Skip to main content
Premium Trial:

Request an Annual Quote

This Week in Nucleic Acids Research: Aug 3, 2016

Researchers from Indiana University assess Escherichia coli genome rearrangements stemming from a form of mobile genetic elements called insertion sequence (IS) elements. Using a software tool called "Graph-based Rearrangement Analysis from Short and Paired-end Reads (GRASPER), the team searched for large-scale rearrangements in the genomes of E. coli from more than 500 mutation accumulation lines, identifying more than 750 insertions, a single excision, and 98 recombinations between different IS elements. From these and other data, they went on to estimate IS element insertion rates, to find the most active IS families, and to uncover deletion hotspots in E. coli.

A team from France and Brazil presents an approach for finding, quantifying, and annotating SNPs from RNA sequence data, even when a reference genome is unavailable. The de novo SNP detection method builds from existing software called KisSplice, the researchers note. With a statistical approach dubbed KisDE, for example, they showed that it was possible to find condition-specific SNPs, while their KisSplice2RefTranscriptome step predicted amino changes in proteins encoded by genes containing such SNPs. Based on their results so far, the study's authors argue that the approach "can be used for any species to annotate SNPs and predict their impact on the protein sequence."

Swedish researchers report on the FocalScan algorithm, a tool intended to find sites in cancer genomes that are affected by copy number changes that subsequently alter the expression of genes in that region. Using DNA copy number and RNA sequencing data, they explain, the integrative metric behind FocalScan brings together copy number and expression patterns at specific genes or in an annotation-independent manner to narrow in sites that may promote or suppress cancer. The team applied the approach to data for 971 invasive breast carcinoma samples assessed by The Cancer Genome Atlas, demonstrating that it came up with a set of candidate cancer drivers that was enriched for known cancer genes.