Predicting Intron Locations in Non-Model Organism Expressed Sequence Tags (ESTs) Using Comparative Homology with Divergent Model Organism Genomes

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dc.contributor.authorMamun, S.M. Al
dc.contributor.examiningcommitteeLeung, Carson Kai-Sang (Computer Science) Fristensky, Brian W. (Plant Science)en_US
dc.contributor.supervisorDomaratzki, Michael (Computer Science) Sharanowski, Barbara J. (Entomology)en_US
dc.date.accessioned2014-01-14T14:21:22Z
dc.date.available2014-01-14T14:21:22Z
dc.date.issued2014-01-14
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractFinding the approximate location of short read genome sequences by comparing them to an already available closely related organism's complete genome sequence is a challenging research issue. Predicting intron locations in the short form of mRNA called Expressed Sequence Tags (ESTs) and the variability of intron lengths are the major challenges. More specifically, finding the intron positions in an EST sequence by comparing it with a reference genome sequence is a time consuming task, as currently it is done manually. In my thesis, I designed a pipeline that can predict the intron positions in ESTs of non-model organisms. Initially, I compared the ESTs to the closest completely sequenced genome. The pipeline then finds the alignment of the ESTs, the reference genome sequence, and the coding region of the gene (known as Coding DNA Sequence or CDS) from the reference genome.en_US
dc.description.noteFebruary 2014en_US
dc.identifier.urihttp://hdl.handle.net/1993/23206
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectNon-model organismen_US
dc.subjectDivergenten_US
dc.titlePredicting Intron Locations in Non-Model Organism Expressed Sequence Tags (ESTs) Using Comparative Homology with Divergent Model Organism Genomesen_US
dc.typemaster thesisen_US
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