Quasi-metrics, Similarities and Searches: aspects of geometry of protein datasets

TL;DR

This thesis explores the geometry of protein datasets using quasi-metrics, establishing a link between biological sequence similarity and asymmetric distance functions, and develops indexing methods for efficient similarity searches.

Contribution

It introduces a new theoretical framework connecting quasi-metrics with biological sequence similarity and develops practical indexing schemes for protein dataset searches.

Findings

High-dimensional quasi-metric spaces are nearly metric spaces

Novel bounds on indexing scheme performance are established

FSIndex significantly accelerates protein similarity searches

Abstract

A quasi-metric is a distance function which satisfies the triangle inequality but is not symmetric: it can be thought of as an asymmetric metric. The central result of this thesis, developed in Chapter 3, is that a natural correspondence exists between similarity measures between biological (nucleotide or protein) sequences and quasi-metrics. Chapter 2 presents basic concepts of the theory of quasi-metric spaces and introduces a new examples of them: the universal countable rational quasi-metric space and its bicompletion, the universal bicomplete separable quasi-metric space. Chapter 4 is dedicated to development of a notion of the quasi-metric space with Borel probability measure, or pq-space. The main result of this chapter indicates that `a high dimensional quasi-metric space is close to being a metric space'. Chapter 5 investigates the geometric aspects of the theory of database similarity search in the context of quasi-metrics. The results about $pq$-spaces are used to produce novel theoretical bounds on performance of indexing schemes. Finally, the thesis presents some biological applications. Chapter 6 introduces FSIndex, an indexing scheme that significantly accelerates similarity searches of short protein fragment datasets. Chapter 7 presents the prototype of the system for discovery of short functional protein motifs called PFMFind, which relies on FSIndex for similarity searches.