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This Week in Nucleic Acids Research: Apr 12, 2017

Massachusetts Institute of Technology researchers present a deep convolutional neural network model designed to discern the DNA methylation consequences of non-coding DNA variants based on learned regulatory codes and neighboring sequence features. The approach, dubbed CpGenie, "predicts the impact of sequence variants on DNA methylation with an accuracy that surpasses existing methods for functional variants prioritization," the study's authors say. Along with single-nucleotide, allele-specific DNA methylation predictions, the team notes that CpGenie can be used to scrutinize variants uncovered by genome-wide association studies, while predicting potential effects associated with methylation quantitative trait loci.

A German team introduces a computational pipeline called PosiGene that aims to unearth positively selected genes based on genome-wide datasets. The researchers validated their software, which includes ortholog assignment catalog, alignment and phylogeny, and positive selection filtering modules, using simulated datasets and real human data from a handful of prior studies. "The results demonstrated that PosiGene reaches a good overlap with existing high-ranking studies on the human lineage," the authors say, noting that "more than two-thirds of the [positively selected genes] that were identified by PosiGene were also found by at least one human study."

German and American researchers explore transcriptomic features of the commensal pathogen Neisseria meningitidis, which is found in the nasopharyngeal tract of nearly one-third of healthy individuals, but can cause dangerous cases of acute bacterial meningitis and/or sepsis. With the help of differential RNA sequencing, RNA co-immunoprecipitation targeting an RNA chaperone called Hfq, and complementary DNA sequencing, the team tracked transcripts and small RNAs in a clinical isolate of N. meningitides that's considered hyperinvasive. The analysis led to more than 1,600 transcriptional start sites and dozens of potential small RNAs, including RNAs and apparent transcript targets that seemed to be regulated post-transcriptionally by Hfq.