count: 29877
list:
- accessibility: Open access
  additionDate: '2024-05-18T15:25:26.545304Z'
  biotoolsCURIE: biotools:circsi-ssl
  biotoolsID: circsi-ssl
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: zouquan@nclab.net
    fundrefid: null
    gridid: null
    name: Quan Zou
    note: null
    orcidid: https://orcid.org/0000-0001-6406-1142
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Chao Cao
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: circRNA-binding site identification based on self-supervised learning.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Binding site prediction
      uri: http://edamontology.org/operation_2575
    - term: Feature extraction
      uri: http://edamontology.org/operation_3937
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    output: []
  homepage: https://github.com/cc646201081/CircSI-SSL
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-05-18T15:25:26.547617Z'
  license: MIT
  link: []
  maturity: null
  name: CircSI-SSL
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/bioinformatics/btae004
    metadata:
      abstract: 'Motivation: In recent years, circular RNAs (circRNAs), the particular
        form of RNA with a closed-loop structure, have attracted widespread attention
        due to their physiological significance (they can directly bind proteins),
        leading to the development of numerous protein site identification algorithms.
        Unfortunately, these studies are supervised and require the vast majority
        of labeled samples in training to produce superior performance. But the acquisition
        of sample labels requires a large number of biological experiments and is
        difficult to obtain. Results: To resolve this matter that a great deal of
        tags need to be trained in the circRNA-binding site prediction task, a self-supervised
        learning binding site identification algorithm named CircSI-SSL is proposed
        in this article. According to the survey, this is unprecedented in the research
        field. Specifically, CircSI-SSL initially combines multiple feature coding
        schemes and employs RNA_Transformer for cross-view sequence prediction (self-supervised
        task) to learn mutual information from the multi-view data, and then fine-tuning
        with only a few sample labels. Comprehensive experiments on six widely used
        circRNA datasets indicate that our CircSI-SSL algorithm achieves excellent
        performance in comparison to previous algorithms, even in the extreme case
        where the ratio of training data to test data is 1:9. In addition, the transplantation
        experiment of six linRNA datasets without network modification and hyperparameter
        adjustment shows that CircSI-SSL has good scalability. In summary, the prediction
        algorithm based on self-supervised learning proposed in this article is expected
        to replace previous supervised algorithms and has more extensive application
        value.'
      authors:
      - name: Cao C.
      - name: Wang C.
      - name: Yang S.
      - name: Zou Q.
      citationCount: 1
      date: '2024-01-01T00:00:00Z'
      journal: Bioinformatics
      title: 'CircSI-SSL: CircRNA-binding site identification based on self-supervised
        learning'
    note: null
    pmcid: PMC10789309
    pmid: '38180876'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: RNA
    uri: http://edamontology.org/topic_0099
  - term: RNA immunoprecipitation
    uri: http://edamontology.org/topic_3794
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T15:23:08.553789Z'
  biotoolsCURIE: biotools:seedmatchr
  biotoolsID: seedmatchr
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: tareian.cazares@lilly.com
    fundrefid: null
    gridid: null
    name: Tareian Cazares
    note: null
    orcidid: https://orcid.org/0000-0003-4624-6156
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Hatice Gulcin Ozer
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: The goal of SeedMatchR is to help users identify potential seed-mediated
    effects in their RNAseq data.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Differential gene expression profiling
      uri: http://edamontology.org/operation_3223
    - term: miRNA expression analysis
      uri: http://edamontology.org/operation_3792
    - term: siRNA binding specificity prediction
      uri: http://edamontology.org/operation_0465
    - term: miRNA target prediction
      uri: http://edamontology.org/operation_0463
    output: []
  homepage: https://github.com/tacazares/SeedMatchR
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-05-18T15:23:08.555952Z'
  license: MIT
  link: []
  maturity: null
  name: SeedMatchR
  operatingSystem:
  - Mac
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/bioinformatics/btae011
    metadata:
      abstract: 'Motivation: On-target gene knockdown, using siRNA, ideally results
        from binding fully complementary regions in mRNA transcripts to induce direct
        cleavage. Off-target siRNA gene knockdown can occur through several modes,
        one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated
        off-target effects occur when the ∼8 nucleotides at the 5'' end of the guide
        strand, called a seed region, bind the 3'' untranslated regions of mRNA, causing
        reduced translation. Experiments using siRNA knockdown paired with RNA-seq
        can be used to detect siRNA sequences with off-target effects driven by the
        seed region. However, there are limited computational tools designed specifically
        for detecting siRNA off-target effects mediated by the seed region in differential
        gene expression experiments. Results: SeedMatchR is an R package developed
        to provide users a single, unified resource for detecting and visualizing
        seed-mediated off-target effects of siRNA using RNA-seq experiments. SeedMatchR
        is designed to extend current differential expression analysis tools, such
        as DESeq2, by annotating results with predicted seed matches. Using publicly
        available data, we demonstrate the ability of SeedMatchR to detect cumulative
        changes in differential gene expression attributed to siRNA seed region activity.
        Availability: SeedMatchR is available on CRAN. Documentation and example workflows
        are available through the SeedMatchR GitHub page at https://github.com/tacazares/SeedMatchR.'
      authors:
      - name: Cazares T.
      - name: Higgs R.E.
      - name: Wang J.
      - name: Ozer H.G.
      citationCount: 0
      date: '2024-01-01T00:00:00Z'
      journal: Bioinformatics
      title: 'SeedMatchR: identify off-target effects mediated by siRNA seed regions
        in RNA-seq experiments'
    note: null
    pmcid: PMC10799297
    pmid: '38192001'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Functional, regulatory and non-coding RNA
    uri: http://edamontology.org/topic_0659
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Protein expression
    uri: http://edamontology.org/topic_0108
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T15:19:43.372407Z'
  biotoolsCURIE: biotools:disoflag
  biotoolsID: disoflag
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: bliu@bliulab.net
    fundrefid: null
    gridid: null
    name: Bin Liu
    note: null
    orcidid: https://orcid.org/0000-0001-7338-5739
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Yihe Pang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Prediction of protein intrinsic disorder and its functions using graph-based
    interaction protein language model.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein disorder prediction
      uri: http://edamontology.org/operation_3904
    - term: Protein secondary structure prediction (coils)
      uri: http://edamontology.org/operation_0470
    - term: Protein interaction prediction
      uri: http://edamontology.org/operation_2492
    - term: RNA-binding protein prediction
      uri: http://edamontology.org/operation_3901
    - term: DNA-binding protein prediction
      uri: http://edamontology.org/operation_3900
    output: []
  homepage: https://github.com/YihePang/DisoFLAG
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-05-18T15:19:43.374652Z'
  license: MIT
  link: []
  maturity: null
  name: DisoFLAG
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/s12915-023-01803-y
    metadata:
      abstract: Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally
        important proteins and regions that exist as highly dynamic conformations
        under natural physiological conditions. IDPs/IDRs exhibit a broad range of
        molecular functions, and their functions involve binding interactions with
        partners and remaining native structural flexibility. The rapid increase in
        the number of proteins in sequence databases and the diversity of disordered
        functions challenge existing computational methods for predicting protein
        intrinsic disorder and disordered functions. A disordered region interacts
        with different partners to perform multiple functions, and these disordered
        functions exhibit different dependencies and correlations. In this study,
        we introduce DisoFLAG, a computational method that leverages a graph-based
        interaction protein language model (GiPLM) for jointly predicting disorder
        and its multiple potential functions. GiPLM integrates protein semantic information
        based on pre-trained protein language models into graph-based interaction
        units to enhance the correlation of the semantic representation of multiple
        disordered functions. The DisoFLAG predictor takes amino acid sequences as
        the only inputs and provides predictions of intrinsic disorder and six disordered
        functions for proteins, including protein-binding, DNA-binding, RNA-binding,
        ion-binding, lipid-binding, and flexible linker. We evaluated the predictive
        performance of DisoFLAG following the Critical Assessment of protein Intrinsic
        Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers
        accurate and comprehensive predictions of disordered functions, extending
        the current coverage of computationally predicted disordered function categories.
        The standalone package and web server of DisoFLAG have been established to
        provide accurate prediction tools for intrinsic disorders and their associated
        functions.
      authors:
      - name: Pang Y.
      - name: Liu B.
      citationCount: 0
      date: '2024-12-01T00:00:00Z'
      journal: BMC Biology
      title: 'DisoFLAG: accurate prediction of protein intrinsic disorder and its
        functions using graph-based interaction protein language model'
    note: null
    pmcid: PMC10762911
    pmid: '38166858'
    type: []
    version: null
  relation: []
  toolType:
  - Workflow
  topic:
  - term: Physiology
    uri: http://edamontology.org/topic_3300
  - term: Protein disordered structure
    uri: http://edamontology.org/topic_3538
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: NMR
    uri: http://edamontology.org/topic_0593
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T15:05:00.968261Z'
  biotoolsCURIE: biotools:planner
  biotoolsID: planner
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Cong Wang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Multi-scale deep language model for the origins of replication site
    prediction.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output: []
  homepage: http://planner.unimelb-biotools.cloud.edu.au/
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-18T15:05:00.970796Z'
  license: null
  link: []
  maturity: null
  name: PLANNER
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1109/jbhi.2024.3349584
    metadata:
      abstract: Origins of replication sites (ORIs) are crucial genomic regions where
        DNA replication initiation takes place, playing pivotal roles in fundamental
        biological processes like cell division, gene expression regulation, and DNA
        integrity. Accurate identification of ORIs is essential for comprehending
        cell replication, gene expression, and mutation-related diseases. However,
        experimental approaches for ORI identification are often expensive and time-consuming,
        leading to the growing popularity of computational methods. In this study,
        we present PLANNER (DeeP LeArNiNg prEdictor for ORI), a novel approach for
        species-specific and cell-specific prediction of eukaryotic ORIs. PLANNER
        uses the multi-scale k-tuple sequences as input and employs the DNABERT pre-training
        model with transfer learning and ensemble learning strategies to train accurate
        predictive models. Extensive empirical test results demonstrate that PLANNER
        achieved superior predictive performance compared to state-of-the-art approaches,
        including iOri-Euk, Stack-ORI, and ORI-Deep, within specific cell types and
        across different cell types. Furthermore, by incorporating an interpretable
        analysis mechanism, we provide insights into the learned patterns, facilitating
        the mapping from discovering important sequential determinants to comprehensively
        analysing their biological functions.
      authors:
      - name: Wang C.
      - name: He Z.
      - name: Jia R.
      - name: Pan S.
      - name: Coin L.J.M.
      - name: Song J.
      - name: Li F.
      citationCount: 1
      date: '2024-04-01T00:00:00Z'
      journal: IEEE Journal of Biomedical and Health Informatics
      title: 'PLANNER: A Multi-Scale Deep Language Model for the Origins of Replication
        Site Prediction'
    note: null
    pmcid: null
    pmid: '38190667'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T15:03:19.968392Z'
  biotoolsCURIE: biotools:cloude
  biotoolsID: cloude
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: acampelodossanto@fau.edu
    fundrefid: null
    gridid: null
    name: Andre Luiz Campelo dos Santos
    note: null
    orcidid: https://orcid.org/0000-0002-5577-1967
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: CLOUDe is a suite of machine learning methods for predicting evolutionary
    targets of gene deletion events from expression data.
  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: Gene-set enrichment analysis
      uri: http://edamontology.org/operation_2436
    - term: Gene tree construction
      uri: http://edamontology.org/operation_0553
    output: []
  homepage: https://github.com/anddssan/CLOUDe
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-05-18T15:03:19.970577Z'
  license: null
  link: []
  maturity: null
  name: CLOUDe
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/bioadv/vbae002
    metadata:
      abstract: 'Motivation: Gene deletion is traditionally thought of as a nonadaptive
        process that removes functional redundancy from genomes, such that it generally
        receives less attention than duplication in evolutionary turnover studies.
        Yet, mounting evidence suggests that deletion may promote adaptation via the
        “less-is-more” evolutionary hypothesis, as it often targets genes harboring
        unique sequences, expression profiles, and molecular functions. Hence, predicting
        the relative prevalence of redundant and unique functions among genes targeted
        by deletion, as well as the parameters underlying their evolution, can shed
        light on the role of gene deletion in adaptation. Results: Here, we present
        CLOUDe, a suite of machine learning methods for predicting evolutionary targets
        of gene deletion events from expression data. Specifically, CLOUDe models
        expression evolution as an Ornstein–Uhlenbeck process, and uses multi-layer
        neural network, extreme gradient boosting, random forest, and support vector
        machine architectures to predict whether deleted genes are “redundant” or
        “unique”, as well as several parameters underlying their evolution. We show
        that CLOUDe boasts high power and accuracy in differentiating between classes,
        and high accuracy and precision in estimating evolutionary parameters, with
        optimal performance achieved by its neural network architecture. Application
        of CLOUDe to empirical data from Drosophila suggests that deletion primarily
        targets genes with unique functions, with further analysis showing these functions
        to be enriched for protein deubiquitination. Thus, CLOUDe represents a key
        advance in learning about the role of gene deletion in functional evolution
        and adaptation.'
      authors:
      - name: dos Santos A.L.C.
      - name: DeGiorgio M.
      - name: Assis R.
      citationCount: 0
      date: '2024-01-01T00:00:00Z'
      journal: Bioinformatics Advances
      title: Predicting evolutionary targets and parameters of gene deletion from
        expression data
    note: null
    pmcid: PMC10812876
    pmid: '38282974'
    type: []
    version: null
  relation: []
  toolType:
  - Workflow
  topic:
  - term: Evolutionary biology
    uri: http://edamontology.org/topic_3299
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Molecular interactions, pathways and networks
    uri: http://edamontology.org/topic_0602
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T14:51:38.985638Z'
  biotoolsCURIE: biotools:evoranker
  biotoolsID: evoranker
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: yuval.tabach@mail.huji.ac.il
    fundrefid: null
    gridid: null
    name: Yuval Tabach
    note: null
    orcidid: https://orcid.org/0000-0001-9521-3217
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Christina Canavati
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: EvORanker is an interactive web tool for prioritizing candidate genes
    using coevolution and STRING.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant prioritisation
      uri: http://edamontology.org/operation_3226
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    output: []
  homepage: https://ccanavati.shinyapps.io/EvORanker/
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-18T14:51:38.988011Z'
  license: null
  link: []
  maturity: null
  name: EvORanker
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/s13073-023-01276-2
    metadata:
      abstract: 'Background: Next-generation sequencing (NGS) has significantly transformed
        the landscape of identifying disease-causing genes associated with genetic
        disorders. However, a substantial portion of sequenced patients remains undiagnosed.
        This may be attributed not only to the challenges posed by harder-to-detect
        variants, such as non-coding and structural variations but also to the existence
        of variants in genes not previously associated with the patient’s clinical
        phenotype. This study introduces EvORanker, an algorithm that integrates unbiased
        data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes.
        Methods: EvORanker utilizes clinical data, multi-scale phylogenetic profiling,
        and other omics data to prioritize disease-associated genes. It was evaluated
        on solved exomes and simulated genomes, compared with existing methods, and
        applied to 6260 knockout genes with mouse phenotypes lacking human associations.
        Additionally, EvORanker was made accessible as a user-friendly web tool. Results:
        In the analyzed exomic cohort, EvORanker accurately identified the “true”
        disease gene as the top candidate in 69% of cases and within the top 5 candidates
        in 95% of cases, consistent with results from the simulated dataset. Notably,
        EvORanker outperformed existing methods, particularly for poorly annotated
        genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker
        linked 41% of these genes to observed human disease phenotypes. Furthermore,
        in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3
        as disease candidates for previously uncharacterized genetic syndromes. Conclusions:
        We highlight clade-based phylogenetic profiling as a powerful systematic approach
        for prioritizing potential disease genes. Our study showcases the efficacy
        of EvORanker in associating poorly annotated genes to disease phenotypes observed
        in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/
        .'
      authors:
      - name: Canavati C.
      - name: Sherill-Rofe D.
      - name: Kamal L.
      - name: Bloch I.
      - name: Zahdeh F.
      - name: Sharon E.
      - name: Terespolsky B.
      - name: Allan I.A.
      - name: Rabie G.
      - name: Kawas M.
      - name: Kassem H.
      - name: Avraham K.B.
      - name: Renbaum P.
      - name: Levy-Lahad E.
      - name: Kanaan M.
      - name: Tabach Y.
      citationCount: 0
      date: '2024-12-01T00:00:00Z'
      journal: Genome Medicine
      title: Using multi-scale genomics to associate poorly annotated genes with rare
        diseases
    note: null
    pmcid: PMC10765705
    pmid: '38178268'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Exome sequencing
    uri: http://edamontology.org/topic_3676
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: Phylogenetics
    uri: http://edamontology.org/topic_3293
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T14:49:23.007488Z'
  biotoolsCURIE: biotools:diagui
  biotoolsID: diagui
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: marc-antoine.gerault@inserm.fr
    fundrefid: null
    gridid: null
    name: Marc-Antoine Gerault
    note: null
    orcidid: https://orcid.org/0009-0000-7430-3467
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Luc Camoin
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Samuel Granjeaud
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'Shiny application and R package to process the output from DIA-NN.

    It returns the quantification of either the precursors, the peptides, the proteins,
    or the genes thanks to the MaxLFQ algorithm. In addition, the latest version provides
    the Top3 and iBAQ quantification and the number of peptides used for the quantification.
    In the end, DIAgui produces ready-to-interpret files from the results of DIA mass
    spectrometry analysis and provides visualization and statistical tools that can
    be used in a user-friendly way.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Data handling
      uri: http://edamontology.org/operation_2409
    output: []
  homepage: https://github.com/marseille-proteomique/DIAgui
  homepage_status: 0
  language:
  - R
  - C++
  lastUpdate: '2024-05-18T14:49:23.009468Z'
  license: null
  link: []
  maturity: null
  name: DIAgui
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/bioadv/vbae001
    metadata:
      abstract: DIAgui is an R package to simplify the processing of the report file
        from the DIA-NN software thanks to a Shiny application. It returns the quantification
        of either the precursors, the peptides, the proteins, or the genes thanks
        to the MaxLFQ algorithm. In addition, the latest version provides the Top3
        and iBAQ quantification and the number of peptides used for the quantification.
        In the end, DIAgui produces ready-to-interpret files from the results of DIA
        mass spectrometry analysis and provides visualization and statistical tools
        that can be used in a user-friendly way.
      authors:
      - name: Gerault M.-A.
      - name: Camoin L.
      - name: Granjeaud S.
      citationCount: 0
      date: '2024-01-01T00:00:00Z'
      journal: Bioinformatics Advances
      title: 'DIAgui: a Shiny application to process the output from DIA-NN'
    note: null
    pmcid: PMC10799745
    pmid: '38249340'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  - Library
  topic:
  - term: Proteomics experiment
    uri: http://edamontology.org/topic_3520
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Proteomics
    uri: http://edamontology.org/topic_0121
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T14:47:16.635684Z'
  biotoolsCURIE: biotools:circnetvis
  biotoolsID: circnetvis
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: TrungNghia.Vu@ki.se
    fundrefid: null
    gridid: null
    name: Trung Nghia Vu
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Thi-Hau Nguyen
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Interactive web application for visualizing interaction networks of
    circular RNAs.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Network visualisation
      uri: http://edamontology.org/operation_3925
    - term: Protein interaction network prediction
      uri: http://edamontology.org/operation_3094
    - term: Protein interaction network analysis
      uri: http://edamontology.org/operation_0276
    - term: Formatting
      uri: http://edamontology.org/operation_0335
    output: []
  homepage: https://www.meb.ki.se/shiny/truvu/CircNetVis/
  homepage_status: 0
  language:
  - R
  lastUpdate: '2024-05-18T14:47:16.638302Z'
  license: null
  link: []
  maturity: null
  name: CircNetVis
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/s12859-024-05646-4
    metadata:
      abstract: Analyzing the interactions of circular RNAs (circRNAs) is a crucial
        step in understanding their functional impacts. While there are numerous visualization
        tools available for investigating circRNA interaction networks, these tools
        are typically limited to known circRNAs from specific databases. Moreover,
        these existing tools usually require complex installation procedures which
        can be time-consuming and challenging for users. There is a lack of a user-friendly
        web application that facilitates interactive exploration and visualization
        of circRNA interaction networks. CircNetVis is an interactive online web application
        to enhance the analysis of human/mouse circRNA interactions. The tool allows
        three different input formats of circRNAs including circRNA IDs from CircBase,
        circRNA coordinates (chromosome, start position, end position), and circRNA
        sequences in the FASTA format. It integrates multiple interaction networks
        for visualization and investigation of the interplay between circRNA, microRNAs,
        mRNAs and RNA binding proteins. CircNetVis also enables users to interactively
        explore the interactions of unknown circRNAs which are not reported from previous
        databases. The tool can generate interactive plots and allows users to save
        results as output files for offline usage. CircNetVis is implemented as a
        web application using R-shiny and freely available for academic use at https://www.meb.ki.se/shiny/truvu/CircNetVis/
        .
      authors:
      - name: Nguyen T.-H.
      - name: Nguyen H.-N.
      - name: Vu T.N.
      citationCount: 0
      date: '2024-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'CircNetVis: an interactive web application for visualizing interaction
        networks of circular RNAs'
    note: null
    pmcid: PMC10795305
    pmid: '38233808'
    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: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: RNA immunoprecipitation
    uri: http://edamontology.org/topic_3794
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T14:44:32.513056Z'
  biotoolsCURIE: biotools:serotoninai
  biotoolsID: serotoninai
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: j.szlek@uj.edu.pl
    fundrefid: null
    gridid: null
    name: Jakub Szlęk
    note: null
    orcidid: https://orcid.org/0000-0003-1537-9650
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Natalia Łapińska
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'Serotonergic System Focused, Artificial Intelligence-Based Application
    for Drug Discovery.


    SerotoninAI is a web application for scientific purposes focused on the serotonergic
    system. By leveraging SerotoninAI, researchers can assess the affinity (pKi value)
    of a molecule to all main serotonin receptors and serotonin transporters based
    on molecule structure introduced as SMILES.'
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Virtual screening
      uri: http://edamontology.org/operation_3938
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    output: []
  homepage: https://serotoninai.streamlit.app/
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-18T14:44:32.515105Z'
  license: null
  link:
  - note: null
    type:
    - Other
    url: https://www.youtube.com/watch?v=MGOwzvvPtxk
  maturity: null
  name: SerotoninAI
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1021/acs.jcim.3c01517
    metadata:
      abstract: SerotoninAI is an innovative web application for scientific purposes
        focused on the serotonergic system. By leveraging SerotoninAI, researchers
        can assess the affinity (pKi value) of a molecule to all main serotonin receptors
        and serotonin transporters based on molecule structure introduced as SMILES.
        Additionally, the application provides essential insights into critical attributes
        of potential drugs such as blood-brain barrier penetration and human intestinal
        absorption. The complexity of the serotonergic system demands advanced tools
        for accurate predictions, which is a fundamental requirement in drug development.
        SerotoninAI addresses this need by providing an intuitive user interface that
        generates predictions of pKi values for the main serotonergic targets. The
        application is freely available on the Internet at https://serotoninai.streamlit.app/,
        implemented in Streamlit with all major web browsers supported. Currently,
        to the best of our knowledge, there is no tool that allows users to access
        affinity predictions for serotonergic targets without registration or financial
        obligations. SerotoninAI significantly increases the scope of drug development
        activities worldwide. The source code of the application is available at https://github.com/nczub/SerotoninAI_streamlit.
      authors:
      - name: Lapinska N.
      - name: Paclawski A.
      - name: Szlek J.
      - name: Mendyk A.
      citationCount: 2
      date: '2024-04-08T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: 'SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based
        Application for Drug Discovery'
    note: null
    pmcid: PMC11005036
    pmid: '38289046'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Drug discovery
    uri: http://edamontology.org/topic_3336
  - term: Drug metabolism
    uri: http://edamontology.org/topic_3375
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Drug development
    uri: http://edamontology.org/topic_3373
  - term: Pharmacology
    uri: http://edamontology.org/topic_0202
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-05-18T14:29:08.555278Z'
  biotoolsCURIE: biotools:hiphase
  biotoolsID: hiphase
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: mholt@pacificbiosciences.com
    fundrefid: null
    gridid: null
    name: James M Holt
    note: null
    orcidid: https://orcid.org/0000-0001-6411-9236
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Michael Eberle
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Jointly phasing small, structural, and tandem repeat variants from
    HiFi sequencing.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Phasing
      uri: http://edamontology.org/operation_3454
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    - term: Molecular replacement
      uri: http://edamontology.org/operation_3455
    output: []
  homepage: https://github.com/PacificBiosciences/HiPhase
  homepage_status: 0
  language: []
  lastUpdate: '2024-05-18T14:29:08.557627Z'
  license: null
  link: []
  maturity: null
  name: HiPhase
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/bioinformatics/btae042
    metadata:
      abstract: 'Motivation: In diploid organisms, phasing is the problem of assigning
        the alleles at heterozygous variants to one of two haplotypes. Reads from
        PacBio HiFi sequencing provide long, accurate observations that can be used
        as the basis for both calling and phasing variants. HiFi reads also excel
        at calling larger classes of variation, such as structural or tandem repeat
        variants. However, current phasing tools typically only phase small variants,
        leaving larger variants unphased. Results: We developed HiPhase, a tool that
        jointly phases SNVs, indels, structural, and tandem repeat variants. The main
        benefits of HiPhase are (i) dual mode allele assignment for detecting large
        variants, (ii) a novel application of the A*-algorithm to phasing, and (iii)
        logic allowing phase blocks to span breaks caused by alignment issues around
        reference gaps and homozygous deletions. In our assessment, HiPhase produced
        an average phase block NG50 of 480 kb with 929 switchflip errors and fully
        phased 93.8% of genes, improving over the current state of the art. Additionally,
        HiPhase jointly phases SNVs, indels, structural, and tandem repeat variants
        and includes innate multi-threading, statistics gathering, and concurrent
        phased alignment output generation. Availability and implementation: HiPhase
        is available as source code and a pre-compiled Linux binary with a user guide
        at https://github.com/ PacificBiosciences/HiPhase.'
      authors:
      - name: Holt
      - name: Saunders
      - name: Rowell
      - name: Kronenberg Z.
      - name: Wenger
      - name: Eberle M.
      citationCount: 0
      date: '2024-02-01T00:00:00Z'
      journal: Bioinformatics
      title: 'HiPhase: jointly phasing small, structural, and tandem repeat variants
        from HiFi sequencing'
    note: null
    pmcid: PMC10868326
    pmid: '38269623'
    type: []
    version: null
  relation: []
  toolType:
  - Library
  topic:
  - term: Structural variation
    uri: http://edamontology.org/topic_3175
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  validated: 0
  version: []
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