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Research article
Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines
Susanne Gerber
Johannes Gutenberg Universitat Mainz
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9513-0729
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Genomics.
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, 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. Consequently, we systematically analyzed the heterogeneity between the sequencing cohorts with respect to genomic annotation and common filter criteria like minimum allele frequency (MAF).
Results
The number of detected variants/variant classes per individual was highly dependent on the sequencing technology. We observed a statistically significant overrepresentation of variants uniquely called by a single platform which indicates potential systematic biases. These variants were enriched in low complexity genomic regions and simple repeats. Furthermore, estimates of allele frequency were highly discrepant for a subset of variants in pairwise comparisons between different sequencing platforms. Applying common filters – such as MAF 5% and HWE- greatly reduced the heterogeneity between cohorts but still left discrepancies of several thousand variants after filtering.
Conclusion
We provide empirical evidence of systematic heterogeneity in variant calls between alternative experimental and data analysis setups. Our results highlight the potential benefit of reprocessing genomic data with harmonized pipelines when integrating data from different studies.
Keywords
Next-generation sequencing (NGS) technologies, platform-biases, Genome-wide association studies (GWAS), healthy aging, Illumina, Wellderly, Longevity, Complete Genomics, Aging, conserved loci
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Version 1
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No comments provided
Editorial decision: Major revision
On 07 Sep, 2020
Review #2 received
Received 03 Sep, 2020
Review #1 received
Received 28 Aug, 2020
Reviewer #3 agreed
On 13 Aug, 2020
Reviewer #2 agreed
On 12 Aug, 2020
Reviewers invited
Invitations sent on 06 Aug, 2020
Reviewer #1 agreed
On 06 Aug, 2020
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First submitted
On 30 Jul, 2020
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Editor invited
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Subject Areas
Epigenetics & Genomics
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This preprint is under consideration at BMC Genomics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines
Susanne Gerber
Johannes Gutenberg Universitat Mainz
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9513-0729
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Published
On 19 Jan, 2021
No community comments so far
Submission checks complete
On 29 Dec, 2020
Editorial decision: Accept
On 28 Dec, 2020
Version (no preprint)
Posted 21 Dec, 2020
Editorial decision: Minor revision
On 21 Dec, 2020
Editor assigned
On 20 Dec, 2020
Submission checks complete
On 20 Dec, 2020
Editor invited
On 20 Dec, 2020
This version was not preprinted
No comments provided
Editorial decision: Minor revision
On 14 Dec, 2020
Review #2 received
Received 04 Dec, 2020
Review #1 received
Received 04 Dec, 2020
Reviewer #2 agreed
On 30 Nov, 2020
Editor assigned
On 30 Nov, 2020
Reviewers invited
Invitations sent on 30 Nov, 2020
Reviewer #1 agreed
On 30 Nov, 2020
Submission checks complete
On 30 Nov, 2020
Editor invited
On 30 Nov, 2020
Version 1
Posted 07 Aug, 2020
No comments provided
Editorial decision: Major revision
On 07 Sep, 2020
Review #2 received
Received 03 Sep, 2020
Review #1 received
Received 28 Aug, 2020
Reviewer #3 agreed
On 13 Aug, 2020
Reviewer #2 agreed
On 12 Aug, 2020
Reviewers invited
Invitations sent on 06 Aug, 2020
Reviewer #1 agreed
On 06 Aug, 2020
Editor assigned
On 31 Jul, 2020
First submitted
On 30 Jul, 2020
Submission checks complete
On 30 Jul, 2020
Editor invited
On 30 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Genomics.
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, 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. Consequently, we systematically analyzed the heterogeneity between the sequencing cohorts with respect to genomic annotation and common filter criteria like minimum allele frequency (MAF).
Results
The number of detected variants/variant classes per individual was highly dependent on the sequencing technology. We observed a statistically significant overrepresentation of variants uniquely called by a single platform which indicates potential systematic biases. These variants were enriched in low complexity genomic regions and simple repeats. Furthermore, estimates of allele frequency were highly discrepant for a subset of variants in pairwise comparisons between different sequencing platforms. Applying common filters – such as MAF 5% and HWE- greatly reduced the heterogeneity between cohorts but still left discrepancies of several thousand variants after filtering.
Conclusion
We provide empirical evidence of systematic heterogeneity in variant calls between alternative experimental and data analysis setups. Our results highlight the potential benefit of reprocessing genomic data with harmonized pipelines when integrating data from different studies.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6