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This Week in Nucleic Acids Research: Aug 21, 2013

DNA damage in Saccharomyces cerevisiae can prompt various changes to the budding yeast's protein abundance and its protein localization patterns, according to a study by researchers at the Massachusetts Institute of Technology. The team exposed a library of yeast strains with fluorescently tagged proteins to a DNA damaging chemical called methyl methanesulfonate. When they followed protein patterns in the exposed yeast cells, investigators saw groups of proteins that were more plentiful after DNA damage, including several transcription factors, as well as other proteins that were repressed in response to DNA damage stress. The study also revealed sets of proteins that seem to be enriched or depleted from nuclear or cytoplasmic portions of the cell under DNA-damaging conditions. These and other findings suggest "cellular responses can navigate different routes according to the extent of [DNA] damage," study authors note, "relying on both expression and localization changes of specific proteins."

A team from Taiwan's Academia Sinica describes an algorithm designed to bring together a range of genetic variation, somatic mutation, and expression data in cancer genomes. The researchers used glioblastoma multiforme data generated by the Cancer Genome Atlas to test and validate the method, which looks to form so-called association modules from SNPs, copy number variants, methylation marks, expression patterns, mutation profiles, and so on. "The inferred association modules simultaneously recapitulate critical molecular aberrations in [glioblastoma multiforme]," authors of the study say, "and map their presence and absence among predefined molecular subtypes."

Researchers at Brigham and Women's Hospital, Harvard Medical School, and the Broad Institute applied a statistical filtering method to data from experiments that involve transposon-based mutagenesis followed by high-throughput sequencing in the cholera-causing pathogen Vibrio cholerae. Using this hidden Markov model-based filter, the team was able to tap transposon mutagenesis sequence data to get finer-than-usual resolution data regarding the genes that are required for — or beneficial to — the pathogen's growth.