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PLoS One. 2015 Sep 18;10(9):e0138259. doi: 10.1371/journal.pone.0138259. eCollection 2015.

Robustness of Massively Parallel Sequencing Platforms.

Author information

1
Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey; Advanced Genomics and Bioinformatics Research Group (İGBAM), BİLGEM, The Scientific and Technological Research Council of Turkey (TÜBİTAK), Gebze, Kocaeli, Turkey.
2
TÜBİTAK - MAM - GMBE (The Scientific and Technological Research Council of Turkey, Genetic Engineering and Biotechnology Institute), Gebze, Kocaeli, Turkey.
3
Advanced Genomics and Bioinformatics Research Group (İGBAM), BİLGEM, The Scientific and Technological Research Council of Turkey (TÜBİTAK), Gebze, Kocaeli, Turkey.
4
Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey.
5
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
6
Department of Computer Engineering, Bilkent University, Ankara, Turkey.

Abstract

The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications.

PMID:
26382624
PMCID:
PMC4575192
DOI:
10.1371/journal.pone.0138259
[Indexed for MEDLINE]
Free PMC Article

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