Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies
| Autor: | Conway C. Huang, Glenn K. Lockwood, Nicholas J. Schork, S. Lamberth, Carrie Brodmerkel, Yauheniya Cherkas, Tristan M. Carland, Wayne Pfeiffer, Kristopher A. Standish, Gunaretnam Rajagopal, Ed Jaeger, Mark Curran, Lance Smith, Mahidhar Tatineni |
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| Rok vydání: | 2014 |
| Předmět: |
Big data
Genomics Variation (game tree) Computational biology Biology Biochemistry Polymorphism Single Nucleotide 03 medical and health sciences 0302 clinical medicine Structural Biology Variant calling Humans Molecular Biology 030304 developmental biology 030203 arthritis & rheumatology Whole genome sequencing 0303 health sciences Whole-genome sequencing business.industry Computers Genome Human Applied Mathematics Scale (chemistry) Methodology Article High-Throughput Nucleotide Sequencing Supercomputing Sequence Analysis DNA Supercomputer Data science Computer Science Applications Workflow Data Interpretation Statistical Human genome business Software |
| Zdroj: | BMC Bioinformatics |
| ISSN: | 1471-2105 |
| Popis: | Motivation Next-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. Results We 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. Conclusions We 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0736-4) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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