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United States Department of Agriculture

Agricultural Research Service

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Using the whole read: structural variant detection using NGS data

Authors
item Bickhart, Derek
item Cole, John
item Hutchison, Jana
item Xu, Lingyang
item Liu, Ge

Submitted to: World Congress of Genetics Applied in Livestock Production
Publication Type: Proceedings
Publication Acceptance Date: April 21, 2014
Publication Date: August 17, 2014
Citation: Bickhart, D.M., Cole, J.B., Hutchison, J.L., Xu, L., Liu, G. 2014. Using the whole read: structural variant detection using NGS data. World Congress of Genetics Applied in Livestock Production. Vancouver, Canada, Aug. 17–22. 5 pp.

Technical Abstract: Several classes of Structural Variants (SV) remain difficult to detect within sequenced genomes. Deletions and tandem duplications may affect a large proportion of variable genomic sequence space, yet their detection is still difficult to discern from false positive signals. Here, we present a method for detecting such variants from short-read sequence data using the orientation and distance of paired-end, and split-read mappings in addition to using read-depth as a filtering agent. We test our data using simulated SVs and find that our method is 27.5 times more precise than a competing detection program in detecting tandem duplications. Our method is also able to detect three times the number of deletions than the competition. This high degree of precision should enable better functional prediction of SVs from short-read sequence data.

Last Modified: 10/23/2014
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