Kmer-based sequence representations for fast retrieval and comparison

Abstract

This thesis presents a study of alignment-free methods for genetic sequence comparison. By using representations based on k-mers – short subsequences of length k - sequence similarity can be measured rapidly and accurately by calculating the distance between these paired representations. This research utilises and adapts conventional methods from information retrieval to generate novel representations for k-mers and sequence fragments. Precision was further improved through the use of machine learning approaches - especially neural networks - to learn relationships between k-mers and to generate enhanced sequence representations. These approaches have applications in large scale sequence comparison, especially in the analysis of metagenomic samples.