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Sökning: LAR1:gu > Tidskriftsartikel > Chalmers tekniska högskola > Hermansson Malte 1954 > Tools for T-RFLP da...

Tools for T-RFLP data analysis using Excel

Fredriksson, Johan, 1979 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för biomedicin, avdelningen för medicinsk kemi och cellbiologi,Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology,University of Gothenburg,Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
Hermansson, Malte, 1954 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kemi och molekylärbiologi,Department of Chemistry and Molecular Biology,University of Gothenburg,Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
Wilen, Britt-Marie, 1966 (författare)
Chalmers tekniska högskola,Chalmers University of Technology,Department of Civil and Environmental Engineering, Water Environment Technology, Chalmers University of Technology, Gothenburg, Sweden
 (creator_code:org_t)
2014-11-08
2014
Engelska.
Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 15
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

NATURVETENSKAP  -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
NATURVETENSKAP  -- Biologi -- Mikrobiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Microbiology (hsv//eng)

Nyckelord

Terminal restriction fragment length polymorphism data analysis
Microbial ecology
Microbial ecology

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