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Genome Research Studies on SARS-CoV-2 Super-Spreaders, Pan-Cancer Epigenetics, More

Investigators from Spain propose an outsized role for COVID-19 super-spreaders based on variants in more than 4,700 SARS-CoV-2 genome sequences. Although the sequence identity across the SARS-CoV-2 genomes was high, exceeding 99.9 percent, the team proposed two main SARS-CoV-2 haplogroups originating in Asia, along with more than 160 viral strains spanning the globe. The authors caution that "[i]dentification of the root of SARS-CoV-2 genomes is not without problems." Even so, their phylogenetic and other analyses revealed clues to coronavirus spread, suggesting "[m]ultiple founder effect episodes, most likely associated with super-spreader hosts, might explain [the] COVID-19 pandemic to a large extent."

An International Agency for Research on Cancer team from France shares findings from a multi-omics analysis of nearly three dozen cancer types. By incorporating available gene expression patterns, DNA methylation profiles, and more for hundreds of genes with apparent roles in histone modification, DNA methylation, or other epigenetic processes, the researchers searched for epigenetic regulatory gene alterations in more than 10,800 tumor samples and 730 cancer-free samples. "We found that, in addition to mutations, copy number alterations in [epigenetic regulator genes] were more frequent than previously anticipated and tightly linked to expression aberrations," they report, noting that a corresponding CRISPR-Cas9-based epigenetic regulator gene screen offered a look at potential "epidriver" genes behind different cancer types.

Finally, researchers from the University of Wisconsin outline an analytical approach designed to pick up previously unappreciated transcripts from RNA sequencing data. The "Pooling RNA-seq and Assembling Models," or PRAM, approach is a one-step alternative to two-step strategies for identifying transcripts, the team writes, particularly those in intergenic regions. The authors used PRAM to take a look at dozens of RNA-seq datasets from the ENCODE study, for example, and in an analysis of transcripts found in mouse hematopoietic RNA-seq data. "We demonstrate in a computational benchmark that '1-Step' outperforms '2-Step' approaches in predicting overall transcript structures and individual splice junctions, while performing competitively in detecting exonic nucleotides," they write.