The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes
Open accessAbstract
A common biological pathway reconstruction approach -- as implemented by many automatic biological pathway services (such as the KAAS and RAST servers) and the functional annotation of metagenomic sequences -- starts with the identification of protein functions or families (e.g., KO families for the KEGG database and the FIG families for the SEED database) in the query sequences, followed by a direct mapping of the identified protein families onto pathways. Given a predicted patchwork of individual biochemical steps, some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences. Commonly, and straightforwardly, a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found. We report, however, that this naïve mapping approach leads to an inflated estimate of biological pathways, and thus overestimates the functional diversity of the sample from which the DNA sequences are derived. We developed a parsimony approach, called MinPath (Minimal set of Pathways), for biological pathway reconstructions using protein family predictions, which yields a more conservative, yet more faithful, estimation of the biological pathways for a query dataset. MinPath identified far fewer pathways for the genomes collected in the KEGG database -- as compared to the naïve mapping approach -- eliminating some obviously spurious pathway annotations. Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities.
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Cites
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Cited by
Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment
Morgan, X. C., Tickle, T. L., Sokol, H., Gevers, D., Devaney, K. L., Ward, D. V., Reyes, J. A., Shah, S. A., LeLeiko, N., Snapper, S. B., et al. (2012) · Genome Biology · doi:10.1186/gb-2012-13-9-r79 · Cited by 2,713
The human tumor microbiome is composed of tumor type–specific intracellular bacteria
Nejman, D., Livyatan, I., Fuks, G., Gavert, N., Zwang, Y., Geller, L. T., Rotter-Maskowitz, A., Weiser, R., Mallel, G., Gigi, E., et al. (2020) · Science · doi:10.1126/science.aay9189 · Cited by 2,464
Species-level functional profiling of metagenomes and metatranscriptomes
Franzosa, E. A., McIver, L. J., Rahnavard, G., Thompson, L. R., Schirmer, M., Weingart, G., Lipson, K. S., Knight, R., Caporaso, J. G., Segata, N., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0176-y · Cited by 1,693
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Current challenges and best-practice protocols for microbiome analysis
Bharti, R., & Grimm, D. G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbz155 · Cited by 592
Metadata
- Authors
- Ye, Y., & Doak, T. G.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2009-08-14
- Volume
- 5
- Issue
- 8
- Pages
- e1000465
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2077631777
- Cited by
- 686
- References
- 24
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Open access
- gold
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