Paragraph: A graph-based structural variant genotyper for short-read sequence data

Authors: Chen, Sai and Krusche, Peter and Dolzhenko, Egor and Sherman, Rachel M and Petrovski, Roman and Schlesinger, Felix and Kirsche, Michael and Bentley, David R and Schatz, Michael S and Sedlazeck, Fritz J and others

Accurate detection and genotyping of structural variations (SVs) from short-read data is a long-standing area of development in genomics research and clinical sequencing pipelines. We introduce Paragraph, a fast and accurate genotyper that models SVs using sequence graphs and SV annotations produced by a range of methods and technologies. We demonstrate the accuracy of Paragraph on whole genome sequence data from a control sample with both short and long read sequencing data available, and then apply it at scale to a cohort of 100 samples of diverse ancestry sequenced with short-reads. Comparative analyses indicate that Paragraph has better accuracy than other existing genotypers. The Paragraph software is open-source and available at ?https://github.com/Illumina/paragraph

Journal: BioRxiv
DOI: 10.1101/635011
Year: 2019

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