A team led by investigators at the Mayo Clinic in Jacksonville focuses on genome regions that are missed or difficult-to-pin down with Illumina short read data due to a dearth of mappable reads or ambiguous alignments. After identifying almost 37,000 'dark' genome regions with little or no coverage depth — which fell in or around more than 6,000 gene bodies from disease-, development-, or reproduction-related pathways — they looked at how well the regions could be resolved with 10x Genomic, Pacific Biosciences, or Oxford Nanopore linked- or long reads. From there, the investigators came up with an algorithm for refining 'camouflaged' genome regions with ambiguous alignments — an approach they applied to whole-genome sequence data from the Alzheimer's Disease Sequencing Project, uncovering more than 4,200 new variants and a rare frameshift deletion in the CR1 gene.
Researchers from the Baylor College of Medicine and elsewhere present an algorithm for gauging DNA methylation variation based on deep, whole-genome bisulfite sequence data. The team used this computational approach to look at between-individual cytosine methylation differences in bisulfite-sequenced thyroid, heart, and brain samples from 10 Genotype-Tissue Expression project participants, representing tissues produced from endoderm, mesoderm, or ectoderm germ layers, respectively. In the process, the authors pinned down 9,926 "correlated regions of systemic interindividual variation," or CoRSIVs, which were further characterized in the context of gene expression and genome features. Based on their results, they suggest that CoRSIV methylation in one tissue can predict expression of associated genes in other tissues."
Finally, a team from Spain describes a computational framework for teasing regulatory network insights out of single-cell RNA sequence data. The strategy relies on a correlation metric designed to pick up unappreciated between-gene correlations, the researchers say. They used this method to explore regulatory networks in 11 mouse organs, a mouse model of Alzheimer's disease, and pancreatic samples from humans with or without type 2 diabetes, all profiled by single-cell RNA-seq. "Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis," the authors note, "significantly broadening the biological insights that can be obtained with this leading technology."