Microbial Communities DNA Sequences Classification Based on Transition Matrix

The paper investigates DNA reads classification based on transition matrix before assembling reads into configs in sequencing microbial communities directly sampled from natural environment. Traditional methods use dynamic programming algorithm to directly detect overlaps between reads in order to find out whether or not two reads can be assembled into one config. However, in microbial communities, there are many species with unknown quantity and distribution so that majority of reads can be impossibly assembled together. To save such unnecessary computation detecting overlaps, we present a new method of feature extraction that can be used to group highly similar reads likely to be assembled into a genome. The basic idea is to define the dinucleotide as a state in a sequence which is viewed as Markov chain, and the transition probabilities of the neighboring states constitutes sixteen-by-sixteen transition matrix. The matrix is used as the feature of a sequence and its reshaped 1-by-256 matrix is viewed as the input pattern of a classifier. Computer simulations verify that the method works well