A team from Poland, Germany, Austria, and the US provides genetic evidence linking recently discovered remains at a World War II-era death camp in Poland to individuals with Ashkenazi Jewish ancestry. With targeted mitochondrial DNA sequencing, Y chromosomal marker analyses, and phylogenetics, the researchers assessed remains from 10 male individuals found during a 2013 archeological search of the Sobibór camp. "Based on the archaeological analysis of the burials and information gathered by local historians, it was initially assumed that these remains may have belonged to a group of Polish partisans, who were killed in the 1950s by the communist government and buried secretly in that area," they write. But based on mitochondrial haplogroup analyses and comparisons to available mitogenome data for hundreds of individuals from Polish or Ashkenazi Jewish populations, the authors found that the individuals came from a Jewish population, which the researchers say led to traditional Jewish burials for the victims.
Researchers at the University of Lausanne, the Swiss Cancer Center, and elsewhere describe gene co-regulation at a subset of chromatin regions in human cancers. Using a "differentially active domain" (DADo) detection algorithm that brings together Hi-C chromatin interaction profiles and gene expression data, the team characterized differentially active domains in human cancers, including forms of lung adenocarcinoma driven by KRAS or EGFR mutations. "Our results consistently showed that gene co-regulation occurs only in a small, yet significant fraction of chromatin domains," the authors report, noting that "these domains provided complementary information to standard differential gene expression analyses." "[W]e expect that differentially active domains alongside differentially expressed genes will provide a more complete picture of transcriptional differences emerging in multiple biological contexts," they write.
Finally, a Stanford University-led team presents a statistical strategy called "single cell precise splice estimation" (SICILIAN) for finding splice junctions in single-cell RNA sequence data. The strategy relies on statistical scoring and score aggregation to identify authentic read junctions from alignment data, the researchers note. After validating SICILIAN using simulated data, as well as scRNA-seq and bulk RNA sequence data for a handful of lung adenocarcinoma cell lines, they applied the method to data from tens of thousands of human lung cells or mouse lemur cells, identifying previously unappreciated splice junctions. "SICILIAN detects unannotated splicing in single cells," the authors write, "enabling the discovery of novel splicing regulation through single-cell analysis workflows."