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STAMP: statistical analysis of taxonomic and functional profiles
Open accessAbstract
Summary: STAMP is a graphical software package that provides statistical hypothesis tests and exploratory plots for analysing taxonomic and functional profiles. It supports tests for comparing pairs of samples or samples organized into two or more treatment groups. Effect sizes and confidence intervals are provided to allow critical assessment of the biological relevancy of test results. A user-friendly graphical interface permits easy exploration of statistical results and generation of publication-quality plots. Availability and implementation: STAMP is licensed under the GNU GPL. Python source code and binaries are available from our website at: http://kiwi.cs.dal.ca/Software/STAMP Contact: donovan.parks@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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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
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
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Hill‐Burns, E. M., Debelius, J. W., Morton, J. T., Wissemann, W. T., Lewis, M. R., Wallen, Z. D., Peddada, S. D., Factor, S. A., Molho, E., Zabetian, C. P., et al. (2017) · Movement Disorders · doi:10.1002/mds.26942 · Cited by 874
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Clemente, J. C., Pehrsson, E. C., Blaser, M. J., Sandhu, K., Gao, Z., Wang, B., Magris, M., Hidalgo, G., Contreras, M., Noya-Alarcón, Ó., et al. (2015) · Science Advances · doi:10.1126/sciadv.1500183 · Cited by 851
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Org, E., Mehrabian, M., Parks, B. W., Shipkova, P., Liu, X., Drake, T. A., & Lusis, A. J. (2016) · Gut Microbes · doi:10.1080/19490976.2016.1203502 · Cited by 796
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Segal, L. N., Clemente, J. C., Tsay, J.-C. J., Koralov, S. B., Keller, B. C., Wu, B. G., Li, Y., Shen, N., Ghedin, E., Morris, A., et al. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.31 · Cited by 685
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He, Y., Kosciolek, T., Tang, J., Zhou, Y., Li, Z., Ma, X., Zhu, Q., Yuan, N., Yuan, L., Li, C., et al. (2018) · European Psychiatry · doi:10.1016/j.eurpsy.2018.05.011 · Cited by 123
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Lei, Y., Tang, L., Chen, Q., Wu, L., He, W., Tu, D., Wang, S., Chen, Y., Liu, S., Xie, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34671-1 · Cited by 104
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Zhou, Z., Li, Z., Chen, K., Chen, Z., Zeng, X., Yu, H., Guo, S., Shangguan, Y., Chen, Q., Fan, H., et al. (2021) · SOIL · doi:10.5194/soil-7-595-2021 · Cited by 39
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Xia, Y., Peng, S., Yu, J., Wang, Y., Ye, Y., Liu, M., Shang, L., Cui, X., Wang, P., & Ding, Z. (2026) · Journal of Oral Microbiology · doi:10.1080/20002297.2026.2714622
Metadata
- Authors
- Parks, D. H., Tyson, G. W., Hugenholtz, P., & Beiko, R. G.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-07-23
- Volume
- 30
- Issue
- 21
- Pages
- 3123-3124
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2153109441
- Cited by
- 4,191
- References
- 2
- FWCI
- 43.32
- Type
- journal article
- Language
- en
- Open access
- bronze
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