ArticleCell communication and signaling : CCS2020
PINOT: an intuitive resource for integrating protein-protein interactions.
Article in Cell communication and signaling : CCS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- PAK6 rescues pathogenic LRRK2-mediated ciliogenesis and centrosomal cohesion defects in a mutation-specific manner.Cell death & disease · 2024Article
- QSOX2 Deficiency-induced short stature, gastrointestinal dysmotility and immune dysfunction.Nature communications · 2024Article
- Transcriptomics and weighted protein network analyses of the LRRK2 protein interactome reveal distinct molecular signatures for sporadic and LRRK2 Parkinson's Disease.NPJ Parkinson's disease · 2024Article
- Human mutations inFrontiers in molecular neuroscience · 2024Article
- Molecular Genetic Characteristics ofGenes · 2023Article
- Tissue specific LRRK2 interactomes reveal a distinct striatal functional unit.PLoS computational biology · 2023Article
- Developing a bioinformatics pipeline for comparative protein classification analysis.BMC genomic data · 2022Article
- Modelling the functional genomics of Parkinson's disease in Caenorhabditis elegans: LRRK2 and beyond.Bioscience reports · 2021Review
- Genomewide Association Studies of LRRK2 Modifiers of Parkinson's Disease.Annals of neurology · 2021Article
- Article
- Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias.iScience · 2021Article
- Preclinical modeling of chronic inhibition of the Parkinson's disease associated kinase LRRK2 reveals altered function of the endolysosomal system in vivo.Molecular neurodegeneration · 2021Article
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8 authors.
Funding
Abstract
backgroundThe past decade has seen the rise of omics data for the understanding of biological systems in health and disease. This wealth of information includes protein-protein interaction (PPI) data derived from both low- and high-throughput assays, which are curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming.
methodsWe here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from IMEx consortium associated repositories (members and observers) and WormBase, for constructing, respectively, human and Caenorhabditis elegans PPI networks.
resultsUsers submit a query containing a list of proteins of interest for which PINOT extracts data describing PPIs. At every query submission PPI data are downloaded, merged and quality assessed. Then each PPI is confidence scored based on the number of distinct methods used for interaction detection and the number of publications that report the specific interaction. Examples of how PINOT can be applied are provided to highlight the performance, ease of use and potential utility of this tool.
conclusionsPINOT is a tool that allows users to survey the curated literature, extracting PPI data in relation to a list of proteins of interest. PINOT extracts a similar numbers of PPIs as other, analogous, tools and incorporates a set of innovative features. PINOT is able to process large queries, it downloads human PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI data (i.e. removing the need for manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each downloaded interaction data entry and outputs the results in a table format that can be straightforwardly further customised and/or directly uploaded into network visualization software. Video abstract.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.