PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions

Statistical methods for biological sequence analysis for DNA binding motifs and protein contacts

Over the last decades a revolution in novel measurement techniques has permeated the biological sciences filling the databases with unprecedented amounts of data ranging from genomics, transcriptomics, proteomics and metabolomics to structural and ecological data. In order to extract insights from the vast quantity of data, computational and statistical methods are nowadays crucial tools in the toolbox of every biological researcher. In this thesis I summarize my contributions in two data-rich fields in biological sciences: transcription factor binding to DNA and protein structure prediction from protein sequences with shared evolutionary ancestry. In the first part of my thesis I introduce our work towards a web server for analysing transcription factor binding data with Bayesian Markov Models. In contrast to classical PWM or di-nucleotide models, Bayesian Markov models can capture complex inter-nucleotide dependencies that can arise from shape-readout and alternative binding modes. In addition to giving access to our methods in an easy-to-use, intuitive web-interface, we provide our users with novel tools and visualizations to better evaluate the biological relevance of the inferred binding motifs. We hope that our tools will prove useful for investigating weak and complex transcription factor binding motifs which cannot be predicted accurately with existing tools. The second part discusses a statistical attempt to correct out the phylogenetic bias arising in co-evolution methods applied to the contact prediction problem. Co-evolution methods have revolutionized the protein-structure prediction field more than 10 years ago, and, until very recently, have retained their importance as crucial input features to deep neural networks. As the co-evolution information is extracted from evolutionarily related sequences, we investigated whether the phylogenetic bias to the signal can be corrected out in a principled way using a variation of the Felsenstein's tree-pruning algorithm applied in combination with an independent-pair assumption to derive pairwise amino counts that are corrected for the evolutionary history. Unfortunately, the contact prediction derived from our corrected pairwise amino acid counts did not yield a competitive performance.2021-09-2

Introduction to protein folding for physicists

The prediction of the three-dimensional native structure of proteins from the knowledge of their amino acid sequence, known as the protein folding problem, is one of the most important yet unsolved issues of modern science. Since the conformational behaviour of flexible molecules is nothing more than a complex physical problem, increasingly more physicists are moving into the study of protein systems, bringing with them powerful mathematical and computational tools, as well as the sharp intuition and deep images inherent to the physics discipline. This work attempts to facilitate the first steps of such a transition. In order to achieve this goal, we provide an exhaustive account of the reasons underlying the protein folding problem enormous relevance and summarize the present-day status of the methods aimed to solving it. We also provide an introduction to the particular structure of these biological heteropolymers, and we physically define the problem stating the assumptions behind this (commonly implicit) definition. Finally, we review the 'special flavor' of statistical mechanics that is typically used to study the astronomically large phase spaces of macromolecules. Throughout the whole work, much material that is found scattered in the literature has been put together here to improve comprehension and to serve as a handy reference.Comment: 53 pages, 18 figures, the figures are at a low resolution due to arXiv restrictions, for high-res figures, go to http://www.pabloechenique.co

Computational Methods for Comparative Non-coding RNA Analysis: from Secondary Structures to Tertiary Structures

Unlike message RNAs (mRNAs) whose information is encoded in the primary sequences, the cellular roles of non-coding RNAs (ncRNAs) originate from the structures. Therefore studying the structural conservation in ncRNAs is important to yield an in-depth understanding of their functionalities. In the past years, many computational methods have been proposed to analyze the common structural patterns in ncRNAs using comparative methods. However, the RNA structural comparison is not a trivial task, and the existing approaches still have numerous issues in efficiency and accuracy. In this dissertation, we will introduce a suite of novel computational tools that extend the classic models for ncRNA secondary and tertiary structure comparisons. For RNA secondary structure analysis, we first developed a computational tool, named PhyloRNAalifold, to integrate the phylogenetic information into the consensus structural folding. The underlying idea of this algorithm is that the importance of a co-varying mutation should be determined by its position on the phylogenetic tree. By assigning high scores to the critical covariances, the prediction of RNA secondary structure can be more accurate. Besides structure prediction, we also developed a computational tool, named ProbeAlign, to improve the efficiency of genome-wide ncRNA screening by using high-throughput RNA structural probing data. It treats the chemical reactivities embedded in the probing information as pairing attributes of the searching targets. This approach can avoid the time-consuming base pair matching in the secondary structure alignment. The application of ProbeAlign to the FragSeq datasets shows its capability of genome-wide ncRNAs analysis. For RNA tertiary structure analysis, we first developed a computational tool, named STAR3D, to find the global conservation in RNA 3D structures. STAR3D aims at finding the consensus of stacks by using 2D topology and 3D geometry together. Then, the loop regions can be ordered and aligned according to their relative positions in the consensus. This stack-guided alignment method adopts the divide-and-conquer strategy into RNA 3D structural alignment, which has improved its efficiency dramatically. Furthermore, we also have clustered all loop regions in non-redundant RNA 3D structures to de novo detect plausible RNA structural motifs. The computational pipeline, named RNAMSC, was extended to handle large-scale PDB datasets, and solid downstream analysis was performed to ensure the clustering results are valid and easily to be applied to further research. The final results contain many interesting variations of known motifs, such as GNAA tetraloop, kink-turn, sarcin-ricin and t-loops. We also discovered novel functional motifs that conserved in a wide range of ncRNAs, including ribosomal RNA, sgRNA, SRP RNA, GlmS riboswitch and twister ribozyme

Molecular Dynamics of a kB DNA Element: Base Flipping via Cross-strand Intercalative Stacking in a Microsecond-scale Simulation

The sequence-dependent structural variability and conformational dynamics of DNA play pivotal roles in many biological milieus, such as in the site-specific binding of transcription factors to target regulatory elements. To better understand DNA structure, function, and dynamics in general, and protein-DNA recognition in the 'kB' family of genetic regulatory elements in particular, we performed molecular dynamics simulations of a 20-base pair DNA encompassing a cognate kB site recognized by the proto-oncogenic 'c-Rel' subfamily of NF-kB transcription factors. Simulations of the kB DNA in explicit water were extended to microsecond duration, providing a broad, atomically-detailed glimpse into the structural and dynamical behavior of double helical DNA over many timescales. Of particular note, novel (and structurally plausible) conformations of DNA developed only at the long times sampled in this simulation -- including a peculiar state arising at ~ 0.7 us and characterized by cross-strand intercalative stacking of nucleotides within a longitudinally-sheared base pair, followed (at ~ 1 us) by spontaneous base flipping of a neighboring thymine within the A-rich duplex. Results and predictions from the us-scale simulation include implications for a dynamical NF-kB recognition motif, and are amenable to testing and further exploration via specific experimental approaches that are suggested herein.Comment: 21 pages, 9 figures; revised version has been updated to include figure

Genome-wide Determination Of Splicing Efficiency And Dynamics From RNA-Seq Data

Eukaryotic genes are mostly composed of a series of exons intercalated by sequences with no coding potential called introns. These sequences are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing, it is important to quantify splicing efficiency. In this thesis, I introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from RNA-Seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements, such as exons from other genes. I demonstrate SPLICE-q’s application using three use cases including two different species and methodologies. These analyses illustrate that SPLICE-q can detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-Seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. Furthermore, I provide an in-depth study of time course nascent BrU-Seq data to address questions concerning differences in the speed of splicing and the underlying biological features that might be associated with it. SPLICE-q and its documentation are publicly available at: https://github.com/vrmelo/SPLICE-q.Eukaryotische Gene bestehen im Wesentlichen aus einer Reihe von Exons, die durch nicht-kodierende Sequenzen (so genannte Introns) getrennt sind. In einem posttranskriptionellen Prozess, der als Splicing bzw. Spleißen bezeichnet wird, werden diese Sequenzen üblicherweise aus den primären Transkripten entfernt, sodass reife RNA Moleküle entstehen. Effizientes Splicing der primären Transkripte ist ein derart essenzieller Schritt in der Expression von Genen, dass dessen Deregulation Ursache zahlreicher Erkrankungen des menschlichen Körpers ist. Deswegen ist es wichtig die Effizienz des Spleißens robust quantifizieren zu können, um die Dynamik dieses Prozesses und die Auswirkungen der aberranten Prozessierung von Transkripten besser zu verstehen. In diesem Manuskript präsentiere ich SPLICE-q, ein effizientes und benutzerfreundliches Pythonprogramm zur genomweiten Quantifizierung von Spleißeffizienzen (SPLICing Efficiency quantification). Es unterstützt u.a. Studien, die den Effekt von Spleißeffizienz auf die generelle Dynamik der Transkriptprozessierung untersuchen. SPLICE-q benutzt alignierte Reads aus RNA-Seq Experimenten, um die Spleißeffizienz für jedes einzelne Intron zu quantifizieren und erlaubt es dem Benutzer Introns in mehreren unterschiedlich restriktiven Stufen nach deren Überlapp mit anderen genomischen Elementen (bspw. Exons aus anderen Genen) zu filtern. Die Verwendung und Robustheit von SPLICE-q wird anhand von drei verschiedenen Anwendungsbeispielen, inkl. zweier unterschiedlicher Spezies und Methodologien, gezeigt. Diese Analysen demonstrieren, dass SPLICE-q in der Lage ist sowohl, anhand von Daten eines nascent RNA Experiments, einen progressiven Anstieg der Spleißeffizienz über die Zeit festzustellen, als auch zum Verständnis der Entwicklung von Krebszellen, über die bloße Genexpression hinaus, beizutragen. Darüber hinaus, untersucht diese Arbeit eine Zeitreihe aus nascent BrU-Seq-Daten im Detail, um Fragestellungen bzgl. Differenzen in der Spleißgeschwindigkeit in Verbindung mit gewissen biologischen Merkmalen zu klären. Der Quellcode von SPLICE-q und dessen Dokumentation sind öffentlich zugänglich unter: https://github.com/vrmelo/SPLICE-q

RNA structure analysis : algorithms and applications

In this doctoral thesis, efficient algorithms for aligning RNA secondary structures and mining unknown RNA motifs are presented. As the major contribution, a structure alignment algorithm, which combines both primary and secondary structure information, can find the optimal alignment between two given structures where one of them could be either a pattern structure of a known motif or a real query structure and the other be a subject structure. Motivated by widely used algorithms for RNA folding, the proposed algorithm decomposes an RNA secondary structure into a set of atomic structural components that can be further organized in a tree model to capture the structural particularities. The novel structure alignment algorithm is implemented using dynamic programming techniques coupled by position-independent scoring matrices. The algorithm can find the optimal global and local alignments between two RNA secondary structures at quadratic time complexity. When applied to searching a structure database, the algorithm can find similar RNA substructures and therefore can be used to identify functional RNA motifs. Extension of the algorithm has also been accomplished to deal with position-dependent scoring matrix in the purpose of aligning multiple structures. All algorithms have been implemented in a package under the name RSmatch and applied to searching mRNA UTR structure database and mining RNA motifs. The experimental results showed high efficiency and effectiveness of the proposed techniques

A bioinformatics framework for RNA structure mining, motif discovery and polyadenylation analysis

The RNA molecules play various important roles in the cell and their functionality depends not only on the sequence information but to a large extent on their structure. The development of computational and predictive approaches to study RNA molecules is extremely valuable. In this research, a tool named RADAR was developed that provides a multitude of functionality for RNA data analysis and research. It aligns structure annotated RNA sequences so that both the sequence as well as structure information is taken into consideration. This tool is capable of performing pair-wise structure alignment, multiple structure alignment, database search and clustering. In addition, it provides two salient features: (i) constrained alignment of RNA secondary structures, and (ii) prediction of consensus structure for a set of RNA sequences. This tool is also hosted on the web and can be freely accessed and the software can be downloaded from http://datalab.njitedu/biodata/rna/RSmatch/server.htm . The RADAR software has been applied to various datasets (genomes of various mammals, viruses and parasites) and our experimental results show that this approach is capable of detecting functionally important regions. As an application of RADAR, a systematic data mining approach was developed, termed GLEAN-UTR, to identify small stem loop RNA structure elements in the Untranslated regions (UTRs) that are conserved between human and mouse orthologs and exist in multiple genes with common Gene Ontology terms. This study resulted in 90 distinct RNA structure groups containing 748 structures, with 3\u27 Histone stem loop (HSL3) and Iron Response element (IRE) among the top hits. Further, the role played by structure in mRNA polyadenylation was investigated. Polyadenylation is an important step towards the maturation of almost all cellular mRNAs in eukaryotes. Studies have identified several cis-elements besides the widely known polyadenylation signal (PAS) element (AATAAA or ATTAAA or a close variant) which may have a role to play in poly(A) site identification. In this study the differences in structural stability of sequences surrounding poly(A) sites was investigated and it was found that for the genes containing single poly(A) site, the surrounding sequence is most stable as compared with the surrounding sequences for alternative poly(A) sites. This indicates that structure may be providing a evolutionary advantage for single poly(A) sites that prevents multiple poly(A) sites from arising. In addition the study found that the structural stability of the region surrounding a polyadenylation site correlates with its distance from the next gene. The shortest distance corresponding to a greater structural stability

Structural approaches to protein sequence analysis

Various protein sequence analysis techniques are described, aimed at improving the prediction of protein structure by means of pattern matching. To investigate the possibility that improvements in amino acid comparison matrices could result in improvements in the sensitivity and accuracy of protein sequence alignments, a method for rapidly calculating amino acid mutation data matrices from large sequence data sets is presented. The method is then applied to the membrane-spanning segments of integral membrane proteins in order to investigate the nature of amino acid mutability in a lipid environment. Whilst purely sequence analytic techniques work well for cases where some residual sequence similarity remains between a newly characterized protein and a protein of known 3-D structure, in the harder cases, there is little or no sequence similarity with which to recognize proteins with similar folding patterns. In the light of these limitations, a new approach to protein fold recognition is described, which uses a statistically derived pairwise potential to evaluate the compatibility between a test sequence and a library of structural templates, derived from solved crystal structures. The method, which is called optimal sequence threading, proves to be highly successful, and is able to detect the common TIM barrel fold between a number of enzyme sequences, which has not been achieved by any previous sequence analysis technique. Finally, a new method for the prediction of the secondary structure and topology of membrane proteins is described. The method employs a set of statistical tables compiled from well-characterized membrane protein data, and a novel dynamic programming algorithm to recognize membrane topology models by expectation maximization. The statistical tables show definite biases towards certain amino acid species on the inside, middle and outside of a cellular membrane