Classification of oligonucleotide fingerprints: application for microbial community and gene expression analyses.
BACKGROUND
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 the 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 the analysis of gene expression data.
Jampachaisri
Katechan
K
Department of Statistics, University of California Riverside, CA 92521, USA.
Valinsky
Lea
L
Borneman
James
J
Press
S James
SJ
D000465:Algorithms; D001419:Bacteria / Q000302:isolation & purification*; D018407:Bacterial Physiological Phenomena; D016000:Cluster Analysis; D016172:DNA Fingerprinting / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012335:RNA, Ribosomal / Q000235:genetics*
D012335:RNA, Ribosomal