Skip to main content
Premium Trial:

Request an Annual Quote

This Week in Genome Biology: Mar 21, 2018

A University College London team takes a look at transcription factor use in the brittle star (Amphiura filiformis), an echinoderm from the ophiuroid class. Using messenger RNA sequencing, de novo transcriptome assembly, and a newly developed transcriptome quantification pipeline, the researchers tracked expression in developing brittle star embryos, uncovering distinct timing for transcription factor expression across four brittle star development stages compared to three other echinoderms (including the sea urchin, from the echinoid class). "Our results reveal a high degree of conservation of genes associated with skeleton formation in the four species," the authors write, noting that they also saw "major differences in the temporal expression of regulatory genes, which suggests a high degree of re-wiring for the developmental [gene regulatory networks]." 

Researchers from the US and Korea present an algorithm called FusorSV for evaluating and optimizing approaches for calling structural variants from next generation sequence data. Starting from deep genome sequence data for more than two dozen participants in the 1000 Genomes Project, the team brought together features from eight structural variant-calling algorithms and set up a fusion model to further merge call sets. In the 27 1000GP genomes, for example, the FusorSV approach unearthed 843 structural variants not detected previously. Subsequent validation experiments suggest that the majority of the new structural variant calls were authentic, the authors say, adding that "an ensemble of algorithms with a data mining approach to callset merging provides higher quality [structural variant] calls than what is possible with a single algorithm."

A team from Japan describes a high-throughput, single-cell RNA sequencing strategy dubbed Quartz-Seq2, over-hauling several library preparation steps to bump up the conversion of the sequence data to unique molecular identifier (UMI) counts for genes expressed in individual cells. For their proof-of-principle experiments, the researchers used Quartz-Seq2 to assess thousands of individual mouse embryonic stem cells or differentiated cells. The authors note that the approach, which has UMI conversion efficiencies of exceeding 30 percent, "can facilitate investigation of the cell state within a cell type, such as gradated or stochastic changes of the cell population in organism development and disease progression."