Hotspots of aberrant enhancer activity in fibrolamellar carcinoma reveal molecular mechanisms of oncogenesis and intrinsic drug resistance

Timothy A. Dinh; Ramja Sritharan; F. Donelson Smith; Adam B. Francisco; Rosanna K. Ma; Rodica P. Bunaciu; Matt Kanke; Charles G. Danko; Andrew P. Massa; John D. Scott; Praveen Sethupathy

To identify TREs across all samples, bigwig files of the same strand from all samples (FLC and NML) were merged. This merged dataset was used to call TREs. TREs from all samples were identified with dREG (Danko et al., 2015; Wang et al., 2019) using the peak calling algorithm. Read counts were quantified within each TRE locus using the R package bigwig (https://github.com/andrelmartins/bigWig). Total read counts on the sense and antisense strands within each TRE across all samples were then imported into DESeq2 1.22.2 (Love et al., 2014) . Analysis of TRE counts from ChRO-seq revealed they followed a negative binomial distribution similar to RNA-seq counts. Therefore, differential transcription analysis of TREs was performed with DESeq2 to identify TREs that were significantly differentially transcribed in FLC or NML.