Italian researchers introduce a tool called EXCAVATOR2 that's designed to dig up copy number variants that would otherwise be missed from whole-exome sequence data. The read count-based tool takes advantage of both in- and off-target regions of the exome, providing a look at the portions of the genome not targeted by exome capture. The team applied the approach to exome data from the 1000 Genomes Project and exome sequences from 14 individuals with bladder cancer. Based on their results, the study's authors note that the approach "enlarge[s] the spectrum of detectable CNVs from [whole-exome sequence] data with an unprecedented resolution."
A team from US and Japan describe results from a single-nucleus RNA sequencing (snRNA-seq) study aimed at untangling human myoblast differentiation. By comparing the RNA present in individual immortalized human myoblasts before and after differentiation, the researchers narrowed in on transcripts, long, non-coding RNAs, and microRNA precursors that marked various myoblast cell types. "Our results further indicate that snRNA-seq has [a] unique advantage in capturing nucleus-enriched lncRNAs and miRNA precursors that are useful in mapping and monitoring differential miRNA expression during cellular differentiation," they write.
Investigators from George Washington University and elsewhere discuss a computational tool for quantitatively considering allele counts based on paired RNA and DNA sequence data. The method, known as RNA2DNAlign, uses read count and variant abundance cues to pick up instances of allele asymmetry reflecting regulatory activity such as RNA editing or somatic mutagenesis. When the team used RNA2DNAlign to assess matched tumor-normal exomes and transcriptomes from 90 individuals with breast cancer from the Cancer Genome Atlas project, it detected more than 2,000 high-confidence allelic asymmetries.