TY  - JOUR
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
Y1  - 2022/07/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1367 
EP  - 1384 
DO  - 10.1101/gr.276542.121 
VL  - 32 
IS  - 7 
UR  - http://genome.cshlp.org/content/32/7/1367.abstract 
N2  - 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. 
ER  -