Graduate Thesis Or Dissertation

Algorithms for massive biological datasets

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  • Within the past several years the technology of high-throughput sequencing has transformed the study of biology by offering unprecedented access to life's fundamental building block, DNA. With this transformation's potential a host of brand-new challenges have emerged, many of which lend themselves to being solved through computational methods. From de novo and reference-guided genome assembly to gene prediction and identification, from genome annotation to gene expression, a multitude of biological questions are being asked and answered using high-throughput sequencing and computational methods. In this thesis we examine topics relating to high-throughput sequencing. Beginning with de novo assembly we outline current state-of-the-art methods for stitching short reads, the output of high-throughput sequencing experiments, into cohesive genomic contigs and scaffolds. Next we present our own de novo assembly software, QSRA, created in an effort to form longer contigs even through areas of low coverage and high error. We then present an application of short-read assembly and mutation analysis in a discussion of single nucleotide polymorphism discovery in hazelnut, followed by a review of de novo gene finding, the act of identifying genes in anonymous stretches of genomic sequence. Next we outline our supersplat software, built to align short reads generated by RNA-seq experiments, which span splice junctions, followed by the presentation of our gumby software, build to construct putative gene models from purely empirical short-read data. Finally we outline current state-of-the-art methods for discovering and quantifying alternative splicing variants from RNA-seq short-read data. High-throughput sequencing has fundamentally changed the way in which we approach biological questions. While an exceptionally powerful tool, high-throughput sequencing analysis demands equally powerful algorithmic techniques. We examine these issues through the lens of computational biology.
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