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Background  Transcriptional regulation is complex, requiring multiple  cis  (local) and  trans  acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from  cis  regulatory factors. However, local chromatin looping and  trans- acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both  cis  and  trans  gene regulatory features.    Results  We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both  cis  and  trans  acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing  cis -regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight  cis -regulatory rare variants for sequence kernel association test analyses of gene expression.    Conclusions  Our models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.    Supplementary Information  The online version contains supplementary material available at 10.1186/s12859-021-04126-3.

Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources