ncRNA-Agents : anotação de RNAs não-codificadores baseada em sistema multiagente

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.Os RNAs não-codificadores (ncRNAs) constituem um importante subconjunto dos transcritos produzidos nas células dos organismos, pois afetam diversos processos celulares. Embora existam métodos computacionais bastante eficazes para identificar proteínas, a anotação de ncRNAs é hoje objeto de pesquisa intensa, pois suas características e sinais não são ainda completamente conhecidos. Neste contexto, nesta tese, apresentamos uma arquitetura para anotação de ncRNAs baseada no paradigma de Sistema Multiagente. A implementação do sistema, denominado de ncRNA-Agents, usa agentes colaborativos, em que cada agente tem conhecimento e raciocínio (simulando os de biólogos) sobre um aspecto específico de RNA, o que contribui para uma anotação curada de ncRNA, com qualidade associada e explicações baseadas nos resultados das ferramentas usadas pelo sistema para recomendar a anotação. Além disso, foram realizados três estudos de casos com os fungos Saccharomyces cerevisiae, Paracoccidioides brasilienses e Schizosaccharomyces pombe, para avaliar o desempenho do sistema quanto a sua capacidade de anotar ncRNAs conhecidos e de predizer novos ncRNAs. Acesso público a esta ferramenta está em http://www.biomol.unb.br/ncrna-agents.Non-coding RNAs (ncRNAs) are an important subset of the transcripts produced in the cells of organisms, since they affect many cellular processes. Although there are efficient and fast computational methods to identify proteins, annotation of ncRNAs is now focus of intensive research once their characteristics and signals are not yet entirely known. In this context, in this thesis, we present an architecture for ncRNAs annotation based on the multi-agent system paradigm. The implementation of a system, called ncRNA-Agents, uses collaborative agents, where each agent has knowledge and reasonig (simulating biologists) about a specific aspect of RNA, which contributes to a curated ncRNA annotation, with associated quality and explanations based on the results of the tools used by the system to recommend the annotation. In addition, we performed three case studies with three fungi, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Paracoccidioides brasiliensis, to evaluate the performance of the system and its ability to annotate known ncRNAs and predict new ncRNAs. This tool is publicly available at http://www.biomol.unb.br/ncrna-agents

Strategies for the intelligent integration of genetic variance information in multiscale models of neurodegenerative diseases

A more complete understanding of the genetic architecture of complex traits and diseases can maximize the utility of human genetics in disease screening, diagnosis, prognosis, and therapy. Undoubtedly, the identification of genetic variants linked to polygenic and complex diseases is of supreme interest for clinicians, geneticists, patients, and the public. Furthermore, determining how genetic variants affect an individual’s health and transmuting this knowledge into the development of new medicine can revolutionize the treatment of most common deleterious diseases. However, this requires the correlation of genetic variants with specific diseases, and accurate functional assessment of genetic variation in human DNA sequencing studies is still a nontrivial challenge in clinical genomics. Assigning functional consequences and clinical significances to genetic variants is an important step in human genome interpretation. The translation of the genetic variants into functional molecular mechanisms is essential in disease pathogenesis and, eventually in therapy design. Although various statistical methods are helpful to short-list the genetic variants for fine-mapping investigation, demonstrating their role in molecular mechanism requires knowledge of functional consequences. This undoubtedly requires comprehensive investigation. Experimental interpretation of all the observed genetic variants is still impractical. Thus, the prediction of functional and regulatory consequences of the genetic variants using in-silico approaches is an important step in the discovery of clinically actionable knowledge. Since the interactions between phenotypes and genotypes are multi-layered and biologically complex. Such associations present several challenges and simultaneously offer many opportunities to design new protocols for in-silico variant evaluation strategies. This thesis presents a comprehensive protocol based on a causal reasoning algorithm that harvests and integrates multifaceted genetic and biomedical knowledge with various types of entities from several resources and repositories to understand how genetic variants perturb molecular interaction, and initiate a disease mechanism. Firstly, as a case study of genetic susceptibility loci of Alzheimer’s disease, I reviewed and summarized all the existing methodologies for Genome Wide Association Studies (GWAS) interpretation, currently available algorithms, and computable modelling approaches. In addition, I formulated a new approach for modelling and simulations of genetic regulatory networks as an extension of the syntax of the Biological Expression Language (OpenBEL). This could allow the representation of genetic variation information in cause-and-effect models to predict the functional consequences of disease-associated genetic variants. Secondly, by using the new syntax of OpenBEL, I generated an OpenBEL model for Alzheimer´s Disease (AD) together with genetic variants including their DNA, RNA or protein position, variant type and associated allele. To better understand the role of genetic variants in a disease context, I subsequently tried to predict the consequences of genetic variation based on the functional context provided by the network model. I further explained that how genetic variation information could help to identify candidate molecular mechanisms for aetiologically complex diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Though integration of genetic variation information can enhance the evidence base for shared pathophysiology pathways in complex diseases, I have addressed to one of the key questions, namely the role of shared genetic variants to initiate shared molecular mechanisms between neurodegenerative diseases. I systematically analysed shared genetic variation information of AD and PD and mapped them to find shared molecular aetiology between neurodegenerative diseases. My methodology highlighted that a comprehensive understanding of genetic variation needs integration and analysis of all omics data, in order to build a joint model to capture all datasets concurrently. Moreover genomic loci should be considered to investigate the effects of GWAS variants rather than an individual genetic variant, which is hard to predict in a biologically complex molecular mechanism, predominantly to investigate shared pathology

Doctor of Philosophy

dissertationSuccessful molecular diagnosis using an exome sequence hinges on accurate association of damaging variants to the patient's phenotype. Unfortunately, many clinical scenarios (e.g., single affected or small nuclear families) have little power to confidently identify damaging alleles using sequence data alone. Today's diagnostic tools are simply underpowered for accurate diagnosis in these situations, limiting successful diagnoses. In response, clinical genetics relies on candidate-gene and variant lists to limit the search space. Despite their practical utility, these lists suffer from inherent and significant limitations. The impact of false negatives on diagnostic accuracy is considerable because candidate-genes and variants lists are assembled ad hoc, choosing alleles based upon expert knowledge. Alleles not in the list are not considered-ending hope for novel discoveries. Rational alternatives to ad hoc assemblages of candidate lists are thus badly needed. In response, I created Phevor, the Phenotype Driven Variant Ontological Re-ranking tool. Phevor works by combining knowledge resident in biomedical ontologies, like the human phenotype and gene ontologies, with the outputs of variant-interpretation tools such as SIFT, GERP+, Annovar and VAAST. Phevor can then accurately to prioritize candidates identified by third-party variant-interpretation tools in light of knowledge found in the ontologies, effectively bypassing the need for candidate-gene and variant lists. Phevor differs from tools such as Phenomizer and Exomiser, as it does not postulate a set of fixed associations between genes and phenotypes. Rather, Phevor dynamically integrates knowledge resident in multiple bio-ontologies into the prioritization process. This enables Phevor to improve diagnostic accuracy for established diseases and previously undescribed or atypical phenotypes. Inserting known disease-alleles into otherwise healthy exomes benchmarked Phevor. Using the phenotype of the known disease, and the variant interpretation tool VAAST (Variant Annotation, Analysis and Search Tool), Phevor can rank 100% of the known alleles in the top 10 and 80% as the top candidate. Phevor is currently part of the pipeline used to diagnose cases as part the Utah Genome Project. Successful diagnoses of several phenotypes have proven Phevor to be a reliable diagnostic tool that can improve the analysis of any disease-gene search

Expanding the repertoire of bacterial (non-)coding RNAs

The detection of non-protein-coding RNA (ncRNA) genes in bacteria and their diverse regulatory mode of action moved the experimental and bio-computational analysis of ncRNAs into the focus of attention. Regulatory ncRNA transcripts are not translated to proteins but function directly on the RNA level. These typically small RNAs have been found to be involved in diverse processes such as (post-)transcriptional regulation and modification, translation, protein translocation, protein degradation and sequestration. Bacterial ncRNAs either arise from independent primary transcripts or their mature sequence is generated via processing from a precursor. Besides these autonomous transcripts, RNA regulators (e.g. riboswitches and RNA thermometers) also form chimera with protein-coding sequences. These structured regulatory elements are encoded within the messenger RNA and directly regulate the expression of their “host” gene. The quality and completeness of genome annotation is essential for all subsequent analyses. In contrast to protein-coding genes ncRNAs lack clear statistical signals on the sequence level. Thus, sophisticated tools have been developed to automatically identify ncRNA genes. Unfortunately, these tools are not part of generic genome annotation pipelines and therefore computational searches for known ncRNA genes are the starting point of each study. Moreover, prokaryotic genome annotation lacks essential features of protein-coding genes. Many known ncRNAs regulate translation via base-pairing to the 5’ UTR (untranslated region) of mRNA transcripts. Eukaryotic 5’ UTRs have been routinely annotated by sequencing of ESTs (expressed sequence tags) for more than a decade. Only recently, experimental setups have been developed to systematically identify these elements on a genome-wide scale in prokaryotes. The first part of this thesis, describes three experimental surveys of exploratory field studies to analyze transcript organization in pathogenic bacteria. To identify ncRNAs in Pseudomonas aeruginosa we used a combination of an experimental RNomics approach and ncRNA prediction. Besides already known ncRNAs we identified and validated the expression of six novel RNA genes. Global detection of transcripts by next generation RNA sequencing techniques unraveled an unexpectedly complex transcript organization in many bacteria. These ultra high-throughput methods give us the appealing opportunity to analyze the complete RNA output of any species at once. The development of the differential RNA sequencing (dRNA-seq) approach enabled us to analyze the primary transcriptome of Helicobacter pylori and Xanthomonas campestris. For the first time we generated a comprehensive and precise transcription start site (TSS) map for both species and provide a general framework for the analysis of dRNA-seq data. Focusing on computer-aided analysis we developed new tools to annotate TSS, detect small protein-coding genes and to infer homology of newly detected transcripts. We discovered hundreds of TSS in intergenic regions, upstream of protein-coding genes, within operons and antisense to annotated genes. Analysis of 5’ UTRs (spanning from the TSS to the start codon of the adjacent protein-coding gene) revealed an unexpected size diversity ranging from zero to several hundred nucleotides. We identified and validated the expression of about 60 and about 20 ncRNA candidates in Helicobacter and Xanthomonas, respectively. Among these ncRNA candidates we found several small protein-coding genes that have previously evaded annotation in both species. We showed that the combination of dRNA-seq and computational analysis is a powerful method to examine prokaryotic transcriptomes. Experimental setups are time consuming and often combined with huge costs. Another limitation of experimental approaches is that genes which are expressed in specific developmental stages or stress conditions are likely to be missed. Bioinformatic tools build an alternative to overcome such restraints. General approaches usually depend on comparative genomic data and evolutionary signatures are used to analyze the (non-)coding potential of multiple sequence alignments. In the second part of my thesis we present our major update of the widely used ncRNA gene finder RNAz and introduce RNAcode, an efficient tool to asses local protein-coding potential of genomic regions. RNAz has been successfully used to identify structured RNA elements in all domains of life. However, our own experience and the user feedback not only demonstrated the applicability of the RNAz approach, but also helped us to identify limitations of the current implementation. Using a much larger training set and a new classification model we significantly improved the prediction accuracy of RNAz. During transcriptome analysis we repeatedly identified small protein-coding genes that have not been annotated so far. Only a few of those genes are known to date and standard proteincoding gene finding tools suffer from the lack of training data. To avoid an excess of false positive predictions, gene finding software is usually run with an arbitrary cutoff of 40-50 amino acids and therefore misses the small sized protein-coding genes. We have implemented RNAcode which is optimized for emerging applications not covered by standard protein-coding gene annotation software. In addition to complementing classical protein gene annotation, a major field of application of RNAcode is the functional classification of transcribed regions. RNA sequencing analyses are likely to falsely report transcript fragments (e.g. mRNA degradation products) as non-coding. Hence, an evaluation of the protein-coding potential of these fragments is an essential task. RNAcode reports local regions of high coding potential instead of complete protein-coding genes. A training on known protein-coding sequences is not necessary and RNAcode can therefore be applied to any species. We showed this with our analysis of the Escherichia coli genome where the current annotation could be accurately reproduced. We furthermore identified novel small protein-coding genes with RNAcode in this extensively studied genome. Using transcriptome and proteome data we found compelling evidence that several of the identified candidates are bona fide proteins. In summary, this thesis clearly demonstrates that bioinformatic methods are mandatory to analyze the huge amount of transcriptome data and to identify novel (non-)coding RNA genes. With the major update of RNAz and the implementation of RNAcode we contributed to complete the repertoire of gene finding software which will help to unearth hidden treasures of the RNA World

Uma ferramenta multiagente baseada em conhecimento para anotação de proteínas : um estudo de caso para o Fungo Saccharomyces cerevisiae

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciências da Computação, 2014.Identificar funções biológicas das sequências é uma atividade chave em projetos genomas. Esta tarefa é realizada na etapa de anotação, que possui duas fases. Na fase manual, biólogos utilizam seu conhecimento e experiência determinar a função de cada sequência, baseada nos resultados produzidos pela fase automática, onde ferramentas e bancos de dados são utilizados para predizer uma anotação funcional. Esta dissertação propõe BioAgents-Prot, uma ferramenta multiagente baseada em conhecimento, que simula o conhecimento e experiência dos biólogos para anotação de proteínas. BioAgents-Prot foi definido com uma abordagem de agentes cooperativos, onde diferentes agentes especializados trabalham em conjunto na tentativa de sugerir uma anotação manual adequada. A arquitetura proposta em três camadas foi desenvolvida com Java Agent DEvelopment Framework - JADE e Drools, um motor de inferência baseado em regras. Para avaliar o desempenho do BioAgents-Prot, as anotações dos transcritos do fungo Saccharomyces cerevisiae foram comparadas com as anotações sugeridas pelo sistema. Usando regras básicas que representam o raciocínio de anotação, obtemos 95.84% de sensibilidade, 93.22% de especificidade, 98.40% de F1-score e 0.80 de MCC, que demonstram a utilidade do BioAgents-Prot na etapa de anotação em projetos transcritoma.Identifying biological function of sequences is a key activity in genome projects. This task is done in the annotation step, which has two phases. In the manual phase, biologists use their knowledge and experience to determine the function for each sequence, based on the results produced by the automatic phase, where tools and data bases are used to predict functional annotation. This dissertation presents BioAgents-Prot, a knowledge based multiagent tool, which simulates biologists expertise to annotate proteins. BioAgents-Prot is defined with an approach of cooperative agents, where specialized intelligent agents work together to suggest proper manual annotation. The proposed three-layer architecture was implemented with Java Agent DEvelopment Framework-JADE and Drools (a rule-based inference engine). To assess performance, transcript annotations of the Saccharomyces cerevisiae fungus were compared to the annotations suggested by BioAgents-Prot. Using basic rules that represents the annotation reasoning, we obtained 95.84% of sensitivity, 93.22% of specificity, 98.40% of F1-score and 0.80 of MCC, which shows the usefulness of BioAgents-Prot in annotation step of transcriptome projects

Using machine learning to predict pathogenicity of genomic variants throughout the human genome

Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

RNA, the Epicenter of Genetic Information

The origin story and emergence of molecular biology is muddled. The early triumphs in bacterial genetics and the complexity of animal and plant genomes complicate an intricate history. This book documents the many advances, as well as the prejudices and founder fallacies. It highlights the premature relegation of RNA to simply an intermediate between gene and protein, the underestimation of the amount of information required to program the development of multicellular organisms, and the dawning realization that RNA is the cornerstone of cell biology, development, brain function and probably evolution itself. Key personalities, their hubris as well as prescient predictions are richly illustrated with quotes, archival material, photographs, diagrams and references to bring the people, ideas and discoveries to life, from the conceptual cradles of molecular biology to the current revolution in the understanding of genetic information. Key Features Documents the confused early history of DNA, RNA and proteins - a transformative history of molecular biology like no other. Integrates the influences of biochemistry and genetics on the landscape of molecular biology. Chronicles the important discoveries, preconceptions and misconceptions that retarded or misdirected progress. Highlights major pioneers and contributors to molecular biology, with a focus on RNA and noncoding DNA. Summarizes the mounting evidence for the central roles of non-protein-coding RNA in cell and developmental biology. Provides a thought-provoking retrospective and forward-looking perspective for advanced students and professional researchers

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Functional large non-coding RNAs in mammals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.It is now clear that RNA is more than a messenger and performs vast and diverse functions. These functional RNAs include the ribosomal, transfer, and splicing-associated RNAs along with a cast of tiny RNAs, including microRNAs and other families. In addition to these classic examples, there were a handful of known functional large ncRNAs that play important biological roles. To identify additional functional large ncRNAs we exploited a chromatin signature of actively transcribed genes to define discrete transcriptional units that do not overlap any known proteincoding genes. Using this approach we identified -3,500 transcriptional units in the human and mouse genomes that produce multi-exonic RNAs that lack any coding potential. We termed these large intergenic non-coding RNAs (lincRNAs). Importantly, these lincRNAs exhibit strong purifying selection across various mammalian genomes. To determine whether the lincRNA transcripts themselves have biological functions, we undertook systematic loss-of-function experiments on most lincRNAs defined in mouse embryonic stem cells (ESCs). We showed that knockdown of the vast majority of ESC-expressed lincRNAs has a strong effect on gene expression patterns in ESCs, of comparable magnitude to that seen for the well-known ESC regulatory proteins. We identify dozens of lincRNAs that upon loss-of-function cause an exit from the pluripotent state and dozens of additional lincRNAs that, while not essential for the maintenance of pluripotency, act to repress lineage-specific gene expression programs in ESCs. Despite their important functional roles, how lincRNAs exert their influence was unknown. We showed that many lincRNAs physically interact with the Polycomb Repressive Complex. We systematically analyzed chromatin-modifying proteins that have been shown to play critical roles in ESCs and identified 11 additional chromatin complexes that physically interact with the ESC lincRNAs. Altogether, we found that -30% of the ESC lincRNAs are associated with multiple chromatin complexes. These interactions are important for proper regulation of gene expression programs in ES cells. Our data suggests a model whereby a distinct set of lincRNAs is transcribed in a cell type and interacts with ubiquitous regulatory protein complexes to give rise to cell-type-specific RNA-protein complexes that coordinate cell-type specific gene expression programs.by Mitchell Guttman.Ph.D