PT  - JOURNAL ARTICLE
AU  - Michael D. Kaiser
AU  - Jennifer R. Davis
AU  - Boris S. Grinberg
AU  - John S. Oliver
AU  - Jay M. Sage
AU  - Leah Seward
AU  - Barrett Bready
TI  - Automated Structural Variant Verification in Human Genomes using Single-Molecule Electronic DNA Mapping
AID  - 10.1101/140699
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 140699
4099  - http://biorxiv.org/content/early/2017/05/22/140699.short
4100  - http://biorxiv.org/content/early/2017/05/22/140699.full
AB  - The importance of structural variation in human disease and the difficulty of detecting structural variants larger than 50 base pairs has led to the development of several long-read sequencing technologies and optical mapping platforms. Frequently, multiple technologies and ad hoc methods are required to obtain a consensus regarding the location, size and nature of a structural variant, with no approach able to reliably bridge the gap of variant sizes between the domain of short-read approaches and the largest rearrangements observed with optical mapping.To address this unmet need, we have developed a new software package, SV-Verify™, which utilizes data collected with the Nabsys High Definition Mapping (HD-Mapping™) system, to perform hypothesis-based verification of putative deletions. We demonstrate that whole genome maps, constructed from electronic detection of tagged DNA, hundreds of kilobases in length, can be used effectively to facilitate calling of structural variants ranging in size from 300 base pairs to hundreds of kilobase pairs. SV-Verify implements hypothesis-based verification of putative structural variants using a set of support vector machines and is capable of concurrently testing several thousand independent hypotheses. We describe support vector machine training, utilizing a well-characterized human genome, and application of the resulting classifiers to another human genome, demonstrating high sensitivity and specificity for deletions ≥300 base pairs.