PINOT: an intuitive resource for integrating protein-protein interactions

Background The 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. Methods We 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. Results Users 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. Conclusions PINOT 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

[1]  Karin Breuer,et al.  InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation , 2012, Nucleic Acids Res..

[2]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[3]  James E. Tomkins,et al.  Comparative Protein Interaction Network Analysis Identifies Shared and Distinct Functions for the Human ROCO Proteins , 2018, Proteomics.

[4]  Anushya Muruganujan,et al.  Alliance of Genome Resources Portal: unified model organism research platform , 2019, Nucleic Acids Res..

[5]  M. Chartier-Harlin,et al.  In silico and Wet Bench Interactomics Sheds Light on the Similitudes and Differences between Human ROCO Proteins , 2018, Proteomics.

[6]  Johannes Goll,et al.  Protein interaction data curation: the International Molecular Exchange (IMEx) consortium , 2012, Nature Methods.

[7]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[8]  Rafael C. Jimenez,et al.  The IntAct molecular interaction database in 2012 , 2011, Nucleic Acids Res..

[9]  J. Hardy,et al.  Weighted Protein Interaction Network Analysis of Frontotemporal Dementia , 2016, Journal of proteome research.

[10]  Kimberly Van Auken,et al.  WormBase: a modern Model Organism Information Resource , 2019, Nucleic Acids Res..

[11]  Damian Szklarczyk,et al.  The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible , 2016, Nucleic Acids Res..

[12]  Kimberly Van Auken,et al.  WormBase 2017: molting into a new stage , 2017, Nucleic Acids Res..

[13]  María Martín,et al.  UniProt: A hub for protein information , 2015 .

[14]  Kara Dolinski,et al.  The BioGRID interaction database: 2015 update , 2014, Nucleic Acids Res..

[15]  Yanhui Hu,et al.  Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data , 2017, Nucleic Acids Res..

[16]  John Hardy,et al.  Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences , 2016, Briefings Bioinform..

[17]  R. Desikan,et al.  Protein network analysis reveals selectively vulnerable regions and biological processes in FTD , 2018, Neurology: Genetics.

[18]  Martin H. Schaefer,et al.  HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality Scores , 2012, PloS one.

[19]  G. Tagliabue,et al.  Tau Mutations Serve as a Novel Risk Factor for Cancer. , 2018, Cancer research.

[20]  Ewout J. N. Groen,et al.  Comparative interactomics analysis of different ALS-associated proteins identifies converging molecular pathways , 2016, Acta Neuropathologica.

[21]  Karl R. Clauser,et al.  MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins , 2015, Nucleic Acids Res..

[22]  Livia Perfetto,et al.  MINT, the molecular interaction database: 2012 update , 2011, Nucleic Acids Res..

[23]  Gary D Bader,et al.  PSICQUIC and PSISCORE: accessing and scoring molecular interactions , 2011, Nature Methods.

[24]  Hawoong Jeong,et al.  A protein interaction network associated with asthma. , 2008, Journal of theoretical biology.

[25]  Martin H. Schaefer,et al.  HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks , 2016, Nucleic Acids Res..

[26]  James C. Hu,et al.  The Gene Ontology Resource: 20 years and still GOing strong , 2019 .

[27]  J. Vilo,et al.  g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update) , 2019, Nucleic Acids Res..

[28]  Marco Y. Hein,et al.  A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances , 2015, Cell.

[29]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[30]  L. Bonetta.  Protein–protein interactions: Interactome under construction , 2010, Nature.

[31]  Demis A. Kia,et al.  Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis , 2018, BMC genomics.

[32]  The Uniprot Consortium,et al.  UniProt: a hub for protein information , 2014, Nucleic Acids Res..

[33]  Paul Denny,et al.  Genenames.org: the HGNC and VGNC resources in 2019 , 2018, Nucleic Acids Res..

[34]  Devin K. Schweppe,et al.  Architecture of the human interactome defines protein communities and disease networks , 2017, Nature.