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Computational Methods for Comparative Non-coding RNA Analysis: From Structural Motif Identification to Genome-wide Functional Classification
Title
Computational Methods for Comparative Non-coding RNA Analysis: From Structural Motif Identification to Genome-wide Functional Classification.
Creator
Zhong, Cuncong, Zhang, Shaojie, Hu, Haiyan, Hua, Kien, Li, Xiaoman, University of Central Florida
Abstract / Description
Non-coding RNA (ncRNA) plays critical functional roles such as regulation, catalysis, and modification etc. in the biological system. Non-coding RNAs exert their functions based on their specific structures, which makes the thorough understanding of their structures a key step towards their complete functional annotation. In this dissertation, we will cover a suite of computational methods for the comparison of ncRNA secondary and 3D structures, and their applications to ncRNA molecular...
Show moreNon-coding RNA (ncRNA) plays critical functional roles such as regulation, catalysis, and modification etc. in the biological system. Non-coding RNAs exert their functions based on their specific structures, which makes the thorough understanding of their structures a key step towards their complete functional annotation. In this dissertation, we will cover a suite of computational methods for the comparison of ncRNA secondary and 3D structures, and their applications to ncRNA molecular structural annotation and their genome-wide functional survey.Specifically, we have contributed the following five computational methods. First, we have developed an alignment algorithm to compare RNA structural motifs, which are recurrent RNA 3D structural fragments. Second, we have improved upon the previous alignment algorithm by incorporating base-stacking information and devise a new branch-and-bond algorithm. Third, we have developed a clustering pipeline for RNA structural motif classification using the above alignment methods. Fourth, we have generalized the clustering pipeline to a genome-wide analysis of RNA secondary structures. Finally, we have devised an ultra-fast alignment algorithm for RNA secondary structure by using the sparse dynamic programming technique.A large number of novel RNA structural motif instances and ncRNA elements have been discovered throughout these studies. We anticipate that these computational methods will significantly facilitate the analysis of ncRNA structures in the future.
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Date Issued
2013
Identifier
CFE0004966, ucf:49580
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004966
Computational Methods for Analyzing RNA Folding Landscapes and its Applications
Title
Computational Methods for Analyzing RNA Folding Landscapes and its Applications.
Creator
Li, Yuan, Zhang, Shaojie, Hua, Kien, Jha, Sumit, Hu, Haiyan, Li, Xiaoman, University of Central Florida
Abstract / Description
Non-protein-coding RNAs play critical regulatory roles in cellular life. Many ncRNAs fold into specific structures in order to perform their biological functions. Some of the RNAs, such as riboswitches, can even fold into alternative structural conformations in order to participate in different biological processes. In addition, these RNAs can transit dynamically between different functional structures along folding pathways on their energy landscapes. These alternative functional structures...
Show moreNon-protein-coding RNAs play critical regulatory roles in cellular life. Many ncRNAs fold into specific structures in order to perform their biological functions. Some of the RNAs, such as riboswitches, can even fold into alternative structural conformations in order to participate in different biological processes. In addition, these RNAs can transit dynamically between different functional structures along folding pathways on their energy landscapes. These alternative functional structures are usually energetically favored and are stable in their local energy landscapes. Moreover, conformational transitions between any pair of alternate structures usually involve high energy barriers, such that RNAs can become kinetically trapped by these stable and local optimal structures.We have proposed a suite of computational approaches for analyzing and discovering regulatory RNAs through studying folding pathways, alternative structures and energy landscapes associated with conformational transitions of regulatory RNAs. First, we developed an approach, RNAEAPath, which can predict low-barrier folding pathways between two conformational structures of a single RNA molecule. Using RNAEAPath, we can analyze folding pathways between two functional RNA structures, and therefore study the mechanism behind RNA functional transitions from a thermodynamic perspective. Second, we introduced an approach, RNASLOpt, for finding all the stable and local optimal structures on the energy landscape of a single RNA molecule. We can use the generated stable and local optimal structures to represent the RNA energy landscape in a compact manner. In addition, we applied RNASLOpt to several known riboswitches and predicted their alternate functional structures accurately. Third, we integrated a comparative approach with RNASLOpt, and developed RNAConSLOpt, which can find all the consensus stable and local optimal structuresthat are conserved among a set of homologous regulatory RNAs. We can use RNAConSLOpt to predict alternate functional structures for regulatory RNA families. Finally, we have proposed a pipeline making use of RNAConSLOpt to computationally discover novel riboswitches in bacterial genomes. An application of the proposed pipeline to a set of bacteria in Bacillus genus results in the re-discovery of many known riboswitches, and the detection of several novel putative riboswitch elements.
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Date Issued
2012
Identifier
CFE0004400, ucf:49365
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004400
Computational Approaches for Binning Metagenomic Reads
Title
Computational Approaches for Binning Metagenomic Reads.
Creator
Wang, Ying, Hu, Haiyan, Li, Xiaoman, Zhang, Shaojie, Wu, Annie, Savage, Anna, University of Central Florida
Abstract / Description
Metagenomics uses sequencing technologies to study genetic sequences from whole microbial communities. Binning metagenomic reads is the most fundamental step in metagenomic studies, which is essential for the understanding of microbial functions, compositions, and interactions in environmental samples. Various taxonomy-dependent and taxonomy-independent approaches have been developed based on information such as sequence similarity, sequence composition, or k-mer frequency. However, there is...
Show moreMetagenomics uses sequencing technologies to study genetic sequences from whole microbial communities. Binning metagenomic reads is the most fundamental step in metagenomic studies, which is essential for the understanding of microbial functions, compositions, and interactions in environmental samples. Various taxonomy-dependent and taxonomy-independent approaches have been developed based on information such as sequence similarity, sequence composition, or k-mer frequency. However, there is still room for improvement, and it is still challenging to bin reads from species with similar or low abundance or to bin reads from unknown species.In this dissertation, we introduce one taxonomy-independent and three taxonomy-dependent approaches to improve the performance of metagenomic reads binning. The taxonomy-independent method called MBBC, bins reads by considering k-mer frequency in reads without reference genomes. The first two taxonomy-dependent methods both bin reads by measuring the similarity of reads to the trained Markov Chains from different taxa. The major difference between these two methods is that the first one selects the potential taxa with the taxonomical decision tree, while the second one, called MBMC, selects potential taxa using ordinary least squares (OLS) method. The third taxonomy-dependent method bins reads by combining the methods of MBMC with clustering Markov chains from the assembled reads. By testing on both simulated and real datasets, these tools showed superior or comparable performance with various the state of the art methods. We anticipate that our tools can significantly improve the accuracy of metagenomic reads binning and thus be widely applied in real environmental samples.
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Date Issued
2016
Identifier
CFE0006515, ucf:51380
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006515
Transcriptional and Post-transcriptional Regulation of Gene Expression
Title
Transcriptional and Post-transcriptional Regulation of Gene Expression.
Creator
Ding, Jun, Hu, Haiyan, Li, Xiaoman, Zhang, Shaojie, Jin, Yier, University of Central Florida
Abstract / Description
Regulation of gene expression includes a variety of mechanisms to increase or decrease specific gene products. Gene expression can be regulated at any stage from transcription to post-transcription and it's essential to almost all living organisms, as it increases the versatility and adaptability by allowing the cell to express the needed proteins. In this dissertation, we comprehensively studied the gene regulation from both transcriptional and post-transcriptional points of view....
Show moreRegulation of gene expression includes a variety of mechanisms to increase or decrease specific gene products. Gene expression can be regulated at any stage from transcription to post-transcription and it's essential to almost all living organisms, as it increases the versatility and adaptability by allowing the cell to express the needed proteins. In this dissertation, we comprehensively studied the gene regulation from both transcriptional and post-transcriptional points of view. Transcriptional regulation is by which cells regulate the transcription from DNA to RNA, thereby directing gene activity. Transcriptional factors (TFs) play a very important role in transcriptional regulation and they are proteins that bind to specific DNA sequences (regulatory elements) to regulate the gene expression. Current studies on TF binding are still very limited and thus, it leaves much to be improved on understanding the TF binding mechanism. To fill this gap, we proposed a variety of computational methods for predicting TF binding elements, which have been proved to be more efficient and accurate compared with other existing tools such as DREME and RSAT peaks-motif. On the other hand, studying only the transcriptional gene regulation is not enough for a comprehensive understanding. Therefore, we also studied the gene regulation at the post-transcriptional level. MicroRNAs (miRNAs) are believed to post-transcriptionally regulate the expression of thousands of target mRNAs, yet the miRNA binding mechanism is still not well understood. In this dissertation, we explored both the traditional and novel features of miRNA-binding and proposed several computational models for miRNA target prediction. The developed tools outperformed the traditional microRNA target prediction methods (.e.g miRanda and TargetScan) in terms of prediction accuracy (precision, recall) and time efficiency.
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Date Issued
2016
Identifier
CFE0006098, ucf:51197
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006098