Prof. Benjamin Berman

Research

Reconstructing altered transcription factor networks from chromatin changes at cis-regulatory elements

Our lab is interested in the reconstruction of cancer gene regulatory networks from DNA methylation and other chromatin changes that occur at transcription factor binding sites (TFBSs). For instance, we collaborated with Peter Jones’ lab to develop the multi-omic NOMe-seq technique for determining the differing roles of DNA methylation and chromatin accessibility at TFBSs (Kelly et al., Genome Res. 2010), and developed a computational approach Bis-SNP to deconvolute methylation and SNP information from bisulfite sequencing to identify allele-specific TFBSs (Liu et al., Genome Biol. 2012). We developed a Bioconductor R package, ELMER, that integrates DNA methylation information at TFBSs with TF expression levels to reconstruct gene regulatory networks (Yao et al., Genome Biol. 2015; Silva et al., Bioinformatics 2018).

Heterochromatin-associated methylation loss in aging and cancer

We collaborated with Peter Jones’ lab to use DNA Methyltransferase (DNMT) knockouts in colon cancer cells to study the effects of global methylation loss, showing dramatic changes in the epigenomic architecture of a large number of non-coding gene regulatory regions and expression of nearby genes (Lay et al., Genome Res. 2015). Turning to in vivo data, we recently compiled methylation data from ~15,000 publicly available tissue samples, and developed a new computational model to discover that Heterochromatin-Associated Methylation Loss begins early in life and behaves as a mitotic clock (Zhou et al., Nat. Genetics 2018).

Understanding cancer evolution through epigenetic intra-tumour heterogeneity

We collaborated with Phil Koeffler’s lab to investigate the genetic and epigenetic heterogeneity of individual cancer cases by sequencing multiple geographic sites from the same tumour. In oesophageal cancer, we found that DNA methylation changes followed a similar evolutionary path as the genome (Hao et al., Nat. Genetics 2016), while we found significant divergence in liver cancer (Lin et al., Cancer Res.2017).