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Tools for T-RFLP data analysis using Excel
Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID iD: 0000-0002-5763-6777
Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
Department of Civil and Environmental Engineering, Water Environment Technology, Chalmers University of Technology, Gothenburg, Sweden.ORCID iD: 0000-0001-6155-7759
2014 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 15, article id 361Article in journal (Refereed) Published
Abstract [en]

Background: Terminal restriction fragment length polymorphism (T-RFLP) analysis is a DNA-fingerprinting method that can be used for comparisons of the microbial community composition in a large number of samples. There is no consensus on how T-RFLP data should be treated and analyzed before comparisons between samples are made, and several different approaches have been proposed in the literature. The analysis of T-RFLP data can be cumbersome and time-consuming, and for large datasets manual data analysis is not feasible. The currently available tools for automated T-RFLP analysis, although valuable, offer little flexibility, and few, if any, options regarding what methods to use. To enable comparisons and combinations of different data treatment methods an analysis template and an extensive collection of macros for T-RFLP data analysis using Microsoft Excel were developed.

Results: The Tools for T-RFLP data analysis template provides procedures for the analysis of large T-RFLP datasets including application of a noise baseline threshold and setting of the analysis range, normalization and alignment of replicate profiles, generation of consensus profiles, normalization and alignment of consensus profiles and final analysis of the samples including calculation of association coefficients and diversity index. The procedures are designed so that in all analysis steps, from the initial preparation of the data to the final comparison of the samples, there are various different options available. The parameters regarding analysis range, noise baseline, T-RF alignment and generation of consensus profiles are all given by the user and several different methods are available for normalization of the T-RF profiles. In each step, the user can also choose to base the calculations on either peak height data or peak area data.

Conclusions: The Tools for T-RFLP data analysis template enables an objective and flexible analysis of large T-RFLP datasets in a widely used spreadsheet application.

Place, publisher, year, edition, pages
BioMed Central, 2014. Vol. 15, article id 361
Keywords [en]
Terminal restriction fragment length polymorphism data analysis, Microbial ecology, DNA-fingerprinting
National Category
Microbiology
Identifiers
URN: urn:nbn:se:oru:diva-78922DOI: 10.1186/s12859-014-0361-7ISI: 000344894900001PubMedID: 25388093Scopus ID: 2-s2.0-84920851210OAI: oai:DiVA.org:oru-78922DiVA, id: diva2:1383911
Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-01-29Bibliographically approved

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