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Title: Computational identification and analysis of non-coding RNAs in large-scale biological data
Creator: Lei, Jikai
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Non-protein-coding RNAs (ncRNAs) are RNA molecules that function directly at the level of RNA without translating into protein. They play important biological functions in all three domains of life, i.e. Eukarya, Bacteria and Archaea. To understand the working mechanisms and the functions of ncRNAs in various species, a fundamental step is to identify both known and novel ncRNAs from large-scale biological data.Large-scale genomic data includes both genomic sequence data and NGS sequencing...
Show moreNon-protein-coding RNAs (ncRNAs) are RNA molecules that function directly at the level of RNA without translating into protein. They play important biological functions in all three domains of life, i.e. Eukarya, Bacteria and Archaea. To understand the working mechanisms and the functions of ncRNAs in various species, a fundamental step is to identify both known and novel ncRNAs from large-scale biological data.Large-scale genomic data includes both genomic sequence data and NGS sequencing data. Both types of genomic data provide great opportunity for identifying ncRNAs. For genomic sequence data, a lot of ncRNA identification tools that use comparative sequence analysis have been developed. These methods work well for ncRNAs that have strong sequence similarity. However, they are not well-suited for detecting ncRNAs that are remotely homologous. Next generation sequencing (NGS), while it opens a new horizon for annotating and understanding known and novel ncRNAs, also introduces many challenges. First, existing genomic sequence searching tools can not be readily applied to NGS data because NGS technology produces short, fragmentary reads. Second, most NGS data sets are large-scale. Existing algorithms are infeasible on NGS data because of high resource requirements. Third, metagenomic sequencing, which utilizes NGS technology to sequence uncultured, complex microbial communities directly from their natural inhabitants, further aggravates the difficulties. Thus, massive amount of genomic sequence data and NGS data calls for efficient algorithms and tools for ncRNA annotation.In this dissertation, I present three computational methods and tools to efficiently identify ncRNAs from large-scale biological data. Chain-RNA is a tool that combines both sequence similarity and structure similarity to locate cross-species conserved RNA elements with low sequence similarity in genomic sequence data. It can achieve significantly higher sensitivity in identifying remotely conserved ncRNA elements than sequence based methods such as BLAST, and is much faster than existing structural alignment tools. miR-PREFeR (miRNA PREdiction From small RNA-Seq data) utilizes expression patterns of miRNA and follows the criteria for plant microRNA annotation to accurately predict plant miRNAs from one or more small RNA-Seq data samples. It is sensitive, accurate, fast and has low-memory footprint. metaCRISPR focuses on identifying Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) from large-scale metagenomic sequencing data. It uses a kmer hash table to efficiently detect reads that belong to CRISPRs from the raw metagonmic data set. Overlap graph based clustering is then conducted on the reduced data set to separate different CRSIPRs. A set of graph based algorithms are used to assemble and recover CRISPRs from the clusters.
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Title: Non-coding RNA identification in large-scale genomic data
Creator: Yuan, Cheng
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Noncoding RNAs (ncRNAs), which function directly as RNAs without translating into proteins, play diverse and important biological functions. ncRNAs function not only through their primary structures, but also secondary structures, which are defined by interactions between Watson-Crick and wobble base pairs. Common types of ncRNA include microRNA, rRNA, snoRNA, tRNA. Functions of ncRNAs vary among different types. Recent studies suggest the existence of large number of ncRNA genes....
Show moreNoncoding RNAs (ncRNAs), which function directly as RNAs without translating into proteins, play diverse and important biological functions. ncRNAs function not only through their primary structures, but also secondary structures, which are defined by interactions between Watson-Crick and wobble base pairs. Common types of ncRNA include microRNA, rRNA, snoRNA, tRNA. Functions of ncRNAs vary among different types. Recent studies suggest the existence of large number of ncRNA genes. Identification of novel and known ncRNAs becomes increasingly important in order to understand their functionalities and the underlying communities.Next-generation sequencing (NGS) technology sheds lights on more comprehensive and sensitive ncRNA annotation. Lowly transcribed ncRNAs or ncRNAs from rare species with low abundance may be identified via deep sequencing. However, there exist several challenges in ncRNA identification in large-scale genomic data. First, the massive volume of datasets could lead to very long computation time, making existing algorithms infeasible. Second, NGS has relatively high error rate, which could further complicate the problem. Third, high sequence similarity among related ncRNAs could make them difficult to identify, resulting in incorrect output. Fourth, while secondary structures should be adopted for accurate ncRNA identification, they usually incur high computational complexity. In particular, some ncRNAs contain pseudoknot structures, which cannot be effectively modeled by the state-of-the-art approach. As a result, ncRNAs containing pseudoknots are hard to annotate.In my PhD work, I aimed to tackle the above challenges in ncRNA identification. First, I designed a progressive search pipeline to identify ncRNAs containing pseudoknot structures. The algorithms are more efficient than the state-of-the-art approaches and can be used for large-scale data. Second, I designed a ncRNA classification tool for short reads in NGS data lacking quality reference genomes. The initial homology search phase significantly reduces size of the original input, making the tool feasible for large-scale data. Last, I focused on identifying 16S ribosomal RNAs from NGS data. 16S ribosomal RNAs are very important type of ncRNAs, which can be used for phylogenic study. A set of graph based assembly algorithms were applied to form longer or full-length 16S rRNA contigs. I utilized paired-end information in NGS data, so lowly abundant 16S genes can also be identified. To reduce the complexity of problem and make the tool practical for large-scale data, I designed a list of error correction and graph reduction techniques for graph simplification.
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Title: The role of microRNA and serine palmitoyltransferase in Alzheimer's disease
Creator: Geekiyanage, Hirosha
Date: 2012
Collection: Electronic Theses & Dissertations
Description: THE ROLE OF MICRORNA AND SERINE PALMITOYLTRANSFERASE IN ALZHEIMER'S DISEASEByHirosha GeekiyanageThe mechanism by which early-on-set Alzheimer's disease (AD) manifests is well understood. However, little is known about the molecular mechanisms contributing to late-on-set AD, which accounts for >95% of AD cases. Research thus far invariably suggests that elevated ceramide, a sphingolipid, may be a risk factor for AD. Serine palmitoyltransferase (SPT) is not only the first rate limiting enzyme...
Show moreTHE ROLE OF MICRORNA AND SERINE PALMITOYLTRANSFERASE IN ALZHEIMER'S DISEASEByHirosha GeekiyanageThe mechanism by which early-on-set Alzheimer's disease (AD) manifests is well understood. However, little is known about the molecular mechanisms contributing to late-on-set AD, which accounts for >95% of AD cases. Research thus far invariably suggests that elevated ceramide, a sphingolipid, may be a risk factor for AD. Serine palmitoyltransferase (SPT) is not only the first rate limiting enzyme in the de novo synthesis of ceramide but varying SPT levels are consistently associated with varying ceramide levels. I observed that increased ceramide levels in AD are directly regulated by increased SPT levels. I also observed that SPT directly regulates amyloid beta (Aâ) levels through the post-transcriptional regulation of miR-137,-181c,-9 and -29a/b, suggesting SPT and the respective miRNAs are potential therapeutic targets for AD. Therefore, I investigated the use of SPT inhibition as a potential therapeutic strategy for AD. I administered a SPT inhibitor subcutaneously through surgically implanted osmotic pumps into an AD mouse model. I observed that the inhibition of SPT and thus ceramide, reduced cortical Aâ and hyperphosphorylated tau levels, major hallmarks of AD, with statistically significant correlations between SPT, ceramide and Aâ levels. With nominal toxic side effects observed, inhibition of SPT is suggested as a safe potential therapeutic strategy to ameliorate the AD pathology. In addition, I have identified that the afore mentioned miRNAs are reduced in the blood sera of probable AD and amnestic mild cognitive impaired patients, suggesting a potential use for these circulating miRNAs as non-invasive diagnostic biomarkers. In the AD mouse model studied, I observed that these miRNAs show positive correlations between their expressions in the brain cortices and presence in the sera, further suggesting a potential diagnostic role for these circulating miRNAs. A positive correlation was also observed between cortical and sera Aâ levels, providing further insights into the search of blood biomarkers.
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