Computational Methods for High-Throughput Genomics and Transcriptomics

The completion of genome sequences for many species, including humans and a number of model organisms, was considered a major milestone at the turn of the millennium. It has been quickly realised that focusing on a single reference genome per species is insufficient to understand the diversity within and between organisms. However, for each species, a multitude of genome sequences are required to give insight into causal sequences for variable traits. The advent of high-throughput technologies such as next-generation sequencing has undoubtedly accelerated sequencing and allowed for many exciting large-scale studies in genetics that were not previously conceivable. Genome-wide association studies, in which the linkage of sequence and phenotype variations is investigated, have highly profited from the recent technology development. For these kinds of studies, it is indispensable to measure genotypes and relevant traits for a large set of individuals. Because such data is of immense quantity and often noisy, computational approaches are required to analyse data from next-generation genetics. In the context of my thesis, I have contributed to the analysis of biological high-throughput data in two respects. In order to accurately describe genotypes, I have designed efficient large-scale tools to identify and catalogue polymorphisms from array data. Moreover, I have developed approaches for the estimation of transcript abundances from next-generation sequencing data, enabling precise analyses of transcriptomes. The first part of my thesis focuses on the analysis of array-based resequencing data that was obtained to describe sequence variation across 20 diverse varieties of domesticated rice. I applied sophisticated machine learning methods for efficient and accurate analysis of this enormous set of hybridisation data. Using an approach based on Support Vector Machines, I uncovered more than 300,000 non-redundant single-nucleotide polymorphisms, which were found to be highly accurate assessed on a gold standard set of polymorphisms. For the detection of complex regions of polymorphisms, I employed a second machine learning method based on Hidden Markov Support Vector Machines, revealing between 65,000 and 203,000 polymorphic regions across varieties and complementing the SNP set derived with the SVM-based approach. Altogether, detecting hundreds of thousands of polymorphisms on a genomewide scale has enabled the assembly of the first whole genome set of polymorphisms for the world’s most important crop plant. In the second part of my dissertation, I address the question of accurate quantification of transcriptomes from RNA sequencing measurements. For this purpose, I developed a novel computational method that uses techniques from machine learning and optimisation. In particular, this tool, rQuant, infers the abundance of alternative transcripts and simultaneously estimates the effect of biases induced by experimental settings. Quantifying transcripts from artificial as well as experimental data sets demonstrated the superiority of rQuant in an evaluation for diverse settings and a comparison against other transcript quantification tools. Moreover, I adapted ideas of rQuant to develop a tool for quantitative deconvolution of RNA secondary structures. rQuant is available to the community as open-source software and as a web service. In conclusion, my thesis contributes to key parts of research in high-throughput genomics and transcriptomics. This work will facilitate the identification of genotype and phenotype linkage and will improve our understanding of the biological processes that make individuals unique.