The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Piphillin: Improved Prediction of Metagenomic Content by Direct Inference from Human Microbiomes
Open accessAbstract
Functional analysis of a clinical microbiome facilitates the elucidation of mechanisms by which microbiome perturbation can cause a phenotypic change in the patient. The direct approach for the analysis of the functional capacity of the microbiome is via shotgun metagenomics. An inexpensive method to estimate the functional capacity of a microbial community is through collecting 16S rRNA gene profiles then indirectly inferring the abundance of functional genes. This inference approach has been implemented in the PICRUSt and Tax4Fun software tools. However, those tools have important limitations since they rely on outdated functional databases and uncertain phylogenetic trees and require very specific data pre-processing protocols. Here we introduce Piphillin, a straightforward algorithm independent of any proposed phylogenetic tree, leveraging contemporary functional databases and not obliged to any singular data pre-processing protocol. When all three inference tools were evaluated against actual shotgun metagenomics, Piphillin was superior in predicting gene composition in human clinical samples compared to both PICRUSt and Tax4Fun (p<0.01 and p<0.001, respectively) and Piphillin's ability to predict disease associations with specific gene orthologs exhibited a 15% increase in balanced accuracy compared to PICRUSt. From laboratory animal samples, no performance advantage was observed for any one of the tools over the others and for environmental samples all produced unsatisfactory predictions. Our results demonstrate that functional inference using the direct method implemented in Piphillin is preferable for clinical biospecimens. Piphillin is publicly available for academic use at http://secondgenome.com/Piphillin.
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
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DADA2: High–resolution sample inference from Illumina amplicon data
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016) · Nature Methods · doi:10.1038/nmeth.3869 · Cited by 35,698
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
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Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
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Self-citationPredictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2676 · Cited by 9,116
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Cited by
PICRUSt2 for prediction of metagenome functions
Douglas, G. M., Maffei, V. J., Zaneveld, J. R., Yurgel, S. N., Brown, J. R., Taylor, C. M., Huttenhower, C., & Langille, M. G. I. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0548-6 · Cited by 6,709
Cryan, J. F., O'Riordan, K. J., Cowan, C. S. M., Sandhu, K. V., Bastiaanssen, T. F. S., Boehme, M., Codagnone, M. G., Cussotto, S., Fulling, C., Golubeva, A. V., et al. (2019) · Physiological Reviews · doi:10.1152/physrev.00018.2018 · Cited by 5,286
Evaluating the Information Content of Shallow Shotgun Metagenomics
Hillmann, B., Al-Ghalith, G. A., Shields-Cutler, R. R., Zhu, Q., Gohl, D. M., Beckman, K. B., Knight, R., & Knights, D. (2018) · mSystems · doi:10.1128/msystems.00069-18 · Cited by 474
“than the correlations of functional profiles predicted from 16S sequencing, which typically has 80% to 90% correlation with the directly observed functions (7, 8).”
BackgroundResultInfluentialExploring the emerging role of the microbiome in cancer immunotherapy
Fessler, J., Matson, V., & Gajewski, T. F. (2019) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-019-0574-4 · Cited by 319
Microbiota from young mice counteracts selective age-associated behavioral deficits
Boehme, M., Guzzetta, K. E., Bastiaanssen, T. F. S., van de Wouw, M., Moloney, G. M., Gual-Grau, A., Spichak, S., Olavarría-Ramírez, L., Fitzgerald, P., Morillas, E., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00093-9 · Cited by 313
Metadata
- Authors
- Iwai, S., Weinmaier, T., Schmidt, B. L., Albertson, D. G., Poloso, N. J., Dabbagh, K., & DeSantis, T. Z.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-11-07
- Volume
- 11
- Issue
- 11
- Pages
- e0166104
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2555180552
- Cited by
- 333
- References
- 8
- FWCI
- 12.34
- Type
- journal article
- Language
- en
- Open access
- gold
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- Iwai, S., Weinmaier, T., Schmidt, B. L., Albertson, D. G., Poloso, N. J., Dabbagh, K., & DeSantis, T. Z. (2016). Piphillin: Improved Prediction of Metagenomic Content by Direct Inference from Human Microbiomes. PLOS ONE. https://doi.org/10.1371/journal.pone.0166104
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