Analyzing taxonomic classification using extensible Markov models

Bioinformatics
Rao M KotamartiMargaret H Dunham

Abstract

As next generation sequencing is rapidly adding new genomes, their correct placement in the taxonomy needs verification. However, the current methods for confirming classification of a taxon or suggesting revision for a potential misplacement relies on computationally intense multi-sequence alignment followed by an iterative adjustment of the distance matrix. Due to intra-heterogeneity issues with the 16S rRNA marker, no classifier is available for sub-genus level, which could readily suggest a classification for a novel 16S rRNA sequence. Metagenomics further complicates the issue by generating fragmented 16S rRNA sequences. This article proposes a novel alignment-free method for representing the microbial profiles using extensible Markov models (EMMs) with an extended Karlin-Altschul statistical framework similar to the classic alignment paradigm. We propose a log odds (LODs) score classifier based on Gumbel difference distribution that confirms correct classifications with statistical significance qualifications and suggests revisions where necessary. We tested our method by generating a sub-genus level classifier with which we re-evaluated classifications of 676 microbial organisms using the NCBI FTP database for the 16S rR...Continue Reading

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Nov 14, 2013·Nature Communications·Mark P HamiltonSean E McGuire
Nov 22, 2013·Nature Communications·Tao MaLiu Jianquan
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Dec 1, 2015·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Xiujun Sylvia Zhu, Monnie McGee
Feb 26, 2014·BMC Bioinformatics·Anurag Nagar, Michael Hahsler

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