Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites

Zheng Sun, Jiang Liu, Meng Zhang, Tong Wang, Shi Huang, Scott T. Weiss and Yang-Yu Liu ()
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Zheng Sun: Brigham and Women’s Hospital and Harvard Medical School
Jiang Liu: Qingdao OE Biotechnology Company Limited
Meng Zhang: Inner Mongolia Agricultural University
Tong Wang: Brigham and Women’s Hospital and Harvard Medical School
Shi Huang: The University of Hong Kong
Scott T. Weiss: Brigham and Women’s Hospital and Harvard Medical School
Yang-Yu Liu: Brigham and Women’s Hospital and Harvard Medical School

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles.

Date: 2023
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DOI: 10.1038/s41467-023-41099-8

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