The rapid development of genomics technologies has resulted in a flood of big-data resources in cancer research (Jiang et al., Nature Genetics 2015). Comprehensive data integration could enable unbiased identification of new regulators in tumorigenesis and cancer drug resistance. I developed a computational method RABIT (Regression Analysis with Background Integration) to identify transcription factors (TF) that drive tumor-specific gene expression patterns through integrating many types of genomics data (Jiang et al., PNAS 2015). The predicted TF regulatory activity in cancer is highly consistent with previous knowledge and reveals many new TFs associated with tumor progression. Recently, the ENCODE3 project utilized RABIT to integrate ENCODE ChIP-Seq and CLIP data with TCGA cancer genomics data, and identified SUB1 as a novel RNA binding protein in promoting tumor progression in many cancer types (Zhang et al., In review. Jiang as co-first author).

• Graphical Abstract of RABIT framework (Link to RABIT website. )