학술논문
A Distributed Whole Genome Sequencing Benchmark Study
Document Type
article
Author
Richard D. Corbett; Robert Eveleigh; Joe Whitney; Namrata Barai; Mathieu Bourgey; Eric Chuah; Joanne Johnson; Richard A. Moore; Neda Moradin; Karen L. Mungall; Sergio Pereira; Miriam S. Reuter; Bhooma Thiruvahindrapuram; Richard F. Wintle; Jiannis Ragoussis; Lisa J. Strug; Jo-Anne Herbrick; Naveed Aziz; Steven J. M. Jones; Mark Lathrop; Stephen W. Scherer; Alfredo Staffa; Andrew J. Mungall
Source
Frontiers in Genetics, Vol 11 (2020)
Subject
Language
English
ISSN
1664-8021
Abstract
Population sequencing often requires collaboration across a distributed network of sequencing centers for the timely processing of thousands of samples. In such massive efforts, it is important that participating scientists can be confident that the accuracy of the sequence data produced is not affected by which center generates the data. A study was conducted across three established sequencing centers, located in Montreal, Toronto, and Vancouver, constituting Canada’s Genomics Enterprise (www.cgen.ca). Whole genome sequencing was performed at each center, on three genomic DNA replicates from three well-characterized cell lines. Secondary analysis pipelines employed by each site were applied to sequence data from each of the sites, resulting in three datasets for each of four variables (cell line, replicate, sequencing center, and analysis pipeline), for a total of 81 datasets. These datasets were each assessed according to multiple quality metrics including concordance with benchmark variant truth sets to assess consistent quality across all three conditions for each variable. Three-way concordance analysis of variants across conditions for each variable was performed. Our results showed that the variant concordance between datasets differing only by sequencing center was similar to the concordance for datasets differing only by replicate, using the same analysis pipeline. We also showed that the statistically significant differences between datasets result from the analysis pipeline used, which can be unified and updated as new approaches become available. We conclude that genome sequencing projects can rely on the quality and reproducibility of aggregate data generated across a network of distributed sites.