The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Fast, accurate error-correction of amplicon pyrosequences using Acacia
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Citations
Cites
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
Removing Noise From Pyrosequenced Amplicons
Quince, C., Lanzen, A., Davenport, R. J., & Turnbaugh, P. J. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-38 · Cited by 1,592
Accuracy and quality of massively parallel DNA pyrosequencing
Huse, S. M., Huber, J. A., Morrison, H. G., Sogin, M. L., & Welch, D. M. (2007) · Genome Biology · doi:10.1186/gb-2007-8-7-r143 · Cited by 1,282
Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions
Reeder, J., & Knight, R. (2010) · Nature Methods · doi:10.1038/nmeth0910-668b · Cited by 678
Characteristics of 454 pyrosequencing data—enabling realistic simulation with flowsim
Balzer, S., Malde, K., Lanzén, A., Sharma, A., & Jonassen, I. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq365 · Cited by 124
Cited by
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
Microbial diversity drives multifunctionality in terrestrial ecosystems
Delgado-Baquerizo, M., Maestre, F. T., Reich, P. B., Jeffries, T. C., Gaitan, J. J., Encinar, D., Berdugo, M., Campbell, C. D., & Singh, B. K. (2016) · Nature Communications · doi:10.1038/ncomms10541 · Cited by 2,624
Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage
Haroon, M. F., Hu, S., Shi, Y., Imelfort, M., Keller, J., Hugenholtz, P., Yuan, Z., & Tyson, G. W. (2013) · Nature · doi:10.1038/nature12375 · Cited by 1,345
Self-citationFungal community analysis by high‐throughput sequencing of amplified markers – a user's guide
Lindahl, B. D., Nilsson, R. H., Tedersoo, L., Abarenkov, K., Carlsen, T., Kjøller, R., Kõljalg, U., Pennanen, T., Rosendahl, S., Stenlid, J., et al. (2013) · New Phytologist · doi:10.1111/nph.12243 · Cited by 892
Fröhlich, E. E., Farzi, A., Mayerhofer, R., Reichmann, F., Jačan, A., Wagner, B., Zinser, E., Bordag, N., Magnes, C., Fröhlich, E., et al. (2016) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2016.02.020 · Cited by 720
Metadata
- Authors
- Bragg, L., Stone, G., Imelfort, M., Hugenholtz, P., & Tyson, G. W.
- Venue
- Nature Methods
- Published
- 2012-04-27
- Volume
- 9
- Issue
- 5
- Pages
- 425-426
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2084791457
- Cited by
- 337
- References
- 5
- FWCI
- 18.70
- Type
- journal article
- Language
- en
- Open access
- closed
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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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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
