Date Available
12-19-2013
Year of Publication
2013
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Engineering
Department/School/Program
Computer Science
Advisor
Dr. Jinze Liu
Abstract
The advance of high-throughput sequencing technologies and their application on mRNA transcriptome sequencing (RNA-seq) have enabled comprehensive and unbiased profiling of the landscape of transcription in a cell. In order to address the current limitation of analyzing accuracy and scalability in transcriptome analysis, a novel computational framework has been developed on large-scale RNA-seq datasets with no dependence on transcript annotations. Directly from raw reads, a probabilistic approach is first applied to infer the best transcript fragment alignments from paired-end reads. Empowered by the identification of alternative splicing modules, this framework then performs precise and efficient differential analysis at automatically detected alternative splicing variants, which circumvents the need of full transcript reconstruction and quantification. Beyond the scope of classical group-wise analysis, a clustering scheme is further described for mining prominent consistency among samples in transcription, breaking the restriction of presumed grouping. The performance of the framework has been demonstrated by a series of simulation studies and real datasets, including the Cancer Genome Atlas (TCGA) breast cancer analysis. The successful applications have suggested the unprecedented opportunity in using differential transcription analysis to reveal variations in the mRNA transcriptome in response to cellular differentiation or effects of diseases.
Recommended Citation
Hu, Yin, "A NOVEL COMPUTATIONAL FRAMEWORK FOR TRANSCRIPTOME ANALYSIS WITH RNA-SEQ DATA" (2013). Theses and Dissertations--Computer Science. 17.
https://uknowledge.uky.edu/cs_etds/17
Included in
Bioinformatics Commons, Computational Biology Commons, Genomics Commons, Other Computer Sciences Commons
