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Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets

View ORCID ProfileDaniel M. Portik, View ORCID ProfileC. Titus Brown, View ORCID ProfileN. Tessa Pierce-Ward
doi: https://doi.org/10.1101/2022.01.31.478527
Daniel M. Portik
1Pacific Biosciences, 1305 O’Brien Dr, Menlo Park, California 93025 USA
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  • For correspondence: dportik@pacb.com
C. Titus Brown
2Department of Population Health and Reproduction, University of California Davis, Davis, California USA
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N. Tessa Pierce-Ward
2Department of Population Health and Reproduction, University of California Davis, Davis, California USA
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ABSTRACT

Background Long-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology (ONT) sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates.

Results Our results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (<2kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing.

Conclusions Our critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

Competing Interest Statement

DMP is an employee and shareholder of Pacific Biosciences of California, Inc.

Footnotes

  • We have added two new methods to the evaluation: mOTUs2 and sourmash. In addition, we clarify which methods are best considered taxonomic profilers versus taxonomic classifiers, provide more details surrounding relative abundance estimation, and improve the best practices section. All figures have been updated, and we include three new figures to improve visualization of precision, recall, and F-scores. The supplementary material has also been updated.

  • https://osf.io/bqtdu/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 15, 2022.
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Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets
Daniel M. Portik, C. Titus Brown, N. Tessa Pierce-Ward
bioRxiv 2022.01.31.478527; doi: https://doi.org/10.1101/2022.01.31.478527
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Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets
Daniel M. Portik, C. Titus Brown, N. Tessa Pierce-Ward
bioRxiv 2022.01.31.478527; doi: https://doi.org/10.1101/2022.01.31.478527

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