AUTHOR=Corbett Richard D. , Eveleigh Robert , Whitney Joe , Barai Namrata , Bourgey Mathieu , Chuah Eric , Johnson Joanne , Moore Richard A. , Moradin Neda , Mungall Karen L. , Pereira Sergio , Reuter Miriam S. , Thiruvahindrapuram Bhooma , Wintle Richard F. , Ragoussis Jiannis , Strug Lisa J. , Herbrick Jo-Anne , Aziz Naveed , Jones Steven J. M. , Lathrop Mark , Scherer Stephen W. , Staffa Alfredo , Mungall Andrew J. TITLE=A Distributed Whole Genome Sequencing Benchmark Study JOURNAL=Frontiers in Genetics VOLUME=11 YEAR=2020 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.612515 DOI=10.3389/fgene.2020.612515 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.