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A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy.

Xiang GaoHuaiying LinKashi RevannaQunfeng Dong
Published in: BMC bioinformatics (2017)
Reliable species-level classification for 16S rRNA or other phylogenetic marker genes is critical for microbiome research. Our software shows significantly higher classification accuracy than the existing tools and we provide probabilistic-based confidence scores to evaluate the reliability of our taxonomic classification assignments based on multiple database matches to query sequences. Despite its higher computational costs, our method is still suitable for analyzing large-scale microbiome datasets for practical purposes. Furthermore, our method can be applied for taxonomic classification of any phylogenetic marker gene sequences. Our software, called BLCA, is freely available at https://github.com/qunfengdong/BLCA .
Keyphrases
  • deep learning
  • machine learning
  • genome wide
  • copy number
  • genome wide identification
  • gene expression
  • dna methylation