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This Week in Genome Biology: Jul 12, 2017

An international team led by investigators in Estonia, the UK, and India look back at the population history of the Parsi in present-day India and Pakistan. Using a combination of array-, targeted sequencing-, and PCR-restriction fragment length polymorphism analyses, the researchers assessed autosomal chromosome, Y chromosome, and/or mitochondrial sequence data for 19 individuals from an Indian Parsi population in Mumbai and 24 Pakistani Parsi individuals from Karachi. Their results suggest mixing took place between Parsi and Indian populations some 1,200 years ago, for example, while current Parsi populations appear to be most closely related to populations in Iran and the Caucasus. The sequence data also pointed to patterns such as historical assimilation of Parsi women into broader populations, along with more recent Parsi population isolation. 

Researchers from the Chinese Academy of Sciences and elsewhere explore genetic relationships between high altitude-adapted Sherpa and Tibetan populations in Tibet, Nepal, and Qinghai. The team considered new and previously available sequence data for 111 Sherpa and 177 Tibetan individuals profiled with microarrays, targeted sequencing, or whole-genome sequencing. When it analyzed the sequence data alongside sequences for individuals from other populations in China and beyond, the group saw evidence of split between the Sherpa and Tibetan populations that appeared to take place thousands of years after the divergence between Sherpa-Tibetan and Han Chinese populations. The analysis suggested that genetic sub-populations might also exist within these groups, depending on their location and linguistic patterns.

University of Edinburgh researchers introduce an analytical method aimed at quantifying transcript splicing comprehensively from single-cell RNA sequence data. The approach — known as "Bayesian regression for isoform estimation," or BRIE — estimates transcriptome-wide isoform patterns using a statistical model that taps into available sequence read distribution data. For proof-of-principle experiments, the team applied BRIE to simulated and real data for mouse and human cells, demonstrating that the statistical approach successfully picked up differential splicing, and provided reproducible exon inclusion ratio estimates.