Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

Handle URI:
http://hdl.handle.net/10754/625171
Title:
Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering
Authors:
Alzahrani, Majed A. ( 0000-0002-4450-1259 )
Abstract:
Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such recently emerging high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment. In this dissertation, we first demonstrate that detecting such “co-fit” gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype data sets for E. coli, proteobacteria, and yeast.
Advisors:
Gao, Xin ( 0000-0002-7108-3574 )
Committee Member:
Bajic, Vladimir B. ( 0000-0001-5435-4750 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Xu, Ying
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
10-Jul-2017
Type:
Dissertation
Appears in Collections:
Dissertations

Full metadata record

DC FieldValue Language
dc.contributor.advisorGao, Xinen
dc.contributor.authorAlzahrani, Majed A.en
dc.date.accessioned2017-07-10T07:07:39Z-
dc.date.available2017-07-10T07:07:39Z-
dc.date.issued2017-07-10-
dc.identifier.urihttp://hdl.handle.net/10754/625171-
dc.description.abstractGrowth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such recently emerging high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment. In this dissertation, we first demonstrate that detecting such “co-fit” gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype data sets for E. coli, proteobacteria, and yeast.en
dc.language.isoenen
dc.subjectdata miningen
dc.subjectbiclusteringen
dc.subjectphenotype profiling dataen
dc.titleMining Genome-Scale Growth Phenotype Data through Constant-Column Biclusteringen
dc.typeDissertationen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberBajic, Vladimir B.en
dc.contributor.committeememberMoshkov, Mikhailen
dc.contributor.committeememberXu, Yingen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameDoctor of Philosophyen
dc.person.id118485en
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