PathExt: a general framework for path-based mining of omics-integrated biological networks

Abstract

Motivation Transcriptomes are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs. Results We provide a computational tool - PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (a) Mycobacterium tuberculosis (M.tb) response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (b) tissue-relevant genes and processes using transcriptomic data for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls. Availability The source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt. Supplementary information Supplementary data are available at Bioinformatics online.

A new blood-based RNA signature (R9), for monitoring effectiveness of tuberculosis treatment in a South Indian longitudinal cohort

“…have earlier shown that a similar network approach is capable of identifying condition-specific perturbations that are biologically relevant and thus useful for biomarker discovery ( Sambarey et al., 2017b ; Metri et al., 2017 ; Ravichandran et al., 2021 ). Briefly, our method uses a knowledge-based comprehensive human protein–protein interaction network (hPPiN) previously constructed by us ( Table S2 A), renders it specific to each given condition by integrating gene expression data into it, and sensitively mines most perturbed subnetworks and their most influential epicentric nodes ( Sambaturu et al., 2016 , 2021 ; Ravichandran and Chandra, 2019 ). The network analysis carried out for the discovery datasets for TB 0 (week 0) samples versus TR (month 6) yielded subnetworks of size 2,457 nodes, 4,459 edges for GSE89403 -Thompson, and 2,710 nodes, 4,649 edges for GSE122485 -Sambarey, and shared 1,454 common genes between them ( Table S2 B).…”

A path-based analysis of infected cell line and COVID-19 patient transcriptome reveals novel potential targets and drugs against SARS-CoV-2

“…While previous works have exploited protein networks to infer transcriptomic perturbations, they have still relied on significantly differentially expressed genes and interpreted them in the context of the network ( 101 , 102 ). We have previously demonstrated ( 12 ), superiority of PathExt over such integrative approaches that nevertheless rely on significant differential expression.…”

“…These approaches nevertheless rely on gene-level differential expression as the lynchpin for the downstream network-assisted analyses. An alternative approach - PathExt - that we have recently shown to be superior to DEG-centric approaches ( 12 ), instead integrates transcriptomic data with curated gene networks and instead of identifying differentially expressed genes, identifies differentially active paths in the integrated network, and then identifies the central genes mediating the differential activities of the most perturbed paths. This alternative approach is based on the recognition that (i) gene expression is noisy and DEGs can therefore lead to false positives, and (ii) key regulatory genes that mediate global transcriptomic changes and thus present a potent target may themselves not be differentially regulated and will thus be missed by DEG-centric approaches.…”

Knock-down of a regulatory barcode shifts macrophage polarization destination from M1 to M2 and increases pathogen burden uponS. aureusinfection

“…3. This method of computing response networks which involves a knowledge-based network and a sensitive interrogation algorithm has been shown to outperform data-driven network inference methods in capturing biologically relevant processes and genes (Ravichandran and Chandra, 2019;Sambaturu et al, 2021). Briefly, the network mining algorithm works by computing minimum weight shortest paths, in which each path begins from a source node and ends with a sink node, identifying connected sets of edges that make up the least-cost paths.…”