A team from Germany and Hungary outlines strategies to come up with cell death or proliferation signatures by tapping into transcriptomic, cell viability, and chemical screening data. The researchers considered transcriptomic perturbation data from phases I and II of the LINCS-L1000 project — alongside essential gene clues gleaned from short hairpin RNA screening for the Achilles project and cell viability data compiled in the Cancer Therapeutics Response Portal from the CTRP cell viability project — for an integrated gene signature-cell viability analysis. Using this approach, the authors not only tracked down transcription factors with apparent ties to cell death or cell proliferation, but also established transcription-based signature called the "CEII viability calculator," or VIChE, that showed promise for predicting cell viability after a range of exposures, including drug responses in specific cell types.
Chinese Academy of Sciences and Shanghai Tech University researchers provide details on a population genome database that specifically represents Han Chinese individuals. The interactive, online resource, known as PGG.Han, currently contains deep whole-genome sequences and/or high-density SNP profiles for nearly 115,000 Han Chinese individuals from dozens of administrative districts in China or Singapore who were assessed for the Han100K effort, the team says. Along with insights for looking at genotype-phenotype links in a population structure-informed way, the authors write, PGG.Han "provides a variety of online analysis tools, including ancestry inference, genotype imputation, and a routine [genome-wide association] pipeline."
Finally, a team from China's Central South University outlines the Gene4Denovo database, which is designed for compiling and uncovering de novo mutations in the human genome. The researchers sifted through published whole-genome or exome sequences for almost 24,000 individuals from two-dozen phenotype groups, including individuals with autism spectrum disorder, schizophrenia, coronary heart disease, and other conditions. Using these data, they uncovered more than 30,000 de novo mutations in coding parts of the genome and some 580,799 de novo mutations across the genomes. Moreover, the authors say they "developed a user-friendly integrated database called Gene4Denovo, which allows [de novo mutations], candidate genes, and annotation information in humans to be conveniently searched, browsed, and analyzed."