Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework

Advanced computational methods exploit gene expression and epigenetic datasets to predict gene regulatory networks controlled by transcription factors (TFs). These methods have identified cell fate determining TFs but require large amounts of reference data and experimental expertise. Here, we present an easy to use network-based computational framework that exploits enhancers defined by bidirectional transcription, using as sole input CAGE sequencing data to correctly predict TFs key to various human cell types. Next, we applied this Analysis Algorithm for Networks Specified by Enhancers based on CAGE (ANANSE-CAGE) to predict TFs driving red and white blood cell development, and THP-1 leukemia cell immortalization. Further, we predicted TFs that are differentially important to either cell line- or primary- associated MLL-AF9-driven gene programs, and in primary MLL-AF9 acute leukemia. Our approach identified experimentally validated as well as thus far unexplored TFs in these processes. ANANSE-CAGE will be useful to identify transcription factors that are key to any cell fate change using only CAGE-seq data as input.