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Research article
Danielle G. Lemay
USDA ARS Western Human Nutrition Research Center and University of California Davis Genome Center
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3318-0485
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX. Results Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene. Conclusions Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.
Keywords
Metagenomes, metagenomics, fecal, stool, human, functional annotation, function, gene-centric analysis, bioinformatics, microbiome
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CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
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Editorial decision: Accept
On 17 Feb, 2020
Editor assigned
On 14 Feb, 2020
Submission checks complete
On 13 Feb, 2020
Editor invited
On 13 Feb, 2020
Version 2
Posted 08 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 07 Feb, 2020
Review #2 received
Received 27 Jan, 2020
Review #1 received
Received 14 Jan, 2020
Reviewer #2 agreed
On 13 Jan, 2020
Reviewer #1 agreed
On 12 Jan, 2020
Reviewers invited
Invitations sent on 09 Jan, 2020
Editor assigned
On 06 Jan, 2020
Submission checks complete
On 05 Jan, 2020
Editor invited
On 05 Jan, 2020
No comments provided
Editorial decision: Major revision
On 07 Dec, 2019
Review #2 received
Received 27 Nov, 2019
Reviewer #2 agreed
On 12 Nov, 2019
Review #1 received
Received 10 Nov, 2019
Reviewer #1 agreed
On 09 Nov, 2019
Reviewers invited
Invitations sent on 08 Nov, 2019
Editor assigned
On 21 Oct, 2019
Submission checks complete
On 09 Oct, 2019
Editor invited
On 09 Oct, 2019
First submitted
On 03 Oct, 2019
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Subject Areas
Bioinformatics
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A new preprint design is coming!
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See the published version of this preprint at BMC Bioinformatics.
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
Danielle G. Lemay
USDA ARS Western Human Nutrition Research Center and University of California Davis Genome Center
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3318-0485
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 Bioinformatics.
No community comments so far
Editorial decision: Accept
On 17 Feb, 2020
Editor assigned
On 14 Feb, 2020
Submission checks complete
On 13 Feb, 2020
Editor invited
On 13 Feb, 2020
Version 2
Posted 08 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 07 Feb, 2020
Review #2 received
Received 27 Jan, 2020
Review #1 received
Received 14 Jan, 2020
Reviewer #2 agreed
On 13 Jan, 2020
Reviewer #1 agreed
On 12 Jan, 2020
Reviewers invited
Invitations sent on 09 Jan, 2020
Editor assigned
On 06 Jan, 2020
Submission checks complete
On 05 Jan, 2020
Editor invited
On 05 Jan, 2020
No comments provided
Editorial decision: Major revision
On 07 Dec, 2019
Review #2 received
Received 27 Nov, 2019
Reviewer #2 agreed
On 12 Nov, 2019
Review #1 received
Received 10 Nov, 2019
Reviewer #1 agreed
On 09 Nov, 2019
Reviewers invited
Invitations sent on 08 Nov, 2019
Editor assigned
On 21 Oct, 2019
Submission checks complete
On 09 Oct, 2019
Editor invited
On 09 Oct, 2019
First submitted
On 03 Oct, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX. Results Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene. Conclusions Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
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