By doing RNA sequencing on 922 genotyped individuals of European descent, a Stanford University-led group got a peek at regulatory variants influencing gene expression in that population. As they describe online in Genome Research, the researchers brought the transcriptome and genetic variant data together to dig up regulatory variants for more than 10,000 genes. The analysis highlighted variants contributing to particular splicing or allelic expression patterns, for example, as well as networks of variants acting within specific chromosomes or parts of the genome.
The Wellcome Trust Sanger Institute's Damian Smedley and colleagues from the UK, US, Germany, and the Netherlands present a scheme for prioritizing whole-exome sequencing results using cross-species phenotype comparisons. The researchers reasoned that phenotypic similarities and differences in humans and mice could help in discerning authentic disease-related mutations from normal variation in the genome. To that end, they came up with an algorithm called "Phenotypic Interpretation of Variants in Exomes," or PHIVE, that considers not only allele frequency and disease heritability patterns, but also phenotypic overlap between human disease and mouse genetic models — an approach validated using data for 100,000 simulated whole-exome sequences.
A team from the US and Korea took a systems biology-based look at ways in which methane production is regulated in the hydrogen-consuming methanogenic archaea species Methanococcus maripaludis. Using data from dozens of M. maripaludis populations grown in the lab — together with information on the microbe's transcriptome and protein repertoire — the researchers developed a so-called "environment and gene-regulatory influence network," or EGRIN, model pointing to players in M. maripaludis methanogenesis. "The EGRIN model demonstrates regulatory affiliations within methanogenesis," study authors say, "as well as between methanogenesis and other cellular functions."