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Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Published on: July 17, 2021

Application of a clustering-based peak alignment algorithm to analyze various DNA fingerprinting data.

Satoshi Ishii¹, Koji Kadota, Keishi Senoo

¹Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan. anaerobe@mail.ecc.u-tokyo.ac.jp

Journal of Microbiological Methods

|July 21, 2009

Summary

This study introduces an R program for aligning DNA fingerprinting data, improving analysis for techniques like ARDRA and rep-PCR. The new algorithm shows high similarity to existing software, enabling advanced statistical analysis in microbiology.

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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

DNA fingerprinting techniques like amplified ribosomal DNA restriction analysis (ARDRA), repetitive extragenic palindromic PCR (rep-PCR), ribosomal intergenic spacer analysis (RISA), and denaturing gradient gel electrophoresis (DGGE) are crucial in microbiology.
A significant challenge in DNA fingerprinting is the accurate alignment of complex, multi-peak datasets.

Purpose of the Study:

To develop and present an R program implementing a clustering-based peak alignment algorithm.
To demonstrate the program's utility in analyzing diverse DNA fingerprinting data (ARDRA, rep-PCR, RISA, DGGE).

Main Methods:

Development of a novel R program utilizing a clustering-based algorithm for DNA fingerprinting peak alignment.
Application and validation of the R program against established software (BioNumerics) using ARDRA, rep-PCR, RISA, and DGGE datasets.

Main Results:

The R program's clustering-based algorithm effectively aligns DNA fingerprinting peak sets.
Results generated by the R program demonstrated high similarity to those obtained using BioNumerics software.
The program produces a distance matrix compatible with existing R packages for advanced statistical analysis.

Conclusions:

The developed R program offers a robust and efficient solution for DNA fingerprinting data analysis.
This tool enhances the statistical analysis capabilities for various DNA fingerprinting methods in microbiology.
The program facilitates previously challenging statistical analyses, expanding the utility of DNA fingerprinting studies.