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Research article
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
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.
Keywords
Next-generation sequencing (NGS) technologies, platform-biases, healthy aging, Illumina, Wellderly, Longevity, Complete Genomics, Aging, GATK
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CURRENT STATUS: Published
View the published article at BMC Genomics.
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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
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
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Subject Areas
Epigenetics & Genomics
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See the published version of this preprint at BMC Genomics.
This is a preprint, 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
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
View the published article at BMC Genomics.
Version 3
Posted 11 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
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 article 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 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This is a list of supplementary files associated with this preprint. Click to download.
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