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Nature Papers Review Integration of Single-Cell Assay Data, Present Approach to Detect Rare Variants

A review of the different approaches taken to integrate data from single-cell assays is published in Nature Biotechnology, highlighting the challenges such efforts face and the need for new definitions and concepts to contextualize existing methods and to enable the development of new ones. In the report, scientists from the European Molecular Biology Laboratory discuss the current state of single-cell data integration and examine the parallels between approaches for genetic analysis of single-cell traits and inference of regulatory dependencies between molecular layers. They also explore hurdles to integrating single-cell molecular profiles across physical dimensions, as well as multiscale modeling strategies for tying cellular representations to medically relevant human traits.

A method for detecting low-frequency DNA variants using targeted sequencing of Watson and Crick strands is presented in Nature Biotechnology this week. Aiming to overcome issues with the baseline error rate of next-generation sequencing technologies that hamper rare mutation detection, a Johns Hopkins University team developed SaferSeqS. The method that involves identically barcoding both strands of template molecules followed by PCR-based enrichment of each strand and in silico reconstruction of template molecules. "Bona fide mutations present in the original starting templates are identified by requiring alterations to be found on both strands of the same initial DNA molecule," the researchers write. They show SaferSeqS can evaluate mutations in a single amplicon or simultaneously in multiple amplicons, assess limited quantities of cell-free DNA with high recovery of both strands, and reduce the error rate of existing PCR-based molecular barcoding approaches by over 100-fold.