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dc.contributor.advisorMiranker, Daniel P.en
dc.contributor.advisorAmbler, Tonyen
dc.creatorWang, Shu, 1973-en
dc.date.accessioned2008-08-29T00:05:34Zen
dc.date.available2008-08-29T00:05:34Zen
dc.date.issued2007-12en
dc.identifier.urihttp://hdl.handle.net/2152/3715en
dc.description.abstractThe tremendous increase in biological sequence data presents us with an opportunity to understand the molecular and cellular basis for cellular life. Comparative studies of these sequences have the potential, when applied with sufficient rigor, to decipher the structure, function, and evolution of cellular components. The accuracy and detail of these studies are directly proportional to the quality of these sequences alignments. Given the large number of sequences per family of interest, and the increasing number of families to study, improving the speed, accuracy and scalability of MSA is becoming an increasingly important task. In the past, much of interest has been on Global MSA. In recent years, the focus for MSA has shifted from global MSA to local MSA. Local MSA is being needed to align variable sequences from different families/species. In this dissertation, we developed two new algorithms for fast and scalable local MSA, a three-way-consistency-based MSA and a biclustering -based MSA. The first MSA algorithm is a three-way-Consistency-Based MSA (CBMSA). CBMSA applies alignment consistency heuristics in the form of a new three-way alignment to MSA. While three-way consistency approach is able to maintain the same time complexity as the traditional pairwise consistency approach, it provides more reliable consistency information and better alignment quality. We quantify the benefit of using three-way consistency as compared to pairwise consistency. We have also compared CBMSA to a suite of leading MSA programs and CBMSA consistently performs favorably. We also developed another new MSA algorithm, a biclustering-based MSA. Biclustering is a clustering method that simultaneously clusters both the domain and range of a relation. A challenge in MSA is that the alignment of sequences is often intended to reveal groups of conserved functional subsequences. Simultaneously, the grouping of the sequences can impact the alignment; precisely the kind of dual situation biclustering algorithms are intended to address. We define a representation of the MSA problem enabling the application of biclustering algorithms. We develop a computer program for local MSA, BlockMSA, that combines biclustering with divide-and-conquer. BlockMSA simultaneously finds groups of similar sequences and locally aligns subsequences within them. Further alignment is accomplished by dividing both the set of sequences and their contents. The net result is both a multiple sequence alignment and a hierarchical clustering of the sequences. BlockMSA was compared with a suite of leading MSA programs. With respect to quantitative measures of MSA, BlockMSA scores comparable to or better than the other leading MSA programs. With respect to biological validation of MSA, the other leading MSA programs lag BlockMSA in their ability to identify the most highly conserved regions.en
dc.format.mediumelectronicen
dc.language.isoengen
dc.rightsCopyright © is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.subject.lcshNucleotide sequence--Data processingen
dc.subject.lcshAmino acid sequence--Data processingen
dc.subject.lcshNucleotide sequence--Computer programsen
dc.subject.lcshBioinformaticsen
dc.subject.lcshComputer algorithmsen
dc.titleOn multiple sequence alignmenten
dc.description.departmentElectrical and Computer Engineeringen
dc.identifier.oclc212411200en
dc.type.genreThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


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