Researchers from the US and Colombia present an algorithm called STing, designed for analyzing microbial genome sequence data to pick out molecular sequence types, gene sequences, and other key features in the bugs. The method improves on a prior k-mer matching algorithm known as stringMLST, the team says, using tweaks intended to help scale up genome-based molecular typing without losing speed or accuracy. When the authors compared STing with half a dozen other molecular typing methods based on genome sequence analyses, they found that the method could accurately identify multi-locus sequence types based on seven genes within seconds. "STing shows superior accuracy and performance for standard multilocus sequence typing schemes, along with large genome-scale typing schemes," they report, "and it enables rapid automated detection of antimicrobial resistance and virulence factor genes."
A Stanford University team outlines a computational strategy for finding so-called R-loop structures that involve hybridized RNA and DNA strands, along with a corresponding strand of DNA displaced by this molecular dalliance. The researchers combined their "quantitative differential DNA-RNA immunoprecipitation," or qDRIP, immunoprecipitation approach with strand-specific next-generation sequencing to track and tally RNA-DNA hybrids across the genome in human cells. From their findings, the authors suggest that qDRIP-seq "provides high-resolution, strand-specific maps of RNA-DNA hybrids, and allows for quantitative comparisons to be made between conditions where R-loop levels are perturbed."
Finally, investigators in Denmark and Germany describe a deep learning-based computational tool dubbed DeepCLIP that is designed to uncover sites where proteins bind RNA based on sequence data, while taking into account the wider genomic context. The approach combines cross-linking and immunoprecipitation methods with sequencing and deep learning, the team writes. "Both in vitro binding assays and in vivo splicing assays as well as observed splicing of disease-causing mutations in patients cells correlate well with DeepCLIP predictions," the authors report, suggesting that "an in silico analysis with DeepCLIP can serve as a valuable tool for assessing the functional effects of potentially pathogenic sequence variants, providing an important tool for clinical diagnosis."