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Computational Haplotyping: theory and practice


Garg,  Shilpa
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

Marschall,  Tobias
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Garg, S. (2018). Computational Haplotyping: theory and practice. PhD Thesis, Universität des Saarlandes, Saarbrücken. doi:10.22028/D291-27252.

Genomics has paved a new way to comprehend life and its evolution, and also to investigate causes of diseases and their treatment. One of the important problems in genomic analyses is haplotype assembly. Constructing complete and accurate haplotypes plays an essential role in understanding population genetics and how species evolve. In this thesis, we focus on computational approaches to haplotype assembly from third generation sequencing technologies. This involves huge amounts of sequencing data, and such data contain errors due to the single molecule sequencing protocols employed. Taking advantage of combinatorial formulations helps to correct for these errors to solve the haplotyping problem. Various computational techniques such as dynamic programming, parameterized algorithms, and graph algorithms are used to solve this problem. This thesis presents several contributions concerning the area of haplotyping. First, a novel algorithm based on dynamic programming is proposed to provide approximation guarantees for phasing a single individual. Second, an integrative approach is introduced to combining multiple sequencing datasets to generating complete and accurate haplotypes. The effectiveness of this integrative approach is demonstrated on a real human genome. Third, we provide a novel efficient approach to phasing pedigrees and demonstrate its advantages in comparison to phasing a single individual. Fourth, we present a generalized graph-based framework for performing haplotype-aware de novo assembly. Specifically, this generalized framework consists of a hybrid pipeline for generating accurate and complete haplotypes from data stemming from multiple sequencing technologies, one that provides accurate reads and other that provides long reads.