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Computational Studies On Recognition, Evolution And Transcriptional Regulation Of MicroRNAs

Posted on:2009-09-18Degree:DoctorType:Dissertation
Country:ChinaCandidate:X W WangFull Text:PDF
GTID:1100360272491665Subject:Control Science and Engineering
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MicroRNAs (miRNAs) are a class of ~22 nt long endogenous small non-coding RNAs. During the last few years, these tiny molecules received wide attention due to their important regulatory roles in various biological processes and potential application in diagnosis and treatment of human diseases. In this dissertation, I studied miRNAs'identification, their evolutionary properties and transcriptional regulations using bioinformatics approaches. My work is mainly composed by the following four parts:(1) We developed a high performance miRNA prediction method, which can identify distantly related miRNA homolog genes. In the early days of miRNA research, only highly expressed miRNA genes can be easily detected by PCR or northern blot due to limitations of the techniques. For finding those low-expression or tissue-specific miRNA genes, computational prediction provides an efficient approach. We developed a miRNA prediction algorithm based on both sequence and structure alignment. Experiments show this approach has higher sensitivity and specificity than other reported homologue searching methods.(2) We studied the evolutionary patterns of miRNAs in vertebrates, and provided a new angle of view to study the function of miRNAs during the evolution. It was believed that most miRNAs are under intense purifying selection over evolutionary time and highly conserved. However, our observations and analysis suggested that miRNAs have complicated evolutionary patterns and may play very active roles in evolution.(3) We studied the transcriptional regulation mechanism of miRNAs using comparative genomic approaches. The regulation of miRNA transcription is largely unknown. Recently, the genome sequencing of diverse animal and plant species provides researchers a great opportunity to study gene transcriptional regulation mechanisms through comparative genomic approaches. In this thesis, we proposed a two-step strategy that takes the advantage of both alignment-based and motif-based methods to identify conserved DNA sequence elements and provided a systematic approach to analyze these elements. We successfully applied this method to analyze the miRNA cis-regulatory elements that are conserved across the Drosophila species.(4) We provided a new gene core promoter prediction method which can accurately identify most of the known miRNA core promoters. The correct localization of gene transcription start site and core promoter is important for understanding the transcriptional regulation of genes. We integrated genome wide histone modification profiles and the DNA sequence features together to predict gene core promoters in the human genome. Our new predictor outperforms existing algorithms by providing significantly higher sensitivity, specificity and finer resolution. This method will greatly help us to identify and characterize core promoters of both coding and non-coding genes.
Keywords/Search Tags:microRNA, Identification, Evolution, Transcriptional regulation
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