Executable Network Models of Integrated Multiomics Data

Multiomics profiling
provides a holistic picture of a condition
being examined and captures the complexity of signaling events, beginning
from the original cause (environmental or genetic), to downstream
functional changes at multiple molecular layers. Pathway enrichment
analysis has been used with multiomics data sets to characterize signaling
mechanisms. However, technical and biological variability between
these layered data limit an integrative computational analyses. We
present a Boolean network-based method, multiomics Boolean Omics Network
Invariant-Time Analysis (mBONITA), to integrate
omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform
topology-based pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular
measurements by combining observed fold-changes and variance, with
a measure of node (i.e., gene or protein) influence over signaling,
and a measure of the strength of evidence for that gene across data
sets. We used mBONITA to integrate multiomics
data sets from RAMOS B cells treated with the immunosuppressant drug
cyclosporine A under varying O2 tensions to identify pathways
involved in hypoxia-mediated chemotaxis. We compare mBONITA’s performance with 6 other pathway analysis methods designed
for multiomics data and show that mBONITA identifies
a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.