Analysis of Antigen Receptor Repertoires Captured by High Throughput Sequencing

Abstract

In vertebrate species, the main mechanisms of defence against various types of pathogens are divided into the innate and the adaptive immune system. While the former relies on generic mechanisms, for example to detect the presence of bacterial cells, the latter features mechanisms that allow the individual to acquire defenses against specific, potentially novel features of pathogens and to maintain them throughout life. In a simplified sense, the adaptive immune system continuously generates new defenses against all kinds of structures randomly, carefully selecting them not to be reactive against the hosts own cells. The underlying generative mechanism is a unique somatic recombination process modifying the genes encoding the proteins responsible for the recognition of such foreign structures, the so-called antigen receptors. With the advances of high throughput DNA sequencing, we have gained the ability to capture the repertoire of different antigen receptor genes that an individual has acquired by selectively sequencing the recombined loci from a cell sample. This enables us to examine and explore the development and behaviour of the adaptive immune system in a new way, with a variety of potential medical applications. The main focus of this thesis is on two computational problems related to immune repertoire sequencing. Firstly, we developed a method to properly annotate the raw sequencing data that is generated in such experiments, taking into account various sources of biases and errors that either generally occur in the context of DNA sequencing or are specific for immune repertoire sequencing experiments. We will describe the algorithmic details of this method and then demonstrate its superiority in comparison with previously published methods on various datasets. Secondly, we developed a machine learning based workflow to interpret this data in the sense that we attempted to classify such recombined genes functionally using a previously trained model. We implemented alternative models within this workflow, which we will first describe formally and then assess their performances on real data in the context of a binary functional feature in T cells, namely whether they have differentiated into cytotoxic or helper T cells.