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Open Access Methodology article

Improving probe set selection for microbial community analysis by leveraging taxonomic information of training sequences

Paul M Ruegger1, Gianluca Della Vedova2, Tao Jiang3 and James Borneman1*

Author Affiliations

1 Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521, USA

2 Department of Statistics, University of Milano-Bicocca, Milan, 20126, Italy

3 Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA

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BMC Bioinformatics 2011, 12:394  doi:10.1186/1471-2105-12-394

Published: 10 October 2011

Abstract

Background

Population levels of microbial phylotypes can be examined using a hybridization-based method that utilizes a small set of computationally-designed DNA probes targeted to a gene common to all. Our previous algorithm attempts to select a set of probes such that each training sequence manifests a unique theoretical hybridization pattern (a binary fingerprint) to a probe set. It does so without taking into account similarity between training gene sequences or their putative taxonomic classifications, however. We present an improved algorithm for probe set selection that utilizes the available taxonomic information of training gene sequences and attempts to choose probes such that the resultant binary fingerprints cluster into real taxonomic groups.

Results

Gene sequences manifesting identical fingerprints with probes chosen by the new algorithm are more likely to be from the same taxonomic group than probes chosen by the previous algorithm. In cases where they are from different taxonomic groups, underlying DNA sequences of identical fingerprints are more similar to each other in probe sets made with the new versus the previous algorithm. Complete removal of large taxonomic groups from training data does not greatly decrease the ability of probe sets to distinguish those groups.

Conclusions

Probe sets made from the new algorithm create fingerprints that more reliably cluster into biologically meaningful groups. The method can readily distinguish microbial phylotypes that were excluded from the training sequences, suggesting novel microbes can also be detected.