Software

T-REX: software for the processing and analysis of T-RFLP data

Steven W Culman^1,6 , Robert Bukowski² , Hugh G Gauch³ , Hinsby Cadillo-Quiroz⁴ and Daniel H Buckley⁵

¹  515 Bradfield Hall, Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA

²  620 Rhodes Hall, Computational Biology Service Unit, Cornell University, Ithaca, NY, USA

³  519 Bradfield Hall, Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA

⁴  Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana IL, USA

⁵  705 Bradfield Hall, Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA

⁶  3150 Plant and Environmental Science, Department of Land, Air and Water Resources, One Shields Avenue, University of California, Davis, CA 95616, USA

author email corresponding author email

BMC Bioinformatics 2009, 10:171doi:10.1186/1471-2105-10-171

Published:	6 June 2009

Abstract

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.