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Bioinformatics Advance Access published online on April 21, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti452
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received February 3, 2005
Revised April 13, 2005
Accepted April 13, 2005

Article

Classification of oligonucleotide fingerprints: application for microbial community and gene expression analyses

Katechan Jampachaisri 1, Lea Valinsky 2, James Borneman 3, and S. James Press 1*

1 Department of Statistics, University of California, Riverside, 92521 USA
2 Central Laboratories, Israeli Ministry of Health, Yaakov Eliav 9, Jerusalem, Israel 94467
3 Department of Plant Pathology, University of California, Riverside, 92521 USA

* To whom correspondence should be addressed.
S. James Press, E-mail: james.press{at}ucr.edu


  Abstract

Motivation: Oligonucleotide fingerprinting of ribosomal RNA genes (OFRG) is a procedure that sorts rRNA gene (rDNA) clones into taxonomic groups through a series of hybridization experiments. The hybridization signals are classified into three discrete values 0, 1, and N, where 0 and 1 respectively specify negative and positive hybridization events and N designates an uncertain assignment. This study examined various approaches for classifying the values including Bayesian classification with normally distributed signal data, Bayesian classification with the exponentially distributed data, and with gamma distributed data, along with tree-based classification. All classification data were clustered using the Unweighted Pair Group Method with Arithmetic Mean.

Results: The performance of each classification/clustering procedure was compared with results from known reference data. Comparisons indicated that the approach using Bayesian classification with normal densities followed by tree clustering out-performed all others. The paper includes a discussion of how this Bayesian approach may be useful for analysis of gene expression data.


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