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Genome Biology Papers on 17th Century TB, RNA-Binding Protein Variant Prioritization, More

A Max Planck Institute for the Science of Human History-led team presents findings from an analysis of Mycobacterium tuberculosis sequences going back to the 17th century, through the lens of modern M. tuberculosis complex (MTBC) members. Starting with metagenomic sequencing and screening, the researchers put together a genome sequence for M. tuberculosis from a lung nodule in a mummified bishop who died in Sweden in the late 1600s, using phylogenomics to place the genome within the broader tuberculosis parasite tree and get a look at M. tuberculosis evolution. "[W]e offer confirmation that the extant MTBC, and all available ancient MTBC genomes, stem from a common ancestor that existed a maximum of 6,000 years before present," the authors write. "Many open questions remain, however, regarding the evolutionary history of the MTBC and its constituent lineages, as well as the role of tuberculosis in human history."

Researchers from Yale University and the University of California, Irvine, report on a computational tool called RADAR, designed for detecting variants most likely to affect the activity of RNA-binding proteins (RBP) behind post-transcriptional regulation. The team put together the variant-scoring framework for RADAR starting from RBP data generated for the "Encyclopedia of DNA Elements" (ENCODE) project, subsequently demonstrating that RADAR could pick up somatic variants influencing RBP function in breast cancer with help from COSMIC data. "Given the fast-expanding collection of RBP binding profiles from additional cell types, we envision that our RADAR framework can better tackle the functional consequences of mutations from both somatic and germline genomes," the paper's authors conclude.

A team from the Harbin Institute of Technology and other centers in China outlines a strategy for uncovering structural variants in the human genome using long-read sequence data. The read alignment-based and structural variant signature clustering approach, known as cuteSV, is designed for detecting deletions, insertions, duplications, inversions, translocations, and other structural variants in long-read sequence data produced with a range of error rate profiles, particularly when sequence coverage is relatively low, the researchers say. "Benchmark results demonstrate that cuteSV achieves good yields and performance simultaneously," they write, noting that cuteSV "has good sensitivity to detect [structural variants], even with low coverage sequencing data, and it also has outstanding scaling performance which is suited to handle many large datasets."