Machine Learning
Alternative prior assumptions for improving the performance of naïve Bayesian classifiers
Data Mining and Knowledge Discovery
Bioinformatics
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The introduction of next generation sequencing in ecological studies has created a major revolution in microbial and fungal ecology. Direct sequencing of hypervariable regions from ribosomal RNA genes can provide rapid and inexpensive analysis for ecological communities. In order to get deep understanding from these rRNA fragments, the Ribosomal Database Project developed the 'RDP Classifier' utilizing 8-mer nucleotide frequencies with Bayesian theorem to obtain taxonomy affiliation. The classifier is computationally efficient and works well with massive short sequences. However, the binary model employed in the RDP classifier does not consider the repetitive 8-mers in each reference sequence. Previous studies have pointed out that multinomial model usually results a better performance than binary model. In this study, we present the naïve Bayesian classifiers with multinomial models that take repetitive 8-mers into account for classifying microbial 16S and fungal 28S rRNA sequences. The results obtained from the multinomial approach were compared with those obtained from the binomial RDP classifier by 250-bp, 400-bp, 800-bp, and full-length reads to demonstrate that the multinomial approach can generally achieve a higher prediction accuracy in most hypervariable regions.