Coverage analysis and visualization in clinical exome sequencing
Motivation: The advent of clinical exome sequencing will require new tools to handlecoverage data and making it relevant to clinicians. That means genes over targets, smartsoftware over BED-files, and full stack, automated solutions from BAM-files to genetic testreport. Fresh ideas can also provide new insights into the factors that cause certain regionsof the exome to receive poor coverage.Results: A novel coverage analysis tool for analyzing clinical exome sequencing data has beendeveloped. Named Chanjo, it’s capable of converting between different elements such astargets and exons, supports custom annotations, and provides powerful statistics andplotting options. A coverage investigation using Chanjo linked both extreme GC content andlow sequence complexity to poor coverage. High bait density was shown to increasereliability of exome capture but not improve coverage of regions that had already proventricky. To improve coverage of especially very G+C rich regions, developing new ways toamplify rather than enrich DNA will likely make the biggest difference.
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