Integrative epigenomic analyses of small cell lung cancer cells demonstrates the clinical translational relevance of gene body methylation.

DNA methylation is a key regulator of gene expression and a clinical therapeutic predictor. We examined global DNA methylation beyond the generally used promoter areas in human small cell lung cancer (SCLC) and find that gene body methylation is a robust positive predictor of gene expression. Combining promoter and gene body methylation better predicts gene expression than promoter methylation alone including genes involved in the neuroendocrine classification of SCLC and the expression of therapeutically relevant genes including , and .

Convergence of SIRT1 and ATR signaling to modulate replication origin dormancy.

During routine genome duplication, many potential replication origins remain inactive or 'dormant'. Such origin dormancy is achieved, in part, by an interaction with the metabolic sensor SIRT1 deacetylase. We report here that dormant origins are a group of consistent, pre-determined genomic sequences that are distinguished from baseline (i.

Dynamics of replication origin over-activation.

Safeguards against excess DNA replication are often dysregulated in cancer, and driving cancer cells towards over-replication is a promising therapeutic strategy. We determined DNA synthesis patterns in cancer cells undergoing partial genome re-replication due to perturbed regulatory interactions (re-replicating cells). These cells exhibited slow replication, increased frequency of replication initiation events, and a skewed initiation pattern that preferentially reactivated early-replicating origins.

BAMscale: quantification of next-generation sequencing peaks and generation of scaled coverage tracks.

Background: Next-generation sequencing allows genome-wide analysis of changes in chromatin states and gene expression. Data analysis of these increasingly used methods either requires multiple analysis steps, or extensive computational time. We sought to develop a tool for rapid quantification of sequencing peaks from diverse experimental sources and an efficient method to produce coverage tracks for accurate visualization that can be intuitively displayed and interpreted by experimentalists with minimal bioinformatics background.