pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis

Abstract

Molecular pathway data are essential in current computational and systems biology research. While there are many primary and integrated pathway databases, several challenges remain, including low proteome coverage (57%), low overlap across different databases, unavailability of direct information about underlying physical connectivity of pathway members, and high fraction of protein-coding genes without any pathway annotations, i.e. ‘pathway orphans’. In order to address all these challenges, we developed pathDIP, which integrates data from 20 source pathway databases, ‘core pathways’, with physical protein–protein interactions to predict biologically relevant protein–pathway associations, referred to as ‘extended pathways’. Cross-validation determined 71% recovery rate of our predictions. Data integration and predictions increase coverage of pathway annotations for protein-coding genes to 86%, and provide novel annotations for 5732 pathway orphans. PathDIP (http://ophid.utoronto.ca/pathdip) annotates 17 070 protein-coding genes with 4678 pathways, and provides multiple query, analysis and output options.

miR-194 as predictive biomarker of responsiveness to neoadjuvant chemoradiotherapy in patients with locally advanced rectal adenocarcinoma

“…We then identified protein–protein interactions (PPIs) among miR-194 targets using IID database V.2016–03 (http://ophid.utoronto.ca/iid), creating a PPI network that we visualised and analysed in NAViGaTOR 3.0 (http://ophid.utoronto.ca/navigator). We then performed a functional annotation and comprehensive pathway enrichment analysis of all the proteins of the network using pathDIP V.2.5 pathway database17 (http://ophid.utoronto.ca/pathDIP), using the following settings: extended pathway associations; experimentally detected PPIs; minimum confidence level for predicted protein–pathway associations: 0.99. We considered significantly enriched only pathways with q-value (false discovery rate (FDR): Benjamini-Hochberh (BH) method) lower than 0.01.…”

Circulating adiposity‐related microRNAs as predictors of the response to a low‐fat diet in subjects with obesity

“…Statistical significances were reported after the Benjamini‐Hochberg (q‐value) correction for multiple comparisons. To better understand the biological relevance of the miRNAs target genes, a network analysis was executed using PathDIP (accessed 30th May 2019) . The Jaccard similarity coefficient (JC) was implemented to elucidate functional similarities between dysregulated miRNAs.…”

“…To better understand the biological relevance of the miR-NAs target genes, a network analysis was executed using PathDIP (accessed 30th May 2019). 32 The Jaccard similarity coefficient (JC) was implemented to elucidate functional similarities between dysregulated miRNAs. This criterion was adopted to investigate the similarity of miRNAs in a pairwise manner, both in terms of their target genes and enriched pathways.…”

Essential gene networks in acute myeloid leukemia identified using a microRNA-knockout CRISPR library screen

“…With advanced computational tools such that integrate all major known scoring algorithms, downstream targets may also be elucidated. Within this analysis, we demonstrated that by examining top miRNA hits from three different AML lines, we can use eigenvector centrality and hub construction to predict common downstream targets and in turn, common downstream pathways associated with AML survival [55][56][57].…”