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PICRUSt2 for prediction of metagenome functions
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“KO analysis showed a significant difference between the two groups, with most of the functional genes under-represented in the DF group.”
From Saliva microbiome alterations in dental fluorosis population (2023) · cites this paper as [33]
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Predictive 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
Self-citationCaspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395
Efficient comparative phylogenetics on large trees
Louca, S., & Doebeli, M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx701 · Cited by 564
Research works printed but not linked: 12
- Iwai, S. et al. PLoS One 11, e0166104 (2016). · DOI
- Jun, S. R. et al. BMC Res. Notes 8, 479 (2015). · DOI
- Aßhauer, K. P., Wemheuer, B., Daniel, R. & Meinicke, P. Bioinformatics 31, 2882–2884 (2015). · DOI
- Wemheuer, F. et al. Preprint at bioRxiv https://doi.org/10.1101/490037 (2018). · DOI
- DeSantis, T. Z. et al. Appl. Environ. Microbiol. 72, 5069–5072 (2006). · DOI · Paper page
- Markowitz, V. M. et al. Nucleic Acids Res. 40 (D1), D115–D122 (2012). · DOI
- Barbera, P. et al. Syst. Biol. 68, 365–369 (2019). · DOI
- Czech, L. & Stamatakis, A. PLoS One 14, e0217050 (2019). · DOI
- Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. Nucleic Acids Res. 40 (D1), D109–D114 (2012). · DOI · Paper page
- Narayan, N. R. et al. BMC Genomics 21, 56 (2020). · DOI
- Franzosa, E. A. et al. Nat. Methods 15, 962–968 (2018). · DOI · Paper page
- Chen, I.-M. A. et al. PLoS One 8, e54859 (2013). · DOI
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Metadata
- Authors
- Douglas, G. M., Maffei, V. J., Zaneveld, J. R., Yurgel, S. N., Brown, J. R., Taylor, C. M., Huttenhower, C., & Langille, M. G. I.
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2020-06
- Volume
- 38
- Issue
- 6
- Pages
- 685-688
- ISSN
- 1087-0156
- 1546-1696
- PMC
- PMC7365738
- Cited by
- 6,709
- References
- 3
- Language
- en
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