Analyzing taxonomic classification using extensible Markov models

Motivation: 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 that could readily suggest a classification for a novel 16S rRNA sequence. Metagenomics further complicates the issue by generating fragmented 16S rRNA sequences. This paper proposes a novel alignment-free method for representing the microbial profiles using Extensible Markov Models (EMM) with an extended Karlin-Altschul statistical framework similar to the classic alignment paradigm. We propose a Log Odds (LOD) score classifier based on Gumbel difference distribution that confirms correct classifications with st...

• Type: Journal
• Year: 2010
• Where: BIOINFORMATICS
• Authors: Rao M. Kotamarti, Michael Hahsler, Douglas Raiford, Monnie McGee, Margaret H. Dunham
• Keywords: 16S RRNA, 16s Rrna Sequences, BIOINFORMATICS 2010, Sub-genus Level