The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
PICRUSt2 for prediction of metagenome functions
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In the citing paper
“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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Citations
Cites
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
Flagged first, then by citations
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
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Cited by
Sorted by citations
Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions
Narunsky-Haziza, L., Sepich-Poore, G. D., Livyatan, I., Asraf, O., Martino, C., Nejman, D., Gavert, N., Stajich, J. E., Amit, G., González, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.005 · Cited by 538
“Functional characterization remains difficult due to low fungal abundances and a paucity of published fungal genomes that limit gene content inference from amplicon data, as possible for bacteria (Douglas et al., 2020).”
BackgroundNext-generation sequencing: insights to advance clinical investigations of the microbiome
Wensel, C. R., Pluznick, J. L., Salzberg, S. L., & Sears, C. L. (2022) · Journal of Clinical Investigation · doi:10.1172/jci154944 · Cited by 437
Huang, B., Chau, S. W. H., Liu, Y., Chan, J. W. Y., Wang, J., Ma, S. L., Zhang, J., Chan, P. K. S., Yeoh, Y. K., Chen, Z., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38248-4 · Cited by 187
Intratumor microbiome features reveal antitumor potentials of intrahepatic cholangiocarcinoma
Chai, X., Wang, J., Li, H., Gao, C., Li, S., Wei, C., Huang, J., Tian, Y., Yuan, J., Lu, J., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2022.2156255 · Cited by 152
Cheng, Y., Tan, G., Zhu, Q., Wang, C., Ruan, G., Ying, S., Qie, J., Hu, X., Xiao, Z., Xu, F., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2284247 · Cited by 147
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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