnd mouse transposable
        element (TE)-derived transcription start site (TSS)
    note: null
    pmcid: PMC10767810
    pmid: '37956335'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Transcription factors and regulatory sites
    uri: http://edamontology.org/topic_0749
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Epigenetics
    uri: http://edamontology.org/topic_3295
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-01T09:12:32.628028Z'
  biotoolsCURIE: biotools:celldialog
  biotoolsID: celldialog
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Lihong Peng
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Computational framework for ligand-receptor-mediated cell-cell communication
    analysis III.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene expression profiling
      uri: http://edamontology.org/operation_0314
    - term: Feature extraction
      uri: http://edamontology.org/operation_3937
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    output: []
  homepage: https://github.com/plhhnu/CellDialog
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-05-01T09:12:32.630915Z'
  license: null
  link: []
  maturity: null
  name: CellDialog
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1109/JBHI.2023.3333828
    metadata:
      abstract: 'Intercellularcommunication significantly influences tumor progression,
        metastasis, and therapy resistance. An intercellular communication inference
        method includes two main procedures: ligand-receptor interaction (LRI) curation
        and LRI-mediated intercellular communication strength measurement. The construction
        of a comprehensive, high-confident and well-organized LRI database contributes
        to intercellular communication inference. Here, we developed a computational
        framework named CellDialog to reconstruct an intercellular connectivity network
        based on the combined expression of ligands and receptors involved in sender
        and receiver cells. CellDialog first captures high-confident LRIs through
        LRI feature extraction, feature selection, and classification. Furthermore,
        CellDialog uses a three-point estimation approach to measure the LRI-mediated
        intercellular communication strength by combining LRI filtering and single-cell
        RNA sequencing data. A comparison analysis of CellDialog and the other tools
        was conducted, and it was found that CellDialog can efficiently decode intercellular
        communications. Additionally, CellDialog offers a heatmap view and network
        view for intercellular communication visualization. In summary, CellDialog
        provides a tool that allows researchers to analyze intercellular signal transduction.'
      authors:
      - name: Peng L.
      - name: Xiong W.
      - name: Han C.
      - name: Li Z.
      - name: Chen X.
      citationCount: 4
      date: '2024-01-01T00:00:00Z'
      journal: IEEE Journal of Biomedical and Health Informatics
      title: 'CellDialog: A Computational Framework for Ligand-Receptor-Mediated Cell-Cell
        Communication Analysis'
    note: null
    pmcid: null
    pmid: '37976192'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-01T09:09:56.514175Z'
  biotoolsCURIE: biotools:mesoscope
  biotoolsID: mesoscope
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: L Autin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: D S Goodsell
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Mesoscope is a web-based data integration and curation tool. Mesoscope
    allows users to begin with a listing of molecules (such as data from proteomics),
    and to use resources at UniProt and the PDB to identify, prepare and validate
    appropriate structures and representations for each molecule, ultimately producing
    a portable output file used by CellPACK and other modeling tools for generation
    of 3D models of the biological mesoscale.
  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: Mapping
      uri: http://edamontology.org/operation_2429
    output: []
  homepage: http://mesoscope.scripps.edu/beta
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-01T09:09:56.516367Z'
  license: null
  link: []
  maturity: null
  name: Mesoscope
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.2312/MOLVA.20201098
    metadata:
      abstract: Interest is growing for 3D models of the biological mesoscale, the
        intermediate scale between the nanometer scale of molecular structure and
        micrometer scale of cellular biology. However, it is currently difficult to
        gather, curate and integrate all the data required to define such models.
        To address this challenge we developed Mesoscope (mesoscope.scripps.edu/beta),
        a web-based data integration and curation tool. Mesoscope allows users to
        begin with a listing of molecules (such as data from proteomics), and to use
        resources at UniProt and the PDB to identify, prepare and validate appropriate
        structures and representations for each molecule, ultimately producing a portable
        output file used by CellPACK and other modeling tools for generation of 3D
        models of the biological mesoscale. The availability of this tool has proven
        essential in several exploratory applications, given the high complexity of
        mesoscale models and the heterogeneity of the available data sources.
      authors:
      - name: Autin L.
      - name: Maritan M.
      - name: Barbaro B.A.
      - name: Gardner A.
      - name: Olson A.J.
      - name: Sanner M.
      - name: Goodsell D.S.
      citationCount: 7
      date: '2020-01-01T00:00:00Z'
      journal: MolVA 2020 - Workshop on Molecular Graphics and Visual Analysis of
        Molecular Data
      title: 'Mesoscope: AWeb-based Tool for Mesoscale Data Integration and Curation'
    note: null
    pmcid: PMC10624244
    pmid: '37928321'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Structure analysis
    uri: http://edamontology.org/topic_0081
  - term: Data submission, annotation and curation
    uri: http://edamontology.org/topic_0219
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  - term: Tomography
    uri: http://edamontology.org/topic_3452
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-01T09:07:33.334601Z'
  biotoolsCURIE: biotools:plm-arg
  biotoolsID: plm-arg
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: weida.tong@fda.hhs.gov
    fundrefid: null
    gridid: null
    name: Weida Tong
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: zhichao.liu@boehringer-ingelheim.com
    fundrefid: null
    gridid: null
    name: Zhichao Liu
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: tlshi@bio.ecnu.edu.cn
    fundrefid: null
    gridid: null
    name: Tieliu Shi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Antibiotic resistance gene identification using a pretrained protein
    language model.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Antimicrobial resistance prediction
      uri: http://edamontology.org/operation_3482
    - term: Sequence trimming
      uri: http://edamontology.org/operation_3192
    - term: Protein modelling
      uri: http://edamontology.org/operation_0477
    output: []
  homepage: http://www.unimd.org/PLM-ARG
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-05-01T09:07:33.336652Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/Junwu302/PLM-ARG
  maturity: null
  name: PLM-ARG
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOINFORMATICS/BTAD690
    metadata:
      abstract: 'Motivation: Antibiotic resistance presents a formidable global challenge
        to public health and the environment. While considerable endeavors have been
        dedicated to identify antibiotic resistance genes (ARGs) for assessing the
        threat of antibiotic resistance, recent extensive investigations using metagenomic
        and metatranscriptomic approaches have unveiled a noteworthy concern. A significant
        fraction of proteins defies annotation through conventional sequence similarity-based
        methods, an issue that extends to ARGs, potentially leading to their under-recognition
        due to dissimilarities at the sequence level. Results: Herein, we proposed
        an Artificial Intelligence-powered ARG identification framework using a pretrained
        large protein language model, enabling ARG identification and resistance category
        classification simultaneously. The proposed PLM-ARG was developed based on
        the most comprehensive ARG and related resistance category information (>28K
        ARGs and associated 29 resistance categories), yielding Matthew''s correlation
        coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy.
        Furthermore, the PLM-ARG model was verified using an independent validation
        set and achieved an MCC of 0.838, outperforming other publicly available ARG
        prediction tools with an improvement range of 51.8%-107.9%. Moreover, the
        utility of the proposed PLM-ARG model was demonstrated by annotating resistance
        in the UniProt database and evaluating the impact of ARGs on the Earth''s
        environmental microbiota.'
      authors:
      - name: Wu J.
      - name: Ouyang J.
      - name: Qin H.
      - name: Zhou J.
      - name: Roberts R.
      - name: Siam R.
      - name: Wang L.
      - name: Tong W.
      - name: Liu Z.
      - name: Shi T.
      citationCount: 0
      date: '2023-11-01T00:00:00Z'
      journal: Bioinformatics
      title: 'PLM-ARG: antibiotic resistance gene identification using a pretrained
        protein language model'
    note: null
    pmcid: PMC10676515
    pmid: '37995287'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Metatranscriptomics
    uri: http://edamontology.org/topic_3941
  - term: Metagenomics
    uri: http://edamontology.org/topic_3174
  - term: Protein expression
    uri: http://edamontology.org/topic_0108
  - term: Microbial ecology
    uri: http://edamontology.org/topic_3697
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-01T09:04:12.913221Z'
  biotoolsCURIE: biotools:afrbase
  biotoolsID: afrbase
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: manish@south.du.ac.in
    fundrefid: null
    gridid: null
    name: Manish Kumar
    note: null
    orcidid: https://orcid.org/0000-0002-7936-9892
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: antoniop.camargo@lbl.gov
    fundrefid: null
    gridid: null
    name: Antonio Pedro Camargo
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Database of protein mutations responsible for antifungal resistance.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Text mining
      uri: http://edamontology.org/operation_0306
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    output: []
  homepage: http://proteininformatics.org/mkumar/afrbase/
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-01T09:04:12.915997Z'
  license: null
  link: []
  maturity: null
  name: AFRbase
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOINFORMATICS/BTAD677
    metadata:
      abstract: 'Motivation: Fungal pathogens are known to cause life threatening
        invasive infections with rising global mortality rates. Besides, the indiscriminate
        use of antifungals in both clinics and agriculture has promoted antifungal
        drug resistance in the last decade. Fungi can show drug resistance by a variety
        of mechanisms. But primary driver in all these hitherto documented mechanisms
        is stable and heritable point mutations in the key proteins. Therefore, cataloguing
        mutations that can confer resistance is the first step toward understanding
        the mechanisms leading to the emergence of antifungal resistance. Results:
        In the present, work we have described a database of all the mutations responsible
        for antifungal resistance. Named as antifungal resistance database (AFRbase),
        it is better than the existing databases of antifungal resistance namely,
        FunResDB and MARDy which have a limited scope and inadequate information.
        Data of AFRbase was collected using both text mining and manual curation.
        AFRbase provides a separate window for visualization of mutations in the 2D
        and 3D formats making it easy for researchers to analyze the mutation data
        and ensures interoperability with other standard molecular biology databases
        like NCBI and UniProtKB. We hope AFRbase can be useful to both clinicians
        and basic biomedical scientists as we envision it as an important resource
        for genotypic susceptibility testing of fungi and to study/predict the course
        of evolution of antifungal resistance. The current version of AFRbase contains
        manually curated 3691 unique mutations present in 29 proteins of 32 fungal
        species along with the information of drugs against which resistance is caused.'
      authors:
      - name: Jain A.
      - name: Singhal N.
      - name: Kumar M.
      citationCount: 0
      date: '2023-11-01T00:00:00Z'
      journal: Bioinformatics
      title: 'AFRbase: a database of protein mutations responsible for antifungal
        resistance'
    note: null
    pmcid: PMC10656092
    pmid: '37947313'
    type: []
    version: null
  relation: []
  toolType:
  - Database portal
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-30T13:12:13.495572Z'
  biotoolsCURIE: biotools:virushound-i
  biotoolsID: virushound-i
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: beltran.lissabet.jf@gmail.com
    fundrefid: null
    gridid: null
    name: Jorge F Beltrán
    note: null
    orcidid: https://orcid.org/0000-0003-2703-9629
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Fernanda Parraguez-Contreras
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Prediction of viral proteins involved in the evasion of host adaptive
    immune response using the random forest algorithm and generative adversarial network
    for data augmentation.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Virulence prediction
      uri: http://edamontology.org/operation_3461
    - term: Peptide immunogenicity prediction
      uri: http://edamontology.org/operation_0252
    - term: Epitope mapping
      uri: http://edamontology.org/operation_0416
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    output: []
  homepage: https://www.biochemintelli.com/VirusHound-I
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-30T13:12:13.499807Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/jfbldevs/virushound-I
  maturity: null
  name: VirusHound-I
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIB/BBAD434
    metadata:
      abstract: Throughout evolution, pathogenic viruses have developed different
        strategies to evade the response of the adaptive immune system. To carry out
        successful replication, some pathogenic viruses encode different proteins
        that manipulate the molecular mechanisms of host cells. Currently, there are
        different bioinformatics tools for virus research; however, none of them focus
        on predicting viral proteins that evade the adaptive system. In this work,
        we have developed a novel tool based on machine and deep learning for predicting
        this type of viral protein named VirusHound-I. This tool is based on a model
        developed with the multilayer perceptron algorithm using the dipeptide composition
        molecular descriptor. In this study, we have also demonstrated the robustness
        of our strategy for data augmentation of the positive dataset based on generative
        antagonistic networks. During the 10-fold cross-validation step in the training
        dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943
        F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew’s
        correlation coefficient and 0.989 AUC. On the other hand, during the testing
        step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0
        specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew’s correlation coefficient
        and 1.0 AUC. Taking this model into account, we have developed a tool called
        VirusHound-I that makes it possible to predict viral proteins that evade the
        host’s adaptive immune system. We believe that VirusHound-I can be very useful
        in accelerating studies on the molecular mechanisms of evasion of pathogenic
        viruses, as well as in the discovery of therapeutic targets.
      authors:
      - name: Beltran J.F.
      - name: Belen L.H.
      - name: Farias J.G.
      - name: Zamorano M.
      - name: Lefin N.
      - name: Miranda J.
      - name: Parraguez-Contreras F.
      citationCount: 1
      date: '2024-01-01T00:00:00Z'
      journal: Briefings in Bioinformatics
      title: 'VirusHound-I: prediction of viral proteins involved in the evasion of
        host adaptive immune response using the random forest algorithm and generative
        adversarial network for data augmentation'
    note: null
    pmcid: PMC10753651
    pmid: '38033292'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Immunoinformatics
    uri: http://edamontology.org/topic_3948
  - term: Immunogenetics
    uri: http://edamontology.org/topic_3930
  - term: Immunoproteins and antigens
    uri: http://edamontology.org/topic_2830
  - term: Pharmacology
    uri: http://edamontology.org/topic_0202
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-30T13:03:54.037389Z'
  biotoolsCURIE: biotools:jsonwp
  biotoolsID: jsonwp
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: jernigan@iastate.edu
    fundrefid: null
    gridid: null
    name: Robert L Jernigan
    note: null
    orcidid: https://orcid.org/0000-0003-0996-8360
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Mesih Kilinc
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'Static website generator for protein bioinformatics research.


    It is a JS application that creates a website based on the JSON data.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Parsing
      uri: http://edamontology.org/operation_1812
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output: []
  homepage: http://jsonwp.onrender.com
  homepage_status: 0
  language:
  - JavaScript
  lastUpdate: '2024-04-30T13:03:54.039616Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/MesihK/react-json-wpbuilder
  maturity: null
  name: JSONWP
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOADV/VBAD154
    metadata:
      abstract: 'Motivation: Presenting the integrated results of bioinformatics research
        can be challenging and requires sophisticated visualization components, which
        can be time-consuming to develop. This article presents a new way to effectively
        communicate research findings. Results: We have developed a static web page
        generator, JSONWP, which is specifically designed for protein bioinformatics
        research. Utilizing React (a JavaScript library used to build interactive
        and dynamic user interfaces for web applications), we have integrated publicly
        available bioinformatics visualization components to provide standardized
        access to these components. JSON (or JavaScript Object Notation, is a lightweight
        textual data format often used to structure and exchange information between
        different software tools.) is used as the input source due to its ability
        to represent nearly all types of data using key and value pairs. This allows
        researchers to use their preferred programming language to create a JSON representation,
        which can then be converted into a website by JSONWP. No server or domain
        is required to host the website, as only the publicly accessible JSON file
        is required. Conclusions: Overall, JSONWP provides a useful new tool for bioinformatics
        researchers to effectively communicate their findings. The open-source implementation
        is located at https://github.com/MesihK/react-json-wpbuilder, and the tool
        can be used at jsonwp.onrender.com.'
      authors:
      - name: Kilinc M.
      - name: Jia K.
      - name: Jernigan R.L.
      citationCount: 0
      date: '2023-01-01T00:00:00Z'
      journal: Bioinformatics Advances
      title: 'JSONWP: a static website generator for protein bioinformatics research'
    note: null
    pmcid: PMC10613403
    pmid: '37904893'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Bioinformatics
    uri: http://edamontology.org/topic_0091
  - term: Software engineering
    uri: http://edamontology.org/topic_3372
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-30T12:45:47.267049Z'
  biotoolsCURIE: biotools:cell4d
  biotoolsID: cell4d
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: jparkin@sickkids.ca
    fundrefid: null
    gridid: null
    name: John Parkinson
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Donny Chan
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Cell4D is a Linux C++-based graphical spatial stochatic cell simulator
    capable of simulating a wide variety of cellular pathways. Molecules are simulated
    as particles within a user-defined simulation space under a Smoluchowski-based
    reaction-diffusion system on a static time-step basis.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Pathway analysis
      uri: http://edamontology.org/operation_3928
    - term: Modelling and simulation
      uri: http://edamontology.org/operation_2426
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: https://github.com/ParkinsonLab/cell4d
  homepage_status: 0
  language:
  - C++
  lastUpdate: '2024-04-30T12:45:47.269277Z'
  license: null
  link: []
  maturity: null
  name: Cell4D
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/s12859-024-05739-0
    metadata:
      abstract: 'Background: With the generation of vast compendia of biological datasets,
        the challenge is how best to interpret ‘omics data alongside biochemical and
        other small-scale experiments to gain meaningful biological insights. Key
        to this challenge are computational methods that enable domain-users to generate
        novel hypotheses that can be used to guide future experiments. Of particular
        interest are flexible modeling platforms, capable of simulating a diverse
        range of biological systems with low barriers of adoption to those with limited
        computational expertise. Results: We introduce Cell4D, a spatial-temporal
        modeling platform combining a robust simulation engine with integrated graphics
        visualization, a model design editor, and an underlying XML data model capable
        of capturing a variety of cellular functions. Cell4D provides an interactive
        visualization mode, allowing intuitive feedback on model behavior and exploration
        of novel hypotheses, together with a non-graphics mode, compatible with high
        performance cloud compute solutions, to facilitate generation of statistical
        data. To demonstrate the flexibility and effectiveness of Cell4D, we investigate
        the dynamics of CEACAM1 localization in T-cell activation. We confirm the
        importance of Ca2+ microdomains in activating calmodulin and highlight a key
        role of activated calmodulin on the surface expression of CEACAM1. We further
        show how lymphocyte-specific protein tyrosine kinase can help regulate this
        cell surface expression and exploit spatial modeling features of Cell4D to
        test the hypothesis that lipid rafts regulate clustering of CEACAM1 to promote
        trans-binding to neighbouring cells. Conclusions: Through demonstrating its
        ability to test and generate hypotheses, Cell4D represents an effective tool
        to help integrate knowledge across diverse, large and small-scale datasets.'
      authors:
      - name: Chan D.
      - name: Cromar G.L.
      - name: Taj B.
      - name: Parkinson J.
      citationCount: 0
      date: '2024-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'Cell4D: a general purpose spatial stochastic simulator for cellular
        pathways'
    note: null
    pmcid: null
    pmid: null
    type: []
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  - term: Lipids
    uri: http://edamontology.org/topic_0153
  - term: Transcription factors and regulatory sites
    uri: http://edamontology.org/topic_0749
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-30T12:35:46.193814Z'
  biotoolsCURIE: biotools:jeasytfm
  biotoolsID: jeasytfm
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: philippe.carl@unistra.fr
    fundrefid: null
    gridid: null
    name: Philippe Carl
    note: null
    orcidid: https://orcid.org/0000-0001-8063-3294
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Philippe Rondé
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Open-source software package for the analysis of large 2D TFM data
    within ImageJ.
  documentation:
  - note: null
    type:
    - User manual
    url: http://questpharma.u-strasbg.fr/JEasyTFM_manual.html
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Splitting
      uri: http://edamontology.org/operation_3359
    - term: Editing
      uri: http://edamontology.org/operation_3096
    - term: Image analysis
      uri: http://edamontology.org/operation_3443
    output: []
  homepage: http://questpharma.u-strasbg.fr/JEasyTFM.html
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-30T12:35:46.196223Z'
  license: null
  link: []
  maturity: null
  name: JeasyTFM
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOADV/VBAD156
    metadata:
      abstract: 'Motivation: Cells adhering to the extracellular matrix can sense
        and respond to a wide variety of chemical and physical features of the adhesive
        surface. Traction force microscopy (TFM) allows determining the tensile forces
        exerted by the cells on their substrate with high resolution. Results: To
        allow broad access of this techniques to cell biology laboratories we developed
        JeasyTFM, an open-source ImageJ package able to process multi-color and multi-position
        time-lapse pictures thus suitable for the automatic analysis of large TFM
        data.'
      authors:
      - name: Carl P.
      - name: Ronde P.
      citationCount: 0
      date: '2023-01-01T00:00:00Z'
      journal: Bioinformatics Advances
      title: 'JEasyTFM: an open-source software package for the analysis of large
        2D TFM data within ImageJ'
    note: null
    pmcid: PMC10625472
    pmid: '37928344'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  - term: Cytometry
    uri: http://edamontology.org/topic_3934
  - term: Physics
    uri: http://edamontology.org/topic_3318
  - term: Protein expression
    uri: http://edamontology.org/topic_0108
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-30T12:33:30.345341Z'
  biotoolsCURIE: biotools:syntenyplotter
  biotoolsID: syntenyplotter
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: m.farre-belmonte@kent.ac.uk
    fundrefid: null
    gridid: null
    name: Marta Farré
    note: null
    orcidid: https://orcid.org/0000-0001-9170-5767
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Sarah Quigley
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: User-friendly R package to visualize genome synteny, ideal for both
    experienced and novice bioinformaticians.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Formatting
      uri: http://edamontology.org/operation_0335
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: https://farre-lab.github.io/syntenyPlotteR/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-04-30T12:33:30.347736Z'
  license: null
  link: []
  maturity: null
  name: syntenyPlotteR
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOADV/VBAD161
    metadata:
      abstract: 'Motivation: The rapid increase in the number of chromosome-scale
        genome assemblies has renewed interest in chromosome evolution studies. The
        visualization of syntenic relationships between genomes is a crucial initial
        step in the study of chromosome rearrangements and evolution. There are few
        tools available that serve this purpose, and they can be difficult to learn.
        Moreover, these tools are limited in the number of species comparisons that
        can be visualized and the size of chromosome rearrangements identified. Thus,
        the development of novel visualization tools is in strong need. Results: Here,
        we present syntenyPlotteR, an R package developed to visualize homologous
        synteny blocks in a pairwise or multispecies manner. This package contains
        three functions that allow users to generate publication-quality representations
        of syntenic relationships easily and quickly between genomes of interest.'
      authors:
      - name: Quigley S.
      - name: Damas J.
      - name: Larkin D.M.
      - name: Farre M.
      citationCount: 2
      date: '2023-01-01T00:00:00Z'
      journal: Bioinformatics Advances
      title: 'syntenyPlotteR: a user-friendly R package to visualize genome synteny,
        ideal for both experienced and novice bioinformaticians'
    note: null
    pmcid: PMC10660287
    pmid: '38023328'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  - term: Structural variation
    uri: http://edamontology.org/topic_3175
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: Evolutionary biology
    uri: http://edamontology.org/topic_3299
  validated: 0
  version: []
next: ?page=2
previous: null
