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In ecology it has become common to apply DNA barcoding to biological samplesleading to datasets containing a large number of nucleotide sequences. Thefocus is then on inferring the taxonomic placement of each of these sequencesby leveraging on existing databases containing reference sequences having knowntaxa. This is highly challenging because i) sequencing is typically onlyavailable for a relatively small region of the genome due to costconsiderations; ii) many of the sequences are from organisms that are eitherunknown to science or for which there are no reference sequences available.These issues can lead to substantial classification uncertainty, particularlyin inferring new taxa. To address these challenges, we propose a new class ofBayesian nonparametric taxonomic classifiers, BayesANT, which use speciessampling model priors to allow new taxa to be discovered at each taxonomicrank. Using a simple product multinomial likelihood with conjugate Dirichletpriors at the lowest rank, a highly efficient algorithm is developed to providea probabilistic prediction of the taxa placement of each sequence at each rank.BayesANT is shown to have excellent performance in real data, including whenmany sequences in the test set belong to taxa unobserved in training.

Inferring taxonomic placement from DNA barcoding allowing discovery of new taxa