Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-6291
Authors: Weißbach, Stephan
Sys, Stanislav
Hewel, Charlotte
Todorov, Hristo
Schweiger, Susann
Winter, Jennifer
Pfenninger, Markus
Torkamani, Ali
Evans, Doug
Burger, Joachim
Everschor-Sitte, Karin
May-Simera, Helen Louise
Gerber, Susanne
Title: Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines
Online publication date: 18-Aug-2021
Language: english
Abstract: BACKGROUND: Next Generation Sequencing (NGS) is the fundament of various studies, providing insights into questions from biology and medicine. Nevertheless, integrating data from different experimental backgrounds can introduce strong biases. In order to methodically investigate the magnitude of systematic errors in single nucleotide variant calls, we performed a cross-sectional observational study on a genomic cohort of 99 subjects each sequenced via (i) Illumina HiSeq X, (ii) Illumina HiSeq, and (iii) Complete Genomics and processed with the respective bioinformatic pipeline. We also repeated variant calling for the Illumina cohorts with GATK, which allowed us to investigate the effect of the bioinformatics analysis strategy separately from the sequencing platform’s impact. RESULTS: The number of detected variants/variant classes per individual was highly dependent on the experimental setup. We observed a statistically significant overrepresentation of variants uniquely called by a single setup, indicating potential systematic biases. Insertion/deletion polymorphisms (indels) were associated with decreased concordance compared to single nucleotide polymorphisms (SNPs). The discrepancies in indel absolute numbers were particularly prominent in introns, Alu elements, simple repeats, and regions with medium GC content. Notably, reprocessing sequencing data following the best practice recommendations of GATK considerably improved concordance between the respective setups. CONCLUSION: We provide empirical evidence of systematic heterogeneity in variant calls between alternative experimental and data analysis setups. Furthermore, our results demonstrate the benefit of reprocessing genomic data with harmonized pipelines when integrating data from different studies.
DDC: 570 Biowissenschaften
570 Life sciences
610 Medizin
610 Medical sciences
620 Ingenieurwissenschaften und Maschinenbau
620 Engineering and allied operations
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 04 Medizin
Place: Mainz
DOI: http://doi.org/10.25358/openscience-6291
Version: Published version
Publication type: Zeitschriftenaufsatz
License: CC-BY
Information on rights of use: https://creativecommons.org/licenses/by/4.0/
Journal: BMC genomics
22
Pages or article number: 62
Publisher: Springer
Publisher place: Heidelberg
Issue date: 2021
ISSN: 1471-2164
Publisher's URL: https://doi.org/10.1186/s12864-020-07362-8
Appears in collections:JGU-Publikationen

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