Bioinformatics for High-throughput Virus Detection and Discovery

Pathogen detection is a challenging problem given that any given specimen may contain one or more of many different microbes. Additionally, a specimen may contain microbes that have yet to be discovered. Traditional diagnostics are ill-equipped to address these challenges because they are focused on the detection of a single agent or panel of agents. I have developed three innovative computational approaches for analyzing high-throughput genomic assays capable of detecting many microbes in a parallel and unbiased fashion. The first is a metagenomic sequence analysis pipeline that was initially applied to 12 pediatric diarrhea specimens in order to give the first ever look at the diarrhea virome. Metagenomic sequencing and subsequent analysis revealed a spectrum of viruses in these specimens including known and highly divergent viruses. This metagenomic survey serves as a basis for future investigations about the possible role of these viruses in disease. The second tool I developed is a novel algorithm for diagnostic microarray analysis called VIPR: Viral Identification with a PRobabilistic algorithm). The main advantage of VIPR relative to other published methods for diagnostic microarray analysis is that it relies on a training set of empirical hybridizations of known viruses to guide future predictions. VIPR uses a Bayesian statistical framework in order to accomplish this. A set of hemorrhagic fever viruses and their relatives were hybridized to a total of 110 microarrays in order to test the performance of VIPR. VIPR achieved an accuracy of 94% and outperformed existing approaches for this dataset. The third tool I developed for pathogen detection is called VIPR HMM. VIPR HMM expands upon VIPR\u27s previous implementation by incorporating a hidden Markov model: HMM) in order to detect recombinant viruses. VIPR HMM correctly identified 95% of inter-species breakpoints for a set of recombinant alphaviruses and flaviviruses Mass sequencing and diagnostic microarrays require robust computational tools in order to make predictions regarding the presence of microbes in specimens of interest. High-throughput diagnostic assays coupled with powerful analysis tools have the potential to increase the efficacy with which we detect pathogens and treat disease as these technologies play more prominent roles in clinical laboratories

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

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Sequence and structural analysis of antibodies

The work presented in this thesis focusses on the sequence and structural analysis of antibodies and has fallen into three main areas. First I developed a method to assess how typical an antibody sequence is of the expressed human antibody repertoire. My hypothesis was that the more \humanlike" an antibody sequence is (in other words how typical it is of the expressed human repertoire), the less likely it is to elicit an immune response when used in vivo in humans. In practice, I found that, while the most and least-human sequences generated the lowest and highest anti-antibody reponses in the small available dataset, there was little correlation in between these extremes. Second, I examined the distribution of the packing angles between VH and VL domains of antibodies and whether residues in the interface in uence the packing angle angle. This is an important factor which has essentially been ignored in modelling antibody structures since the packing angle can have a signi�cant e�ect on the topography of the combining site. Finding out which interface residues have the greatest in uence is also important in protocols for `humanizing' mouse antibodies to make them more suitable for use in therapy in humans. Third, I developed a method to apply standard Kabat or Chothia numbering schemes to an antibody sequence automatically. In brief, the method uses pro�les to identify the ends of the framework regions and then �lls in the numbers for each section. Benchmarking the performance of this algorithm against annotations in the Kabat database highlighted several errors in the manual annotations in the Kabat database. Based on structural analysis of insertions and deletions in the framework regions of antibodies, I have extended the Chothia numbering scheme to identify the structurally correct positions of insertions and deletions in the framework regions

Development of a recommendation system for scientific literature based on deep learning

Dissertação de mestrado em BioinformaticsThe previous few decades have seen an enormous volume of articles from the scientific commu nity on the most diverse biomedical topics, making it extremely challenging for researchers to find relevant information. Methods like Machine Learning (ML) and Deep Learning (DL) have been used to create tools that can speed up this process. In that context, this work focuses on examining the performance of different ML and DL techniques when classifying biomedical documents, mainly regarding their relevance to given topics. To evaluate the different techniques, the dataset from the BioCreative VI Track 4 challenge was used. The objective of the challenge was to identify documents related to protein-protein interactions altered by mutations, a topic extremely important in precision medicine. Protein-protein interactions play a crucial role in the cellular mechanisms of all living organisms, and mutations in these interaction sites could be indicative of diseases. To handle the data to be used in training, some text processing methods were implemented in the Omnia package from OmniumAI, the host company of this work. Several preprocessing and feature extraction methods were implemented, such as removing stopwords and TF-IDF, which may be used in other case studies. They can be used either with generic text or biomedical text. These methods, in conjunction with ML pipelines already developed by the Omnia team, allowed the training of several traditional ML models. We were able to achieve a small improvement on performance, compared to the challenge baseline, when applying these traditional ML models on the same dataset. Regarding DL, testing with a CNN model, it was clear that the BioWordVec pre-trained embedding achieved the best performance of all pre-trained embeddings. Additionally, we explored the application of more complex DL models. These models achieved a better performance than the best challenge submission. BioLinkBERT managed an improvement of 0.4 percent points on precision, 4.9 percent points on recall, and 2.2 percent points on F1.As décadas anteriores assistiram a um enorme aumento no volume de artigos da comunidade científica sobre os mais diversos tópicos biomédicos, tornando extremamente difícil para os investigadores encontrar informação relevante. Métodos como Aprendizagem Máquina (AM) e Aprendizagem Profunda (AP) tem sido utilizados para criar ferramentas que podem acelerar este processo. Neste contexto, este trabalho centra-se na avaliação do desempenho de diferentes técnicas de AM e AP na classificação de documentos biomédicos, principalmente no que diz respeito à sua relevância para determinados tópicos. Para avaliar as diferentes técnicas, foi utilizado o conjunto de dados do desafio BioCreative VI Track 4. O objectivo do desafio era identificar documentos relacionados com as interações proteína-proteína alteradas por mutações, um tópico extremamente importante na medicina de precisão. As interacções proteína-proteína desempenham um papel crucial nos mecanismos celulares de todos os organismos vivos, e as mutações nestes locais de interacção podem ser indicativas de doenças. Para tratar os dados a utilizar no treino, alguns métodos de processamento de texto foram implementados no pacote Omnia da OmniumAI, a empresa anfitriã deste trabalho. Foram implementados vários métodos de pré-processamento e extracção de características, tais como a remoção de palavras irrelevantes e TF-IDF, que podem ser utilizados em outros casos de estudos, tanto com texto genérico quer com texto biomédico. Estes métodos, em conjunto com as pipelines de AM já desenvolvidas pela equipa da Omnia, permitiram o treino de vários modelos tradicionais de AM. Conseguimos alcançar uma pequena melhoria no desempenho, em comparação com a linha de referência do desafio, ao aplicar estes modelos tradicionais de AM no mesmo conjunto de dados. Relativamente a AP, testando com um modelo CNN, ficou claro que o embedding pré-treinado BioWordVec alcançou o melhor desempenho de todos os embeddings pré-treinados. Adicionalmente, exploramos a aplicação de modelos de AP mais complexos. Estes modelos alcançaram um melhor desempenho do que a melhor submissão do desafio. BioLinkBERT conseguiu uma melhoria de 0,4 pontos percentuais na precisão, 4,9 pontos percentuais no recall, e 2,2 pontos percentuais em F1

Research And Application Of Parallel Computing Algorithms For Statistical Phylogenetic Inference

Estimating the evolutionary history of organisms, phylogenetic inference, is a critical step in many analyses involving biological sequence data such as DNA. The likelihood calculations at the heart of the most effective methods for statistical phylogenetic analyses are extremely computationally intensive, and hence these analyses become a bottleneck in many studies. Recent progress in computer hardware, specifically the increase in pervasiveness of highly parallel, many-core processors has created opportunities for new approaches to computationally intensive methods, such as those in phylogenetic inference. We have developed an open source library, BEAGLE, which uses parallel computing methods to greatly accelerate statistical phylogenetic inference, for both maximum likelihood and Bayesian approaches. BEAGLE defines a uniform application programming interface and includes a collection of efficient implementations that use NVIDIA CUDA, OpenCL, and C++ threading frameworks for evaluating likelihoods under a wide variety of evolutionary models, on GPUs as well as on multi-core CPUs. BEAGLE employs a number of different parallelization techniques for phylogenetic inference, at different granularity levels and for distinct processor architectures. On CUDA and OpenCL devices, the library enables concurrent computation of site likelihoods, data subsets, and independent subtrees. The general design features of the library also provide a model for software development using parallel computing frameworks that is applicable to other domains. BEAGLE has been integrated with some of the leading programs in the field, such as MrBayes and BEAST, and is used in a diverse range of evolutionary studies, including those of disease causing viruses. The library can provide significant performance gains, with the exact increase in performance depending on the specific properties of the data set, evolutionary model, and hardware. In general, nucleotide analyses are accelerated on the order of 10-fold and codon analyses on the order of 100-fold

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

DPWeka: Achieving Differential Privacy in WEKA

Organizations belonging to the government, commercial, and non-profit industries collect and store large amounts of sensitive data, which include medical, financial, and personal information. They use data mining methods to formulate business strategies that yield high long-term and short-term financial benefits. While analyzing such data, the private information of the individuals present in the data must be protected for moral and legal reasons. Current practices such as redacting sensitive attributes, releasing only the aggregate values, and query auditing do not provide sufficient protection against an adversary armed with auxiliary information. In the presence of additional background information, the privacy protection framework, differential privacy, provides mathematical guarantees against adversarial attacks. Existing platforms for differential privacy employ specific mechanisms for limited applications of data mining. Additionally, widely used data mining tools do not contain differentially private data mining algorithms. As a result, for analyzing sensitive data, the cognizance of differentially private methods is currently limited outside the research community. This thesis examines various mechanisms to realize differential privacy in practice and investigates methods to integrate them with a popular machine learning toolkit, WEKA. We present DPWeka, a package that provides differential privacy capabilities to WEKA, for practical data mining. DPWeka includes a suite of differential privacy preserving algorithms which support a variety of data mining tasks including attribute selection and regression analysis. It has provisions for users to control privacy and model parameters, such as privacy mechanism, privacy budget, and other algorithm specific variables. We evaluate private algorithms on real-world datasets, such as genetic data and census data, to demonstrate the practical applicability of DPWeka

A MACHINE LEARNING APPROACH TO QUERY TIME-SERIES MICROARRAY DATA SETS FOR FUNCTIONALLY RELATED GENES USING HIDDEN MARKOV MODELS

Microarray technology captures the rate of expression of genes under varying experimental conditions. Genes encode the information necessary to build proteins; proteins used by cellular functions exhibit higher rates of expression for the associated genes. If multiple proteins are required for a particular function then their genes show a pattern of coexpression during time periods when the function is active within a cell. Cellular functions are generally complex and require groups of genes to cooperate; these groups of genes are called functional modules. Modular organization of genetic functions has been evident since 1999. Detecting functionally related genes in a genome and detecting all genes belonging to particular functional modules are current research topics in this field. The number of microarray gene expression datasets available in public repositories increases rapidly, and advances in technology have now made it feasible to routinely perform whole-genome studies where the behavior of every gene in a genome is captured. This promises a wealth of biological and medical information, but making the amount of data accessible to researchers requires intelligent and efficient computational algorithms. Researchers working on specific cellular functions would benefit from this data if it was possible to quickly extract information useful to their area of research. This dissertation develops a machine learning algorithm that allows one or multiple microarray data sets to be queried with a set of known and functionally related input genes in order to detect additional genes participating in the same or closely related functions. The focus is on time-series microarray datasets where gene expression values are obtained from the same experiment over a period of time from a series of sequential measurements. A feature selection algorithm selects relevant time steps where the provided input genes exhibit correlated expression behavior. Time steps are the columns in microarray data sets, rows list individual genes. A specific linear Hidden Markov Model (HMM) is then constructed to contain one hidden state for each of the selected experiments and is trained using the expression values of the input genes from the microarray. Given the trained HMM the probability that a sequence of gene expression values was generated by that particular HMM can be calculated. This allows for the assignment of a probability score for each gene in the microarray. High-scoring genes are included in the result set (of genes with functional similarities to the input genes.) P-values can be calculated by repeating this algorithm to train multiple individual HMMs using randomly selected genes as input genes and calculating a Parzen Density Function (PDF) from the probability scores of all HMMs for each gene. A feedback loop uses the result generated from one algorithm run as input set for another iteration of the algorithm. This iterated HMM algorithm allows for the characterization of functional modules from very small input sets and for weak similarity signals. This algorithm also allows for the integration of multiple microarray data sets; two approaches are studied: Meta-Analysis (combination of the results from individual data set runs) and the extension of the linear HMM across multiple individual data sets. Results indicate that Meta-Analysis works best for integration of closely related microarrays and a spanning HMM works best for the integration of multiple heterogeneous datasets. The performance of this approach is demonstrated relative to the published literature on a number of widely used synthetic data sets. Biological application is verified by analyzing biological data sets of the Fruit Fly D. Melanogaster and Baker‟s Yeast S. Cerevisiae. The algorithm developed in this dissertation is better able to detect functionally related genes in common data sets than currently available algorithms in the published literature