RT Journal
A1 Siahpirani, Alireza Fotuhi
A1 Knaack, Sara
A1 Chasman, Deborah
A1 Seirup, Morten
A1 Sridharan, Rupa
A1 Stewart, Ron
A1 Thomson, James
A1 Roy, Sushmita
T1 Dynamic regulatory module networks for inference of cell type–specific transcriptional networks
JF Genome Research 
JO Genome Research 
YR 2022 
FD July 01 
VO 32 
IS 7 
SP 1367 
OP 1384 
DO 10.1101/gr.276542.121 
UL http://genome.cshlp.org/content/32/7/1367.abstract 
AB Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type–specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type–specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type–specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.