Tools for T-RFLP data analysis using Excel

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 compariso...

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Veröffentlicht in:BMC bioinformatics Jg. 15; H. 1; S. 361
Hauptverfasser: Fredriksson, Nils Johan, Hermansson, Malte, Wilén, Britt-Marie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 08.11.2014
BioMed Central Ltd
Springer Nature B.V
Schlagworte:
Algorithms
Alignment
Analysis
Automation
Bacteria - genetics
Bioinformatics
Biomedical and Life Sciences
Colleges & universities
Community composition
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data processing
Deoxyribonucleic acid
DNA
DNA-fingerprinting
Enzymes
Genes
Genetic polymorphisms
Life Sciences
Manuals
Mathematical analysis
Microarrays
Microbial ecology
Microorganisms
Molecular Biology
Molekylärbiologi
Noise
Noise threshold
Polymorphism, Restriction Fragment Length
Programming languages
Sequence Alignment - methods
Sequence analysis (applications)
Sequence Analysis, DNA - methods
Software
Statistical methods
Terminal restriction fragment length polymorphism data analysis
DNA-fingerprinting
Microbial ecology
Terminal restriction fragment length polymorphism data analysis
ISSN:1471-2105, 1471-2105
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Zusammenfassung: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-014-0361-7