4.7 Article

Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy

期刊

MSYSTEMS
卷 7, 期 2, 页码 -

出版社

AMER SOC MICROBIOLOGY
DOI: 10.1128/msystems.00167-22

关键词

operational genomic unit; taxonomy independent; reference phylogeny; UniFrac; supervised learning; metagenomics

资金

  1. Arizona State University
  2. Sloan Foundation [G-2017-9838]
  3. IBM Research AI through the AI Horizons Network-AI for Healthy Living [A1770534]
  4. DARPA JUMP/CRISP
  5. NIH [P30DK120515, DP1AT010885, U19AG063744, U24CA248454]
  6. Emerald Foundation Distinguished Investigator Award
  7. Crohn's and Colitis Foundation [675191]
  8. NSF [RAPID 2038509]
  9. IBM Research AI through the AI Horizons Network
  10. UC San Diego Center for Microbiome Innovation
  11. National Institutes of Health [F30 CA243480]
  12. Emil Aaltonen Foundation
  13. Finnish Medical Foundation
  14. Finnish Foundation for Cardiovascular Disease
  15. Academy of Finland [321351, 295741]
  16. Finnish Foundation for Cardiovascular Research
  17. NIH/NIGMS [IRACDA K12 GM068524]
  18. San Diego Supercomputer Center through Extreme Science and Engineering Discovery Environment (XSEDE) [BIO150043]

向作者/读者索取更多资源

The operational genomic unit (OGU) method directly exploits sequence alignment hits to individual reference genomes for assessing microbial community diversity and relevance to environmental factors, offering maximal resolution of community composition and supporting phylogenetic methods. The method outperforms taxonomic unit-based analyses in informing biologically relevant insights, as demonstrated in real-world case studies.
We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

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