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

Steven W Culman1, Robert Bukowski, Hugh G Gauch

  • 1Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA. swculman@ucdavis.edu

BMC Bioinformatics
|June 9, 2009
PubMed
Summary

We developed T-REX, a free online tool to simplify the analysis of microbial community terminal restriction fragment length polymorphism (T-RFLP) data. This software addresses common challenges, offering a more robust and efficient approach for researchers.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Microbial community analysis using techniques like terminal restriction fragment length polymorphism (T-RFLP) is increasingly popular.
  • Current data processing for T-RFLP is time-intensive, prone to errors, and can introduce variability.
  • Existing methods present obstacles to efficient and accurate analysis of microbial fingerprinting data.

Purpose of the Study:

  • To develop T-REX, a free, online software solution for processing and analyzing T-RFLP data.
  • To overcome the limitations of manual data processing in microbial community fingerprinting.
  • To provide researchers with an integrated and robust tool for T-RFLP analysis.

Main Methods:

  • Developed T-REX, a free, platform-independent online software.

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  • Implemented features for raw data labeling, noise reduction (baseline threshold), and fragment alignment (T-RF binning).
  • Enabled construction and analysis of two-way data matrices, including variance distribution and AMMI model analysis.

    • Main Results:

    • T-REX successfully processed T-RFLP data from a multiple-factorial study.
    • The software facilitated sample attribute labeling and identification of true peaks over noise.
    • T-REX enabled T-RF alignment across samples and analysis of data matrices using various statistical approaches, including AMMI.

    Conclusions:

    • T-REX offers a free, integrated, and rapid solution for T-RFLP data analysis.
    • The software enhances the robustness of microbial community fingerprinting data interpretation.
    • T-REX is a valuable, platform-independent tool for the research community.