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This Week in Genome Research: May 24, 2017

A team from the UK presents findings from a genome sequencing study of esophageal adenocarcinoma, focusing on samples taken from 10 individuals before and after they received chemotherapy. For work done through the International Cancer Genome Consortium, the researchers searched for differences in single nucleotide changes, copy number variants, and other mutational features in matched pre- and post-chemotherapy samples from the original 10 cases, before expanding their analysis to include 62 more pre-treatment esophageal adenocarcinoma samples and 58 post-treatment samples — analyses that revealed relatively subtle genetic changes after chemotherapy treatment.

Korean researchers report on coding and non-coding transcriptome maps produced with a combination of stranded and unstranded RNA sequence data assembled with a high-performance pipeline known as CAFE. The team brought new stranded and unstranded RNA sequence data for mouse embryonic stem cells together with publicly available RNA-seq datasets and sequences generated by cap analysis gene expression by sequencing (CAGE-seq) or poly(A) sequencing, leading to what they say are more accurate transcriptome maps. "Our pipeline should not only help to build comprehensive, precise transcriptome maps from complex genomes, but also to expand the universe of non-coding genomes," the authors note.

Finally, a team from Norway and Florida demonstrate the insights that can be gleaned from an automated Blast pipeline called Leapfrog, designed to detect hidden orthologs, or orthologous genes that do not have obvious homology to sequences from related species. The researchers applied this approach to 35 transcriptome datasets for flatworms from dozens of worm lineages, uncovering more than 3,400 apparent hidden orthologs. "By using Leapfrog," the authors write, "we identify key centrosome-related genes and homeodomain classes previously reported as absent in free-living flatworms."