In Genome Research this week, researchers at the University of California, San Diego, and the University of Massachusetts, Amherst, report their structural and operational annotation of the Geobacter sulfurreducens genome. By combining proteomics, transcriptomics, RNA polymerase and sigma factor binding data with "deep-sequencing based analysis of primary 5' end transcripts allowed for a most precise annotation," the authors write. "The experimentally determined structural and operational annotations can be combined with functional annotation yielding a new three-level annotation that greatly expands our understanding of prokaryotic genomes," the team concludes.
Investigators in Switzerland this week examine "a global network of coexisting microbes from environmental and whole-genome sequence data." Using taxonomically grouped, publicly available 16S rRNA sequences, the team systematically searched for co-occurrence across environments; they found that several coexisting lineages are closely related phylogenetically, although they discovered "a significant number of distant associations" as well. The team hypothesizes that "groupings of lineages are often ancient, and that they may have significantly impacted on genome evolution."
A research team from the Centre for Genomic Regulation in Barcelona describes their method for direct "strand-specific deep sequencing of the transcriptome," using the Illumina platform in the July issue of Genome Research. Testing their method with both prokaryotic and eukaryotic samples, the team suggests that it's a "simple and efficient strategy for strand-specific transcriptome sequencing and as a tool for genome annotation exploiting the increased read lengths that next-generation sequencing technology now is capable to deliver." They validated their results with qPCR.
Hui Wang at the Baylor College of Medicine Human Genome Sequencing Center and colleagues detail their method to identify heterozygous mutations in Drosophila using genomic capture sequencing in Genome Research this week. Wang et al. show that "by combining rough genetic mapping, targeted DNA capture, and second generation sequencing technology," they could identify such mutations with relative speed and ease, at a reduced cost — as low as $1,000 per mutant.