Finding Phylogeny-aware and Biologically Meaningful Averages of Metagenomic Samples: L2UniFrac

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2023 Jun 30

PMID 37387190

Authors

Wei Wei

Andrew Millward

David Koslicki

Affiliations

Soon will be listed here.

Abstract

Motivation: Metagenomic samples have high spatiotemporal variability. Hence, it is useful to summarize and characterize the microbial makeup of a given environment in a way that is biologically reasonable and interpretable. The UniFrac metric has been a robust and widely used metric for measuring the variability between metagenomic samples. We propose that the characterization of metagenomic environments can be improved by finding the average, a.k.a. the barycenter, among the samples with respect to the UniFrac distance. However, it is possible that such a UniFrac-average includes negative entries, making it no longer a valid representation of a metagenomic community.

Results: To overcome this intrinsic issue, we propose a special version of the UniFrac metric, termed L2UniFrac, which inherits the phylogenetic nature of the traditional UniFrac and with respect to which one can easily compute the average, producing biologically meaningful environment-specific "representative samples." We demonstrate the usefulness of such representative samples as well as the extended usage of L2UniFrac in efficient clustering of metagenomic samples, and provide mathematical characterizations and proofs to the desired properties of L2UniFrac.

Availability And Implementation: A prototype implementation is provided at https://github.com/KoslickiLab/L2-UniFrac.git. All figures, data, and analysis can be reproduced at https://github.com/KoslickiLab/L2-UniFrac-Paper.

References

McClelland J, Koslicki D . EMDUniFrac: exact linear time computation of the UniFrac metric and identification of differentially abundant organisms. J Math Biol. 2018; 77(4):935-949. DOI: 10.1007/s00285-018-1235-9. View

Nunan N, Wu K, Young I, Crawford J, Ritz K . In situ spatial patterns of soil bacterial populations, mapped at multiple scales, in an arable soil. Microb Ecol. 2002; 44(4):296-305. DOI: 10.1007/s00248-002-2021-0. View

Moura V, Ribeiro I, Moriggi P, Capao A, Salles C, Bitati S . The influence of surface microbial diversity and succession on microbiologically influenced corrosion of steel in a simulated marine environment. Arch Microbiol. 2018; 200(10):1447-1456. DOI: 10.1007/s00203-018-1559-2. View

Wang L, Zhang F, Dong W, Wang C, Liang X, Suo L . A novel approach for the forensic diagnosis of drowning by microbiological analysis with next-generation sequencing and unweighted UniFrac-based PCoA. Int J Legal Med. 2020; 134(6):2149-2159. DOI: 10.1007/s00414-020-02358-1. View

Mocali S, Benedetti A . Exploring research frontiers in microbiology: the challenge of metagenomics in soil microbiology. Res Microbiol. 2010; 161(6):497-505. DOI: 10.1016/j.resmic.2010.04.010. View

McDonald D, Vazquez-Baeza Y, Koslicki D, McClelland J, Reeve N, Xu Z . Striped UniFrac: enabling microbiome analysis at unprecedented scale. Nat Methods. 2018; 15(11):847-848. PMC: 7250580. DOI: 10.1038/s41592-018-0187-8. View

Evans S, Matsen F . The phylogenetic Kantorovich-Rubinstein metric for environmental sequence samples. J R Stat Soc Series B Stat Methodol. 2012; 74(3):569-592. PMC: 3405733. DOI: 10.1111/j.1467-9868.2011.01018.x. View

Lozupone C, Knight R . UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005; 71(12):8228-35. PMC: 1317376. DOI: 10.1128/AEM.71.12.8228-8235.2005. View

Liang C, Tseng H, Chen H, Wang W, Chiu C, Chang J . Diversity and enterotype in gut bacterial community of adults in Taiwan. BMC Genomics. 2017; 18(Suppl 1):932. PMC: 5310273. DOI: 10.1186/s12864-016-3261-6. View

10.

Yachida S, Mizutani S, Shiroma H, Shiba S, Nakajima T, Sakamoto T . Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat Med. 2019; 25(6):968-976. DOI: 10.1038/s41591-019-0458-7. View

11.

Johnson K, Watson K, Dunbar R, Burnet P . Sociability in a non-captive macaque population is associated with beneficial gut bacteria. Front Microbiol. 2022; 13:1032495. PMC: 9691693. DOI: 10.3389/fmicb.2022.1032495. View

12.

Kang D, Adams J, Gregory A, Borody T, Chittick L, Fasano A . Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study. Microbiome. 2017; 5(1):10. PMC: 5264285. DOI: 10.1186/s40168-016-0225-7. View

13.

Banerjee J, Mishra N, Dhas Y . Metagenomics: A new horizon in cancer research. Meta Gene. 2015; 5:84-9. PMC: 4477109. DOI: 10.1016/j.mgene.2015.05.005. View

14.

Gonzalez A, Navas-Molina J, Kosciolek T, McDonald D, Vazquez-Baeza Y, Ackermann G . Qiita: rapid, web-enabled microbiome meta-analysis. Nat Methods. 2018; 15(10):796-798. PMC: 6235622. DOI: 10.1038/s41592-018-0141-9. View

15.

Kasmanas J, Bartholomaus A, Correa F, Tal T, Jehmlich N, Herberth G . HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes. Nucleic Acids Res. 2020; 49(D1):D743-D750. PMC: 7778935. DOI: 10.1093/nar/gkaa1031. View