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Tools for T-RFLP data analysis using Excel.

Fredriksson NJ, Hermansson M, Wilén BM - BMC Bioinformatics (2014)

Bottom Line: 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.In each step, the user can also choose to base the calculations on either peak height data or peak area data.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. johan.fredriksson@gu.se.

ABSTRACT

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.

Show MeSH
Number of T-RFs in the consensus profiles. T-RFs were included in the consensus profiles if they were present in one (squares) or both (diamonds) of the two replicates.
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Fig3: Number of T-RFs in the consensus profiles. T-RFs were included in the consensus profiles if they were present in one (squares) or both (diamonds) of the two replicates.

Mentions: Consensus profiles generated by only considering T-RFs that were present in both of the two replicates, as suggested by Dunbar et al. [6], had fewer T-RFs than the consensus profiles generated considering all T-RFs in both replicate profiles, as in the program T-REX (Figure 3).Figure 3


Tools for T-RFLP data analysis using Excel.

Fredriksson NJ, Hermansson M, Wilén BM - BMC Bioinformatics (2014)

Number of T-RFs in the consensus profiles. T-RFs were included in the consensus profiles if they were present in one (squares) or both (diamonds) of the two replicates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4228162&req=5

Fig3: Number of T-RFs in the consensus profiles. T-RFs were included in the consensus profiles if they were present in one (squares) or both (diamonds) of the two replicates.
Mentions: Consensus profiles generated by only considering T-RFs that were present in both of the two replicates, as suggested by Dunbar et al. [6], had fewer T-RFs than the consensus profiles generated considering all T-RFs in both replicate profiles, as in the program T-REX (Figure 3).Figure 3

Bottom Line: 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.In each step, the user can also choose to base the calculations on either peak height data or peak area data.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. johan.fredriksson@gu.se.

ABSTRACT

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.

Show MeSH