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This Week in Genome Research: May 4, 2011

In a paper published online in advance in Genome Research this week, investigators at the Wellcome Trust Sanger Institute and elsewhere present an approach to map trait loci via artificial selection in populations of haploid and diploid segregants, which the team generated by "crossing two budding yeast strains of different heat tolerance for up to 12 generations." Parts et al. report that, by taking this approach, they were "able to detect the same set of trait loci in a population of diploid individuals with similar power and resolution, and observed primarily additive effects, similar to what is seen for complex trait genetics in other diploid organisms such as humans."

Researchers at the Berlin Institute for Medical Systems Biology and the University of Utah this week describe their use of massively parallel sequencing and shotgun proteomics to de novo assemble a freshwater planaria Schmidtea mediterranea transcriptome, and suggest that this pipeline could potentially be extended to other organisms that have no prior genome assemblies. In its Genome Research paper, the team describes its "efficient sequencing strategy" and validation of S. mediterranea transcript authenticity via "independent assays and massive shotgun proteomics."

Investigators at the University of Pennsylvania and the Children's Hospital of Philadelphia report their RNA-seq-based analysis of human B-cells this week. "We identified 20,766 genes and 67,453 of their alternatively spliced transcripts," the Penn-CHOP team reports online in Genome Research. In addition, the researchers suggest that "while 100 million reads are sufficient to detect most expressed genes and transcripts, about 500 million reads are needed to measure accurately their expression levels."

In another paper appearing online in advance, researchers at the University of Hawaii at Manoa and the J. Craig Venter Institute describe an approach for total transcript amplification from a single bacterium for RNA-seq-based transcriptome analysis. In assessing its amplification approach via microarray analysis, the team found that the "method showed low fold-change bias and dropouts."