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RNA molecules form complex higher-order structures which are essential to perform their biological activities. The accurate prediction of an RNA secondary structure and other higher-order structural constraints will significantly enhance the understanding of RNA molecules and help interpret their functions. Covariation analysis is the predominant computational method to accurately predict the base pairs in the secondary structure of RNAs. I developed a novel and powerful covariation method, Phylogenetic Events Count (PEC) method, to determine the positional covariation. The application of the PEC method onto a bacterial 16S rRNA sequence alignment proves that it is more sensitive and accurate than other mutual information based method in the identification of base-pairs and other structural constraints of the RNA structure. The analysis also discoveries a new type of structural constraint -- neighbor effect, between sets of nucleotides that are in proximity in the three dimensional RNA structure with weaker but significant covariation with one another. Utilizing these covariation methods, a proposed secondary structure model of an entire HIV-1 genome RNA is evaluated. The results reveal that vast majority of the predicted base pairs in the proposed HIV-1 secondary structure model do not have covariation, thus lack the support from comparative analysis. Generating the most accurate multiple sequence alignment is fundamental and essential of performing high-quality comparative analysis. The rapid determination of nucleic acid sequences dramatically increases the number of available sequences. Thus developing the accurate and rapid alignment program for these RNA sequences has become a vital and challenging task to decipher the maximum amount of information from the data. A template-based RNA sequence alignment system, CRWAlign-2, is developed to accurately align new sequences to an existing reference sequence alignment based on primary and secondary structural similarity. A comparison of CRWAlign-2 with eight alternative widely-used alignment programs reveals that CRWAlign-2 outperforms other programs in aligning new sequences with higher accuracy. In addition to aligning sequences accurately, CRWAlign-2 also creates secondary structure models for each sequence to be aligned, which provides very useful information for the comparative analysis of RNA sequences and structures. The CRWAlign-2 program also provides opportunities for multiple areas including the identification of chimeric 16S rRNA sequences generated in microbiome sequencing projects.
The accurate prediction of an RNA secondary structure from its sequence will enhance the experimental design and interpretation for the increasing number of scientists that study RNA. While the computer programs that make these predictions have improved, additional improvements are necessary, in particular for larger RNAs. The first major section of this dissertation is concerned with improving the prediction accuracy of RNA secondary structures by generating new energetic parameters and evaluating a new RNA folding model. Statistical potentials for hairpin and internal loops produce significantly higher prediction accuracy when compared with nine other folding programs. While more improvements can be made to the energetic parameters used by secondary structure folding programs, I believe that a new approach is also necessary. I describe a RNA folding model that is predicated on a large body of computational and experimental work. This model includes energetics, contact distance, competition and a folding pathway. Each component of this folding model is evaluated and substantiated for its validity. The statistical potentials were created with comparative analysis. Comparative analysis requires the creation of highly accurate multiple RNA sequence alignments. The second major section of this dissertation is focused on my template-based sequence aligner, CRWAlign. Multiple sequence aligners generally run into problems when the pairwise sequence identity drops too low. By utilizing multiple dimensions of data to establish a profile for each position in a template alignment, CRWAlign is able to align new sequences with high accuracy even for pairs of sequence with low identity.
With the dramatic increase in RNA 3D structure determination in recent years, we now know that RNA molecules are highly structured. Moreover, knowledge of RNA 3D structures has proven crucial for understanding in atomic detail how they carry out their biological functions. Because of the huge number of potentially important RNA molecules in biology, many more than can be studied experimentally, we need theoretical approaches for predicting 3D structures on the basis of sequences alone. This volume provides a comprehensive overview of current progress in the field by leading practitioners employing a variety of methods to model RNA 3D structures by homology, by fragment assembly, and by de novo energy and knowledge-based approaches.
This dissertation, "Efficient Methods for Improving the Sensitivity and Accuracy of RNA Alignments and Structure Prediction" by Yaoman, Li, 李耀满, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: RNA plays an important role in molecular biology. RNA sequence comparison is an important method to analysis the gene expression. Since aligning RNA reads needs to handle gaps, mutations, poly-A tails, etc. It is much more difficult than aligning other sequences. In this thesis, we study the RNA-Seq align tools, the existing gene information database and how to improve the accuracy of alignment and predict RNA secondary structure. The known gene information database contains a lot of reliable gene information that has been discovered. And we note most DNA align tools are well developed. They can run much faster than existing RNA-Seq align tools and have higher sensitivity and accuracy. Combining with the known gene information database, we present a method to align RNA-Seq data by using DNA align tools. I.e. we use the DNA align tools to do alignment and use the gene information to convert the alignment to genome based. The gene information database, though updated daily, there are still a lot of genes and alternative splicings that hadn't been discovered. If our RNA align tool only relies on the known gene database, then there may be a lot reads that come from unknown gene or alternative splicing cannot be aligned. Thus, we show a combinational method that can cover potential alternative splicing junction sites. Combining with the original gene database, the new align tools can cover most alignments which are reported by other RNA-Seq align tools. Recently a lot of RNA-Seq align tools have been developed. They are more powerful and faster than the old generation tools. However, the RNA read alignment is much more complicated than other sequence alignment. The alignments reported by some RNA-Seq align tools have low accuracy. We present a simple and efficient filter method based on the quality score of the reads. It can filter most low accuracy alignments. At last, we present a RNA secondary prediction method that can predict pseudoknot(a type of RNA secondary structure) with high sensitivity and specificity. DOI: 10.5353/th_b5153733 Subjects: Nucleotide sequence - Data processing
Covers the fundamentals and techniques of multiple biological sequence alignment and analysis, and shows readers how to choose the appropriate sequence analysis tools for their tasks This book describes the traditional and modern approaches in biological sequence alignment and homology search. This book contains 11 chapters, with Chapter 1 providing basic information on biological sequences. Next, Chapter 2 contains fundamentals in pair-wise sequence alignment, while Chapters 3 and 4 examine popular existing quantitative models and practical clustering techniques that have been used in multiple sequence alignment. Chapter 5 describes, characterizes and relates many multiple sequence alignment models. Chapter 6 describes how traditionally phylogenetic trees have been constructed, and available sequence knowledge bases can be used to improve the accuracy of reconstructing phylogeny trees. Chapter 7 covers the latest methods developed to improve the run-time efficiency of multiple sequence alignment. Next, Chapter 8 covers several popular existing multiple sequence alignment server and services, and Chapter 9 examines several multiple sequence alignment techniques that have been developed to handle short sequences (reads) produced by the Next Generation Sequencing technique (NSG). Chapter 10 describes a Bioinformatics application using multiple sequence alignment of short reads or whole genomes as input. Lastly, Chapter 11 provides a review of RNA and protein secondary structure prediction using the evolution information inferred from multiple sequence alignments. • Covers the full spectrum of the field, from alignment algorithms to scoring methods, practical techniques, and alignment tools and their evaluations • Describes theories and developments of scoring functions and scoring matrices •Examines phylogeny estimation and large-scale homology search Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Applications is a reference for researchers, engineers, graduate and post-graduate students in bioinformatics, and system biology and molecular biologists. Ken Nguyen, PhD, is an associate professor at Clayton State University, GA, USA. He received his PhD, MSc and BSc degrees in computer science all from Georgia State University. His research interests are in databases, parallel and distribute computing and bioinformatics. He was a Molecular Basis of Disease fellow at Georgia State and is the recipient of the highest graduate honor at Georgia State, the William M. Suttles Graduate Fellowship. Xuan Guo, PhD, is a postdoctoral associate at Oak Ridge National Lab, USA. He received his PhD degree in computer science from Georgia State University in 2015. His research interests are in bioinformatics, machine leaning, and cloud computing. He is an editorial assistant of International Journal of Bioinformatics Research and Applications. Yi Pan, PhD, is a Regents' Professor of Computer Science and an Interim Associate Dean and Chair of Biology at Georgia State University. He received his BE and ME in computer engineering from Tsinghua University in China and his PhD in computer science from the University of Pittsburgh. Dr. Pan's research interests include parallel and distributed computing, optical networks, wireless networks and bioinformatics. He has published more than 180 journal papers with about 60 papers published in various IEEE/ACM journals. He is co-editor along with Albert Y. Zomaya of the Wiley Series in Bioinformatics.
The existence of genes for RNA molecules not coding for proteins (ncRNAs) has been recognized since the 1950's, but until recently, aside from the critically important ribosomal and transfer RNA genes, most focus has been on protein coding genes. However, a long series of striking discoveries, from RNA's ability to carry out catalytic function, to discovery of riboswitches, microRNAs and other ribo-regulators performing critical tasks in essentially all living organisms, has created a burgeoning interest in this primordial component of the biosphere. However, the structural characteristics and evolutionary constraints on RNA molecules are very different from those of proteins, necessitating development of a completely new suite of informatic tools to address these challenges. In RNA Sequence, Structure, Function: Computational and Bioinformatic Methods, expert researchers in the field describe a substantial and relevant fraction of these methodologies from both practical and computational/algorithmic perspectives. Focusing on both of these directions addresses both the biologist interested in knowing more about RNA bioinformatics as well as the bioinformaticist interested in more detailed aspects of the algorithms. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results. Thorough and intuitive, RNA Sequence, Structure, Function: Computational and Bioinformatic Methods aids scientists in continuing to study key methods and principles of RNA bioinformatics.
This book explores recent progress in RNA secondary, tertiary structure prediction, and its application from an expansive point of view. Because of advancements in experimental protocols and devices, the integration of new types of data as well as new analysis techniques is necessary, and this volume discusses additional topics that are closely related to RNA structure prediction, such as the detection of structure-disrupting mutations, high-throughput structure analysis, and 3D structure design. Written for the highly successful Methods in Molecular Biology series, chapters feature the kind of detailed implementation advice that leads to quality research results. Authoritative and practical, RNA Structure Prediction serves as a valuable guide for both experimental and computational RNA researchers.
"Functional RNA sequences typically have structural elements that are highly conserved during evolution. Here we present an algorithmic method for multiple alignment of RNAs, taking into consideration both structural similarity and sequence identity. Furthermore, we performed a comparative analysis on pairing probability matrices of a set of aligned orthologous sequences and predicted the conserved secondary structure. Our alignment method outperforms the most widely used multiple alignment tool - Clustal W, and the structure prediction approach we proposed can generate a more accurate secondary structure for 5S rRNA compared to the existing approaches such as Alifold. In addition, our algorithms are efficient in terms of CPU time and memory usage compared to most existing methods for secondary structure prediction." --