Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies

BMC Bioinformatics

Methodology Article

Kristopher A. Standish¹  Tristan M. Carland²  Nicholas J. Schork²  Mahidhar Tatineni³  Glenn K. Lockwood³  Wayne Pfeiffer³  Carrie Brodmerkel⁴  Mark E. Curran⁴  Yauheniya Cherkas⁴  Sarah Lamberth⁴  C Chris Huang⁴  Lance Smith⁵  Ed Jaeger⁵  Gunaretnam Rajagopal⁵ 

[1] Biomedical Sciences Graduate Program, University of California, San Diego, Gilman Drive, 92092, La Jolla, CA, USA;Human Biology, J. Craig Venter Institute, 4120 Capricorn Lane, 92092, La Jolla, CA, USA;Human Biology, J. Craig Venter Institute, 4120 Capricorn Lane, 92092, La Jolla, CA, USA;San Diego Supercomputer Center, University of California, San Diego, Gilman Drive, 92092, La Jolla, CA, USA;Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA;Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA;R&D IT, Janssen R&D LLC, Springhouse, PA, USA;

关键词: Variant calling;    Supercomputing;    Whole-genome sequencing;   

DOI  :  10.1186/s12859-015-0736-4

 received in 2014-12-13, accepted in 2015-09-11,  发布年份 2015

来源: Springer

【 摘 要 】

MotivationNext-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost.ResultsWe describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study.ConclusionsWe ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging ‘big data’ problems in biomedical research brought on by the expansion of NGS technologies.

【 授权许可】

CC BY   
© Standish et al. 2015