TY - JOUR
T1 - T-REX
T2 - Software for the processing and analysis of T-RFLP data
AU - Culman, Steven W.
AU - Bukowski, Robert
AU - Gauch, Hugh G.
AU - Cadillo-Quiroz, Hinsby
AU - Buckley, Daniel H.
N1 - Funding Information:
The authors wish to acknowledge Noah Spies for contributing to ideas and developments of an earlier version of this software. Funding for this software development was provided by the National Science Foundation (IGERT Fellowship DGE 0221658) and by the Microsoft Corporation.
PY - 2009/6/6
Y1 - 2009/6/6
N2 - Background: Despite increasing popularity and improvements in terminal restriction fragment length polymorphism (T-RFLP) and other microbial community fingerprinting techniques, there are still numerous obstacles that hamper the analysis of these datasets. Many steps are required to process raw data into a format ready for analysis and interpretation. These steps can be time-intensive, error-prone, and can introduce unwanted variability into the analysis. Accordingly, we developed T-REX, free, online software for the processing and analysis of T-RFLP data. Results: Analysis of T-RFLP data generated from a multiple-factorial study was performed with T-REX. With this software, we were able to i) label raw data with attributes related to the experimental design of the samples, ii) determine a baseline threshold for identification of true peaks over noise, iii) align terminal restriction fragments (T-RFs) in all samples (i.e., bin T-RFs), iv) construct a two-way data matrix from labeled data and process the matrix in a variety of ways, v) produce several measures of data matrix complexity, including the distribution of variance between main and interaction effects and sample heterogeneity, and vi) analyze a data matrix with the additive main effects and multiplicative interaction (AMMI) model. Conclusion: T-REX provides a free, platform-independent tool to the research community that allows for an integrated, rapid, and more robust analysis of T-RFLP data.
AB - Background: Despite increasing popularity and improvements in terminal restriction fragment length polymorphism (T-RFLP) and other microbial community fingerprinting techniques, there are still numerous obstacles that hamper the analysis of these datasets. Many steps are required to process raw data into a format ready for analysis and interpretation. These steps can be time-intensive, error-prone, and can introduce unwanted variability into the analysis. Accordingly, we developed T-REX, free, online software for the processing and analysis of T-RFLP data. Results: Analysis of T-RFLP data generated from a multiple-factorial study was performed with T-REX. With this software, we were able to i) label raw data with attributes related to the experimental design of the samples, ii) determine a baseline threshold for identification of true peaks over noise, iii) align terminal restriction fragments (T-RFs) in all samples (i.e., bin T-RFs), iv) construct a two-way data matrix from labeled data and process the matrix in a variety of ways, v) produce several measures of data matrix complexity, including the distribution of variance between main and interaction effects and sample heterogeneity, and vi) analyze a data matrix with the additive main effects and multiplicative interaction (AMMI) model. Conclusion: T-REX provides a free, platform-independent tool to the research community that allows for an integrated, rapid, and more robust analysis of T-RFLP data.
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U2 - 10.1186/1471-2105-10-171
DO - 10.1186/1471-2105-10-171
M3 - Article
C2 - 19500385
AN - SCOPUS:67649610667
SN - 1471-2105
VL - 10
JO - BMC bioinformatics
JF - BMC bioinformatics
M1 - 171
ER -