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This Week in Genome Biology: Nov 29, 2017

A Seoul National University-led team expands the set of available reference genomes for hot pepper plants, using these genomes to explore transposable element effects on the expansion of gene families related to disease resistance. In addition to sequencing and assembling new reference genomes for Capsicum baccatum and C. chinense peppers, generating more than 130-fold sequence coverage for each plant, the researchers annotated and improved on an existing reference genome for the C. annuum plant. Comparing these genomes to one another and to sequences from other plants, they uncovered a role for retroduplication in new disease resistance genes from nucleotide-binding and leucine-rich-repeat, or NLR, protein families. 

Researchers from the University of Bath and University of Edinburgh describe a dip in synonymous somatic mutation density in splicing-related sequences from cancer genomes. For its analysis, the team turned to data from the Cancer Genome Atlas project, bringing together information on synonymous mutations in thousands of tumor samples from 15 tumor types. Synonymous somatic mutation density was roughly 17 percent lower in exon flank sequences involved in splicing fidelity. While they could not rule out mutation bias, the authors suspect the pattern stems from purifying selection and argue that their results "have implications for modeling somatic mutations during cancer evolution, identifying additional splicing-associated sequences, functional annotation of synonymous somatic variants, and identification of cancer-driving mutations."

Baylor College of Medicine's Sharon Plon and colleagues compare the variant classification performance of more than two dozen algorithms, in the hopes of achieving more standardized clinical variant interpretation under existing ACMG/AMP guidelines. The team brought together 14,819 benign or pathogenic missense variants from the ClinVar database, looking at the variant classification achieved with 25 in silico algorithms. The researchers saw "wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants," they report, and uncovered a previously undetected type of variant interpretation error dubbed false concordance, in which algorithms agreed on classifications that were odds with those achieved with other types of information for ClinVar.