Download or read book Computational Analysis of the Interplay Between RNA Structure and Function written by Elan A. Shatoff and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: RNA is ubiquitous in the cellular environment, and it can function in innumerable ways with a variety of interaction partners. A RNA molecule's structure, in particular the set of base pairing interactions between the nucleotides of the molecule known as secondary structure, can help determine its function. Since most proteins can only bind to either single stranded or double stranded RNA, RNA secondary structure can also help determine where and how RNA-protein binding interactions occur. In this work I investigate computational models for RNA-protein interactions in a variety of different contexts. In Chapter 2 I probe the effect of single nucleotide variations on RNA-protein binding as mediated by RNA secondary structure. Single nucleotide variations are single nucleotide changes in an organism's genome that can often cause disease, and may do so through a number of different mechanisms. In this work we propose that sequence changes can affect accessibility to protein binding sites through changes in secondary structure, even when these sequence changes occur tens of nucleotides outside of protein binding sites. We find that single nucleotide variations can have a many fold effect on the binding affinity of proteins for RNA, and characterize the genome-wide effect of single nucleotide variations on HuR binding. HuR is a single-stranded RNA binding protein that binds to AU-rich sequences, and has links to diseases such as cancer. We also find an asymmetry in this effect for HuR, indicating that this effect may be under selection. Following the previous work, which utilizes a model incorporating single stranded RNA binding proteins into RNA secondary structure folding, I introduce a model for incorporating double stranded RNA binding proteins (dsRBPs) into RNA secondary structure partition function calculations in Chapter 3. The dsRBPs are an important but understudied class of proteins that have uses in a wide range of processes. We implement our model in the ViennaRNA package, and validate it by calculating a number of experimental observables for transactivation response element RNA-binding protein. We find that RNA secondary structure can have a many fold effect on the effective binding affinity of dsRBPs, and show that calculated affinities for pre-miRNA-like constructs correlate with experimentally measured processing rates. Our model provides a novel method for interrogating the interplay between dsRBPs and RNA secondary structure. In Chapter 4 I study RNA-protein interactions in a different context, and investigate the role of Shine-Dalgarno (SD) sequences in translation in the Bacteroidetes. The Bacteroidetes are a phylum of bacteria known to rarely use SD sequences, but after performing a survey of SD usage in the phylum we find that certain ribosomal protein genes utilize them, particularly rpsU. A cryo-electron microscopy structure of the ribosome from Flavobacterium johnsoniae, a member of the Bacteroidetes, also shows that S21, which is encoded by the ribosomal open reading frame rpsU, sequesters the anti-Shine-Dalgarno (ASD) sequence. In our survey of SD sequences we also find covariation between the SD sequence of rpsU and the ASD sequence. These observations suggest an autoregulatory model for S21 in the Bacteroidetes.

Download or read book Computational Analysis and Prediction of RNA protein Interactions written by Michael Uhl and published by . This book was released on 2022* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This dissertation is about the computational analysis and prediction of RNA-protein interactions. Ribonucleic acids (RNAs) and proteins both are essential for the control of gene expression in our cells. Gene expression is the process by which a functional gene product, namely a protein or an RNA, is produced from a gene, starting from the gene region on the DNA with the transcription of an RNA. Once regarded primarily as a messenger to transmit the protein information, recent years have seen RNA moving further into the biomedical spotlight, thanks to its increasingly uncovered roles in regulating gene expression. In addition, RNA has showcased its therapeutic potential, as famously demonstrated by the groundbreaking success of RNA vaccines in the COVID-19 pandemic. However, RNAs rarely function on their own: In humans, more than 1,500 different RNA-binding proteins (RBPs) are involved in controlling the various stages of an RNA's life cycle, creating a highly complex regulatory interplay between RNAs and proteins. It is therefore of fundamental importance to study these RNA-protein interactions, in order to deepen our understanding of gene expression. Over the last decade, CLIP-seq has become the dominant experimental method to identify the set of cellular RNA binding sites for an RBP of interest. However, analysing the resulting CLIP-seq data can be challenging, as there are many analysis steps and CLIP-seq protocol variants available, each requiring specific adaptations to the analysis workflow. Consequently, there is a need for analysis guidelines, providing easy access to tools, as well as the constant improvement of tools and workflows to increase the accuracy of the analysis results. The first set of works included in this thesis (publications P1, P4, and P5) deals with these topics, by providing a review article on CLIP-seq data analysis, as well as two articles on how to further improve CLIP-seq data analysis. Publication P1 supplies readers with an overview of tools and protocols, as well as guidelines to conduct a successful analysis, drawing largely from our own experience with analysing CLIP-seq data. Publication P4 demonstrates the issues current binding site identification tools have with CLIP-seq data from RBPs that bind to processed RNAs, and that the integration of RNA processing information improves the resulting binding site quality. On top of this, publication P5 presents Peakhood, the first tool that utilizes RNA processing information in order to increase the quality of RBP binding sites identified from CLIP-seq data. A natural drawback of experimental methods is that a target RNA needs to be sufficiently expressed in the observed cells for an RNA-protein interaction to be detected. Hence, since gene expression is a dynamic process that differs between cell types, time points, and conditions, a CLIP-seq experiment cannot recover the complete set of cellular RBP binding sites. This creates a demand for computational methods which can learn the binding properties of an RBP from existing CLIP-seq data, in order to predict RBP binding sites on any given target RNA. Besides interacting with proteins, RNAs can also interact with other RNAs, further increasing the amount of possible regulatory interactions between RNAs and proteins. In this regard, long non-coding RNAs (lncRNAs), a large class of non-protein-coding RNAs whose functions are still vastly unexplored, have become especially important, as it has been shown that they can engage in RNA-RNA interactions, whose regulatory mechanisms also include RNA-protein interactions. As such mechanistic studies are typically slow and expensive, computational tools that combine RNA-protein and RNA-RNA interaction predictions to infer potential mechanisms could be of great help, e.g., by screening a set of target RNAs and proteins and suggesting plausible mechanisms for experimental validation. The second set of works included in this thesis (publications P2 and P3) thus deals with the computational prediction of RNA-protein interactions, RNA-RNA interactions and the functional mechanisms that can be inferred from these interactions. Publication P2 introduces MechRNA, the first tool to infer functional mechanisms of lncRNAs based on their predicted interactions with RBPs and other RNAs, as well as gene expression data. We demonstrated MechRNA's capability to identify formerly described lncRNA mechanisms and experimentally validated one prediction, underlining its value for functional lncRNA studies. Finally, publication P3 presents RNAProt, a flexible and performant RBP binding site prediction tool based on recurrent neural networks. Compared to other popular deep learning methods, RNAProt achieves state-of-the-art predictive performance, as well as superior runtime efficiency. In addition, it is more feature-rich than any other available method, including the support of user-defined predictive features. We further showed that its visualizations agree with known RBP binding preferences, and demonstrated that its additional predictive features can increase the specificity of predictions

Download or read book Computational Analysis of Ribonucleic Acid Basepairs in RNA Structure and RNA RNA Interactions written by Daniel Lai and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book RNA 3D Structure Analysis and Prediction written by Neocles Leontis and published by Springer Science & Business Media. This book was released on 2012-06-05 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Characterization of Protein RNA Interactions and Implications for Phase Separation written by Alexandros Armaos and published by . This book was released on 2020 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite what was previously considered, the role of RNA is not only to carry the geneticinformation from DNA to proteins. Indeed, RNA has proven to be implicated in morecomplex cellular processes. Recent evidence suggests that transcripts have a regulatoryrole on gene expression and contribute to the spatial and temporal organization of theintracellular environment. They do so by interacting with RNA-binding proteins (RBPs)to form complex ribonucleoprotein (RNP) networks, however the key determinants thatgovern the formation of these complexes are still not well understood. In this work, I willdescribe algorithms that I developed to estimate the ability of RNAs to interact withproteins. Additionally, I will illustrate applications of computational methods to proposean alternative model for the function of Xist lncRNA and its protein network.Finally, I will show how computational predictions can be integrated with highthroughput approaches to elucidate the relationship between the structure of the RNA andits ability to interact with proteins. I conclude by discussing open questions and futureopportunities for computational analysis of cell's regulatory network.Overall, the underlying goal of my work is to provide biologists with new insights intothe functional association between RNAs and proteins as well as with sophisticated toolsthat will facilitate their investigation on the formation of RNP complexes.

Download or read book Computational Analysis and Annotation of Structurally Functional RNAs written by Milad Miladi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This work is a dissertation about computational methodologies and analyses of ribonucleic acid (RNA) molecules based on their sequence and structure properties. RNA is an essential molecule of living cells that acts as the career of the proteins genetic information and also as a regulatory functional element that contributes to cellular mechanisms. While only less than 3% of the human genome is encoding for known proteins, more than 85% of the genome is getting transcribed into RNA. Alone for the human genome, tens of thousands of non-coding RNA genes exist bearing pervasive functions. Despite the important roles of RNAs, functional and the regulatory mechanisms of a large number of the non-coding and protein-coding RNAs is either unknown or poorly understood. To solve this challenge, computational methodologies are a vital asset for a scalable and systematic analysis and annotation of RNAs with unknown functions. RNAs are polymer molecules that fold into complex structures within the cells. For a functional RNA, its folded structure often plays an important role and is better conserved than the polymer sequence through evolution. Therefore, it is essential to consider both the sequence and structure information for the task of annotation and discovery of functional RNAs using the computational approaches. Comparative methodologies utilise the evolutionary conservation information of both sequence and structure. They are pivot assets for providing reliable structure prediction and annotation of functional RNAs. Over the past decade, millions of RNA sequences have been obtained using techniques such as genomic screens and high-throughput sequencing experiments. These techniques produce up to several thousands or even millions of sequences and can be applied over all the domains of life. Analysing these large collections of sequences, for the evaluation and annotation of functional RNAs, demands efficient optimisation algorithms with sufficiently accurate models. Additionally, since the cells rely on heterogeneous molecules and mechanisms to function, integrative analysis of biological data is commonly required nowadays. Therefore, computational approaches based on techniques such as machine learning are needed to provide comprehensive strategies with high efficiencies also at different levels of the data. This thesis addresses some substantial challenges for the evaluation and annotation of functional RNAs by presenting novel contributions using computational analysis, optimisation algorithms, comparative methodologies, clustering approaches. The personal contributions are presented in the form of six works that are encompassed as six publications from three domains for the tasks of annotation, discovery, and analysis of functional RNAs. SPARSE and Pankov are two novel contributed algorithms for the problem of simultaneous alignment and folding (SA&F) of RNAs. SPARSE achieves a quadratic complexity without sequence-based heuristics by utilising a strong sparsification over the ensemble of possible secondary structure formations. The second SA&F algorithm Pankov, enables a fast simultaneous alignment and folding of RNAs while cohering to the nearest-neighbour thermodynamics principle of the standard RNA folding model. Pankov provides the most accurate SA&F probabilistic energy model until today, by mapping the nearest-neighbour principle to a Markov scheme using conditional in-loop probabilities. RNAscClust and GraphClust2 are presented for scalable clustering of RNA sequences based on sequence and structure. The RNAscClust methodology enables a linear-time clustering of paralogous RNAs based on their sequence and structure. Both tools are machine learning approaches that utilise graph kernel and locality-sensitive hashing schemes to support the clustering of input entries in an asymptotically linear time. RNAscClust incorporates orthogonal structure conservation to enhance the clustering and annotation performance. GraphClust2 is an integrative approach for the accessible and scalable clustering of RNAs to identify structurally conserved non-coding RNAs and motifs. GraphClust2 outperforms its predecessor and importantly supports diverse sources of genomic and experimental data in an accessible fashion. GraphClust2 bridges the gap between high-throughput sequencing experiments and the structure-based methodologies for functional RNA discovery. The final topic covered by this thesis is the mutational analysis of RNA secondary structure and function. A large-scale compilation and statistical analysis of somatic cancer synonymous mutations is presented. The analysis and experiments reveal that the synonymous mutations, despite not changing encoded protein sequence, can have substantial impacts on the gene expression levels and considerably disrupt the local secondary structure of mRNAs. Finally, MutaRNA is presented as an accessible web-based solution for evaluating the impact of mutation on the RNA secondary structure and visualising the complex impacts of the mutation on the intra-molecular interactions potentials in an intuitive manner

Download or read book Analyzing Microarray Gene Expression Data written by Geoffrey J. McLachlan and published by John Wiley & Sons. This book was released on 2005-02-18 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: A multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis of data derived from the latest DNA microarray technologies. Designed for biostatisticians entering the field of microarray analysis as well as biologists seeking to more effectively analyze their own experimental data, the text features a unique interdisciplinary approach and a combined academic and practical perspective that offers readers the most complete and applied coverage of the subject matter to date. Following a basic overview of the biological and technical principles behind microarray experimentation, the text provides a look at some of the most effective tools and procedures for achieving optimum reliability and reproducibility of research results, including: An in-depth account of the detection of genes that are differentially expressed across a number of classes of tissues Extensive coverage of both cluster analysis and discriminant analysis of microarray data and the growing applications of both methodologies A model-based approach to cluster analysis, with emphasis on the use of the EMMIX-GENE procedure for the clustering of tissue samples The latest data cleaning and normalization procedures The uses of microarray expression data for providing important prognostic information on the outcome of disease

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Download or read book Molecular Biology of The Cell written by Bruce Alberts and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book RNA protein Interactions written by Kiyoshi Nagai and published by Oxford University Press, USA. This book was released on 1994 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of RNA-protein interactions is crucial to understanding the mechanisms and control of gene expression and protein synthesis. The realization that RNAs are often far more biologically active than was previously appreciated has stimulated a great deal of new research in this field. Uniquely, in this book, the world's leading researchers have collaborated to produce a comprehensive and current review of RNA-protein interactions for all scientists working in this area. Timely, comprehensive, and authoritative, this new Frontiers title will be invaluable for all researchers in molecular biology, biochemistry and structural biology.

Download or read book Protein Nucleic Acid Interactions written by Phoebe A. Rice and published by Royal Society of Chemistry. This book was released on 2008-05-22 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides both in-depth background and up-to-date information in this area. The chapters are organized by general themes and principles, written by experts who illustrate topics with current findings. Topics covered include: - the role of ions and hydration in protein-nucleic acid interactions - transcription factors and combinatorial specificity - indirect readout of DNA sequence - single-stranded nucleic acid binding proteins - nucleic acid junctions and proteins, - RNA protein recognition - recognition of DNA damage. It will be a key reference for both advanced students and established scientists wishing to broaden their horizons.

Download or read book Structural Bioinformatics written by Jenny Gu and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 1105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structural Bioinformatics was the first major effort to show the application of the principles and basic knowledge of the larger field of bioinformatics to questions focusing on macromolecular structure, such as the prediction of protein structure and how proteins carry out cellular functions, and how the application of bioinformatics to these life science issues can improve healthcare by accelerating drug discovery and development. Designed primarily as a reference, the first edition nevertheless saw widespread use as a textbook in graduate and undergraduate university courses dealing with the theories and associated algorithms, resources, and tools used in the analysis, prediction, and theoretical underpinnings of DNA, RNA, and proteins. This new edition contains not only thorough updates of the advances in structural bioinformatics since publication of the first edition, but also features eleven new chapters dealing with frontier areas of high scientific impact, including: sampling and search techniques; use of mass spectrometry; genome functional annotation; and much more. Offering detailed coverage for practitioners while remaining accessible to the novice, Structural Bioinformatics, Second Edition is a valuable resource and an excellent textbook for a range of readers in the bioinformatics and advanced biology fields. Praise for the previous edition: "This book is a gold mine of fundamental and practical information in an area not previously well represented in book form." —Biochemistry and Molecular Education "... destined to become a classic reference work for workers at all levels in structural bioinformatics...recommended with great enthusiasm for educators, researchers, and graduate students." —BAMBED "...a useful and timely summary of a rapidly expanding field." —Nature Structural Biology "...a terrific job in this timely creation of a compilation of articles that appropriately addresses this issue." —Briefings in Bioinformatics

Download or read book RNA Sequence Structure and Function Computational and Bioinformatic Methods written by Jan Gorodkin and published by Humana. This book was released on 2014-03-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Transcriptome Data Analysis written by Yejun Wang and published by Humana. This book was released on 2019-03-20 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed volume provides comprehensive practical guidance on transcriptome data analysis for a variety of scientific purposes. Beginning with general protocols, the collection moves on to explore protocols for gene characterization analysis with RNA-seq data as well as protocols on several new applications of transcriptome studies. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and useful, Transcriptome Data Analysis: Methods and Protocols serves as an ideal guide to the expanding purposes of this field of study.

Download or read book DNA Modifications written by Alexey Ruzov and published by Humana. This book was released on 2020-08-22 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of methods and experimental protocols that are currently used to analyze the presence and abundance of non-canonical DNA nucleotides in different biological systems. Focusing particularly on the newly discovered and less studied DNA modifications that are enzymatically produced and are likely to play specific roles in various biological processes, the volume explores chromatography- and mass spectrometry-based techniques for the detection and quantification of DNA modifications, antibody-based approaches to study their spatial distribution in different cells and tissues, and methods to analyze their genomic distribution with the help of bioinformatics tools that interrogate the corresponding datasets. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and comprehensive, DNA Modifications: Methods and Protocols serves as an ideal guide to research scientists and PhD students in this rapidly developing discipline, and, thus, will ultimately contribute to deciphering the roles of non-canonical DNA nucleotides in different biological systems.

Download or read book Statistical Models written by David Freedman and published by Cambridge University Press. This book was released on 2009-04-27 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.

Download or read book RNA RNA Interactions written by Frank J. Schmidt and published by Humana Press. This book was released on 2014-10-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this volume expert researchers in the field detail many of the methods which are now commonly used to study RNA. These methods are presented as a guidebook to scientists who are experienced with RNA research and want to brush up on a new technique. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls. Thorough and intuitive, RNA-RNA Interactions: Methods and Protocols guides scientists investigating biological systems and studying RNA.