A novel analysis method for paired-sample microbial ecology experiments
Abstract
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Many microbial ecology experiments use sequencing data to measure a community's response to an experimental treatment. In a common experimental design, two units, one control and one experimental, are sampled before and after the treatment is applied to the experimental unit. The four resulting samples contain information about the dynamics of organisms that respond to the treatment, but there are no analytical methods designed to extract exactly this type of information from this configuration of samples. Here we present an analytical method specifically designed to visualize and generate hypotheses about microbial community dynamics in experiments that have paired samples and few or no replicates. The method is based on the Poisson lognormal distribution, long studied in macroecology, which we found accurately models the abundance distribution of taxa counts from 16S rRNA surveys. To demonstrate the method's validity and potential, we analyzed an experiment that measured the effect of crude oil on ocean microbial communities in microcosm. Our methodmore »
- Authors:
-
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Univ. of Tennessee, Knoxville, TN (United States)
- Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Masdar Institute of Science and Technology, Abu Dhabi (United Arab Emirates)
- INRA (France)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1327714
- Alternate Identifier(s):
- OSTI ID: 1480721
- Grant/Contract Number:
- AC05-00OR22725; AC02-05CH11231
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 5; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; oils; microbial ecology; probability distribution; community ecology; crude oil; oceans; sequence databases; sequence alignment
Citation Formats
Olesen, Scott W., Vora, Suhani, Techtmann, Stephen M., Fortney, Julian L., Bastidas-Oyanedel, Juan R., Rodriguez, Jorge, Hazen, Terry C., Alm, Eric J., and Humbert, Jean -Francois. A novel analysis method for paired-sample microbial ecology experiments. United States: N. p., 2016.
Web. doi:10.1371/journal.pone.0154804.
Olesen, Scott W., Vora, Suhani, Techtmann, Stephen M., Fortney, Julian L., Bastidas-Oyanedel, Juan R., Rodriguez, Jorge, Hazen, Terry C., Alm, Eric J., & Humbert, Jean -Francois. A novel analysis method for paired-sample microbial ecology experiments. United States. https://doi.org/10.1371/journal.pone.0154804
Olesen, Scott W., Vora, Suhani, Techtmann, Stephen M., Fortney, Julian L., Bastidas-Oyanedel, Juan R., Rodriguez, Jorge, Hazen, Terry C., Alm, Eric J., and Humbert, Jean -Francois. 2016.
"A novel analysis method for paired-sample microbial ecology experiments". United States. https://doi.org/10.1371/journal.pone.0154804. https://www.osti.gov/servlets/purl/1327714.
@article{osti_1327714,
title = {A novel analysis method for paired-sample microbial ecology experiments},
author = {Olesen, Scott W. and Vora, Suhani and Techtmann, Stephen M. and Fortney, Julian L. and Bastidas-Oyanedel, Juan R. and Rodriguez, Jorge and Hazen, Terry C. and Alm, Eric J. and Humbert, Jean -Francois},
abstractNote = {This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Many microbial ecology experiments use sequencing data to measure a community's response to an experimental treatment. In a common experimental design, two units, one control and one experimental, are sampled before and after the treatment is applied to the experimental unit. The four resulting samples contain information about the dynamics of organisms that respond to the treatment, but there are no analytical methods designed to extract exactly this type of information from this configuration of samples. Here we present an analytical method specifically designed to visualize and generate hypotheses about microbial community dynamics in experiments that have paired samples and few or no replicates. The method is based on the Poisson lognormal distribution, long studied in macroecology, which we found accurately models the abundance distribution of taxa counts from 16S rRNA surveys. To demonstrate the method's validity and potential, we analyzed an experiment that measured the effect of crude oil on ocean microbial communities in microcosm. Our method identified known oil degraders as well as two clades, Maricurvus and Rhodobacteraceae, that responded to amendment with oil but do not include known oil degraders. Our approach is sensitive to organisms that increased in abundance only in the experimental unit but less sensitive to organisms that increased in both control and experimental units, thus mitigating the role of "bottle effects".},
doi = {10.1371/journal.pone.0154804},
url = {https://www.osti.gov/biblio/1327714},
journal = {PLoS ONE},
issn = {1932-6203},
number = 5,
volume = 11,
place = {United States},
year = {Fri May 06 00:00:00 EDT 2016},
month = {Fri May 06 00:00:00 EDT 2016}
}
Web of Science
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