Unbiased probabilistic taxonomic classification for DNA barcoding

摘要

Motivation: When targeted to a barcoding region, high-throughput sequencing can be used to identify species or operational taxonomical units from environmental samples, and thus to study the diversity and structure of species communities. Although there are many methods which provide confidence scores for assigning taxonomic affiliations, it is not straightforward to translate these values to unbiased probabilities. We present a probabilistic method for taxonomical classification (PROTAX) of DNA sequences. Given a pre-defined taxonomical tree structure that is partially populated by reference sequences, PROTAX decomposes the probability of one to the set of all possible outcomes. PROTAX accounts for species that are present in the taxonomy but that do not have reference sequences, the possibility of unknown taxonomical units, as well as mislabeled reference sequences. PROTAX is based on a statistical multinomial regression model, and it can utilize any kind of sequence similarity measures or the outputs of other classifiers as predictors.