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Pham et al. Microbiome Res Rep 2024;3:25 Microbiome Research
DOI: 10.20517/mrr.2024.01
Reports

Original Article Open Access

MetaBIDx: a new computational approach to bacteria
identification in microbiomes

Diem-Trang Pham , Vinhthuy Phan

Department of Computer Science, University of Memphis, Memphis, TN 38152, USA.
Correspondence to: Dr. Vinhthuy Phan, Department of Computer Science, University of Memphis, 3720 Alumni Avenue,
Memphis, TN 38152, USA. E-mail: vphan@memphis.edu

How to cite this article: Pham DT, Phan V. MetaBIDx: a new computational approach to bacteria identification in microbiomes.
Microbiome Res Rep 2024;3:25. https://dx.doi.org/10.20517/mrr.2024.01
Received: 7 Jan 2024 First Decision: 5 Feb 2024 Revised: 4 Mar 2024 Accepted: 25 Mar 2024 Published: 1 Apr 2024

Academic Editor: Gabriele Andrea Lugli Copy Editor: Dong-Li Li Production Editor: Dong-Li Li

Abstract
Objectives: This study introduces MetaBIDx, a computational method designed to enhance species prediction in
metagenomic environments. The method addresses the challenge of accurate species identification in complex
microbiomes, which is due to the large number of generated reads and the ever-expanding number of bacterial
genomes. Bacterial identification is essential for disease diagnosis and tracing outbreaks associated with microbial
infections.

Methods: MetaBIDx utilizes a modified Bloom filter for efficient indexing of reference genomes and incorporates a
novel strategy for reducing false positives by clustering species based on their genomic coverages by identified
reads. The approach was evaluated and compared with several well-established tools across various datasets.
Precision, recall, and F1-score were used to quantify the accuracy of species prediction.

Results: MetaBIDx demonstrated superior performance compared to other tools, especially in terms of precision
and F1-score. The application of clustering based on approximate coverages significantly improved precision in
species identification, effectively minimizing false positives. We further demonstrated that other methods can also
benefit from our approach to removing false positives by clustering species based on approximate coverages.
Conclusion: With a novel approach to reducing false positives and the effective use of a modified Bloom filter to
index species, MetaBIDx represents an advancement in metagenomic analysis. The findings suggest that the
proposed approach could also benefit other metagenomic tools, indicating its potential for broader application in

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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