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This Week in Nucleic Acids Research: May 11, 2016

A Penn State University team tracks the dynamics of more than a dozen epigenetic marks across several human cell types profiled for the ENCODE project. The researchers tallied the epigenetic patterns and regulatory regions with the help of the "integrative and discriminative epigenome annotation system" (IDEAS) algorithm, an approach aimed at finding epigenetic features across a given genome and between cell types. "A key distinction between our method and existing state-of-the-art algorithms is that IDEAS integrates epigenomes of many cell types simultaneously in a way that preserves the position-dependent and cell type-specific information at fine scales," the team explains, "thereby greatly improving segmentation accuracy and producing comparable annotations across cell types."

Icahn Institute for Genomics and Multiscale Biology researchers describe an approach for uncovering cancer drivers from transcriptional data with the help of molecular signature cues. The investigators started by combing through variant data for genes involved in seven cancer-related pathways in breast, ovarian, and colon cancer samples assessed for the Cancer Genome Atlas. They searched for activity of these pathways in available RNA sequence data, uncovering signatures that were subsequently verified in additional testing and validation datasets. "Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level," the authors write, "and that the transcriptional signatures can generally provide an alternative to DNA sequencing method in detecting specific driver pathways."

A team from the Karolinska Institute and other centers in Sweden and Estonia present an approach called TAB-Methyl-SEQ that combines Tet1-assisted bisulfite sequencing with Agilent Methyl-SEQ targeted enrichment to assess 5-hydroxymethylcytosine (hmC) and 5-methylcytosine patterns at single base resolution. Using the TAB-Methyl-SEQ method, the researchers profiled 188 human genes from 20 adult human liver samples, considering hmC patterns that varied by sample, the gene considered, and/or site in the genome. "Our data suggest that both the gene- and site-specific components of hmC variability might contribute to the epigenetic control of hepatic genes," they say, noting that "[t]'he protocol described here should be useful for targeted DNA analysis in a variety of applications."