Abstract
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.
Highlights
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
This study focuses on improving an alternative method for analyzing population changes in microbial communities, termed oligonucleotide fingerprinting of ribosomal rRNA genes (OFRG) [8,9,10], which may be useful for studies requiring the analysis of many samples at higher taxonomic resolution than current highthroughput sequencing methods provide
Very divergent sequences having the same fingerprint are considered no worse than very similar sequences having the same fingerprint. We address this shortcoming of the Maximum Distinguishing Probe Set (MDPS) with a new formulation for probe set selection termed the Maximum Fidelity Probe Set (MFPS) and a new processing pipeline for preparing the training data used by the MFPS
Summary
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. High-throughput sequencing of portions of 16S rRNA genes currently provides the best compromise between accuracy and throughput, but due to the short read-lengths (~150-450 bp) these are limited to elucidating the population densities of a microbial community confidently only at the order taxonomic level and some confidence at the genus level, but very little confidence at the species level [3,4]. Because of this limitation, follow on studies where one endeavors to track population densities of specific bacterial species are often impossible
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