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

BMC Bioinformatics

Methodology Article

Tao Jiang¹  James Borneman²  Paul M Ruegger²  Gianluca Della Vedova³ 

[1] Department of Computer Science and Engineering, University of California, 92521, Riverside, CA, USA;Department of Plant Pathology and Microbiology, University of California, 92521, Riverside, CA, USA;Department of Statistics, University of Milano-Bicocca, 20126, Milan, Italy;

关键词: Cost Function;    Terminal Restriction Fragment Length Polymorphism;    Training Sequence;    Hypervariable Region;    Ribosomal Database Project;   

DOI  :  10.1186/1471-2105-12-394

 received in 2011-05-06, accepted in 2011-10-10,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundPopulation 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.ResultsGene 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.ConclusionsProbe 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.

【 授权许可】

Unknown   
© Ruegger et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.