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Research article
William S Pearman
Massey University Institute of Natural and Mathematical Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7265-8499
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The first step in understanding ecological community diversity and dynamics is quantifying community membership. An increasingly common method for doing so is through metagenomics. Because of the rapidly increasing popularity of this approach, a large number of computational tools and pipelines are available for analysing metagenomic data. However, the majority of these tools have been designed and benchmarked using highly accurate short read data (i.e. Illumina), with few studies benchmarking classification accuracy for long error-prone reads (PacBio or Oxford Nanopore). In addition, few tools have been benchmarked for non-microbial communities.
Results: Here we compare simulated long reads from Oxford Nanopore and Pacific Biosciences with high accuracy Illumina read sets to systematically investigate the effects of sequence length and taxon type on classification accuracy for metagenomic data from both microbial and non-microbial communities. We show that very generally, classification accuracy is far lower for non-microbial communities, even at low taxonomic resolution (e.g. family rather than genus). We then show that for two popular taxonomic classifiers, long reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities.
Conclusions: This work provides insight on the expected accuracy for metagenomic analyses for different taxonomic groups, and establishes the point at which read length becomes more important than error rate for assigning the correct taxon.
Keywords
metagenomics, Nanopore, Illumina, long read, community composition
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CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
Version 4
Posted 13 May, 2020
No community comments so far
Editorial decision: Accept
On 29 Apr, 2020
Submission checks complete
On 11 Jun, 2019
No comments provided
Editorial decision: Minor revision
On 24 Apr, 2020
Reviewer #1 agreed
On 20 Apr, 2020
Review #1 received
Received 20 Apr, 2020
Reviewers invited
Invitations sent on 15 Apr, 2020
Editor assigned
On 05 Apr, 2020
Submission checks complete
On 04 Apr, 2020
Editor invited
On 04 Apr, 2020
No comments provided
Editorial decision: Major revision
On 17 Mar, 2020
Review #1 received
Received 10 Feb, 2020
Reviewer #1 agreed
On 04 Feb, 2020
Editor assigned
On 09 Dec, 2019
Reviewers invited
Invitations sent on 09 Dec, 2019
Submission checks complete
On 08 Dec, 2019
Editor invited
On 08 Dec, 2019
No comments provided
Editorial decision: Major revision
On 30 Oct, 2019
Review #2 received
Received 06 Oct, 2019
Review #1 received
Received 06 Oct, 2019
Reviewer #1 agreed
On 24 Sep, 2019
Reviewer #2 agreed
On 24 Sep, 2019
Reviewers invited
Invitations sent on 13 Jul, 2019
Editor assigned
On 25 Jun, 2019
Submission checks complete
On 11 Jun, 2019
Editor invited
On 11 Jun, 2019
First submitted
On 05 Jun, 2019
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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
William S Pearman
Massey University Institute of Natural and Mathematical Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7265-8499
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.
Version 4
Posted 13 May, 2020
No community comments so far
Editorial decision: Accept
On 29 Apr, 2020
Submission checks complete
On 11 Jun, 2019
No comments provided
Editorial decision: Minor revision
On 24 Apr, 2020
Reviewer #1 agreed
On 20 Apr, 2020
Review #1 received
Received 20 Apr, 2020
Reviewers invited
Invitations sent on 15 Apr, 2020
Editor assigned
On 05 Apr, 2020
Submission checks complete
On 04 Apr, 2020
Editor invited
On 04 Apr, 2020
No comments provided
Editorial decision: Major revision
On 17 Mar, 2020
Review #1 received
Received 10 Feb, 2020
Reviewer #1 agreed
On 04 Feb, 2020
Editor assigned
On 09 Dec, 2019
Reviewers invited
Invitations sent on 09 Dec, 2019
Submission checks complete
On 08 Dec, 2019
Editor invited
On 08 Dec, 2019
No comments provided
Editorial decision: Major revision
On 30 Oct, 2019
Review #2 received
Received 06 Oct, 2019
Review #1 received
Received 06 Oct, 2019
Reviewer #1 agreed
On 24 Sep, 2019
Reviewer #2 agreed
On 24 Sep, 2019
Reviewers invited
Invitations sent on 13 Jul, 2019
Editor assigned
On 25 Jun, 2019
Submission checks complete
On 11 Jun, 2019
Editor invited
On 11 Jun, 2019
First submitted
On 05 Jun, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background: The first step in understanding ecological community diversity and dynamics is quantifying community membership. An increasingly common method for doing so is through metagenomics. Because of the rapidly increasing popularity of this approach, a large number of computational tools and pipelines are available for analysing metagenomic data. However, the majority of these tools have been designed and benchmarked using highly accurate short read data (i.e. Illumina), with few studies benchmarking classification accuracy for long error-prone reads (PacBio or Oxford Nanopore). In addition, few tools have been benchmarked for non-microbial communities.
Results: Here we compare simulated long reads from Oxford Nanopore and Pacific Biosciences with high accuracy Illumina read sets to systematically investigate the effects of sequence length and taxon type on classification accuracy for metagenomic data from both microbial and non-microbial communities. We show that very generally, classification accuracy is far lower for non-microbial communities, even at low taxonomic resolution (e.g. family rather than genus). We then show that for two popular taxonomic classifiers, long reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities.
Conclusions: This work provides insight on the expected accuracy for metagenomic analyses for different taxonomic groups, and establishes the point at which read length becomes more important than error rate for assigning the correct taxon.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
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