Application of Markov Structure of Genomes to Outlier Identification and Read Classification

TL;DR

This paper leverages the Markov structure of genomes to improve outlier detection in genome databases and enhance read classification in metagenomics, demonstrating practical applications with coronavirus and adenovirus data.

Contribution

It introduces a novel approach using second-order Markov models for genome analysis, specifically for outlier detection and read classification in metagenomics.

Findings

Effective outlier identification in genome databases

Improved accuracy in read classification for metagenomics

Validated with coronavirus and adenovirus datasets

Abstract

In this paper we apply the structure of genomes as second-order Markov processes specified by the distributions of successive triplets of bases to two bioinformatics problems: identification of outliers in genome databases and read classification in metagenomics, using real coronavirus and adenovirus data.