OGT to Share Strategies for Reliable NGS Panel Assays
News Sep 19, 2013
Oxford Gene Technology (OGT) will share its expertise in NGS assay design by hosting a live webinar entitled ‘Enhanced bait design strategies that deliver more reliable NGS panel assays’ on 3rd October 2013, at 3:00pm (BST).
Guest speaker, Dr Chris Mattocks, Senior Clinical Research Scientist at the National Genetics Reference Laboratory (Wessex), Salisbury UK, will discuss the advantages of hybridization enrichment and the importance of optimal bait design to ensure increased uniformity for all target regions.
As the use of NGS moves closer to the clinic, it is of paramount importance that relevant mutations can be detected and called with complete certainty.
Only reliable and reproducible methods will inspire enough confidence to allow the transition of NGS assays from useful research tools to clinically dependable tests.
As such, it is becoming increasingly apparent that some NGS enrichment methods do not reliably detect known mutants due to issues with PCR artefacts, excessive off-target enrichment, target region bias, non-uniformity of coverage or even wholesale target region drop-out.
This highlights the need for methods with increased uniformity of coverage (when each locus is read at sufficient depth without resorting to increasing the number of reads) and minimal variation between loci, to provide confidence that all loci are covered at dependable levels.
As well as discussing bait design, the webinar will also cover the impact of coverage, non-conformity on sequencing efficiency and cost, and how coverage affects confidence levels in the results.
OGT’s significant expertise in NGS is demonstrated by Genefficiency™ Custom Targeted Sequencing Services, which combine expert bait design with a unique and fully interactive variant analysis report that delivers rapid access to reliable, meaningful results.
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.
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