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

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 sequen

SUBMITTER: Ruegger PM 

PROVIDER: S-EPMC3224148 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature