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This Week in Genome Biology: May 15, 2019

A pan-African population analysis suggests populations in sub-Saharan African experienced archaic gene flow from "an early divergent and currently extinct ghost modern human lineage." As it reports in Genome Biology, an international team led by investigators in Spain analyzed new and available whole-genome sequence data for 21 African individuals from 15 agriculturalist or hunter-gatherer populations. Along with sequence data for four Eurasian individuals and array-based genotypes for additional individuals from the Human Genome Diversity Project, the deep genome sequences provided insights into the genetic diversity and past interactions between populations on different parts of the continent. And based on half a dozen demographic models, the authors uncovered a "fingerprint of an archaic introgression" in sequences from individuals in the Khoisan, Mbuti Pygmies, and Mandenka populations. 

The University of Pennsylvania's Sarah Tishkoff and her colleagues take a look at the evolutionary history, population structure, and genomic variation in dozens of indigenous African populations. That team did whole-genome sequencing on 43 individuals from 22 populations, incorporating genome sequences for 49 additional individuals profiled for the Simons Genome Diversity Project for phylogenetic and other analyses. Together, the genomes represented 44 indigenous populations from four language phyla, with a range of cultures, subsistence lifestyles, and geography. The authors teased out ancestral patterns and population relationships over the last 200,000 years or so, demonstrating, for example, that the lineage leading to the San population was basal to other human lineages. "Our analyses also identified signatures of multiple waves of migration in Africa," they add, noting that migrating populations "encountered novel environments and selective pressures, resulting in local adaptation."

Finally, a Shandong University team presents a de novo transcriptome assembler known as TransLiG. The approach relies on path phasing and iterative line graphs constructed from splicing graphs to bring together transcriptome sequences with a range of sequence depth and paired-end features, the researchers say. They applied TransLiG to simulated data generated from tens of thousands of human transcripts as well as real transcripts sequences from human and mouse cells, demonstrating that the freely available method appears to compare favorably with five previous methods for doing de novo transcriptome assembly.