A Parallel Template for Implementing Filters for Biological Correlation Networks

High throughput biological experiments are critical for their role in systems biology – the ability to survey the state of cellular mechanisms on the broad scale opens possibilities for the scientific researcher to understand how multiple components come together, and what goes wrong in disease states. However, the data returned from these experiments is massive and heterogeneous, and requires intuitive and clever computational algorithms for analysis. The correlation network model has been proposed as a tool for modeling and analysis of this high throughput data; structures within the model identified by graph theory have been found to represent key players in major cellular pathways. Previous work has found that network filtering using graph theoretic structural concepts can reduce noise and strengthen biological signals in these networks. However, the process of filtering biological network using such filters is computationally intensive and the filtered networks remain large. In this research, we develop a parallel template for these network filters to improve runtime, and use this high performance environment to show that parallelization does not affect network structure or biological function of that structure

The Gene Ontology Handbook

bioinformatics; biotechnolog

Functional coherence and annotation agreement metrics for enzyme families

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2015A range of methodologies is used to create sequence annotations, from manual curation by specialized curators to several automatic procedures. The multitude of existing annotation methods consequently generates an annotation heterogeneity in terms of coverage and specificity across the biological sequence space. When comparing groups of similar sequences (such as protein families) this heterogeneity can introduce issues regarding the interpretation of the actual functional similarity and the overall functional coherence. A direct path to mitigate these issues is the annotation extension within the protein families under analysis. This thesis postulates that the protein families can be used as knowledgebases for their own annotation extension with the assistance of a proper functional coherence analysis. Therefore, a modular framework for functional coherence analysis and annotation extension in protein families was proposed. The framework includes a proposed module for functional coherence analysis that relies on graph visualization, term enrichment and other statistics. In this work it was implemented and made available as a publicly accessible web application, GRYFUN which can be accessed at http://xldb.di.fc.ul.pt/gryfun/. In addition, four metrics were developed to assess distinct aspects of the coherence and completeness in protein families in conjunction with additional existing metrics. Therefore the use of the complete proposed framework by curators can be regarded as a semi-automatic approach to annotation able to assist with protein annotation extension.Diversas metodologias são usadas para criar anotações em sequências, desde a curação manual por curadores especializados até vários procedimentos automáticos. A multitude de métodos de anotação existentes consequentemente gera heterogeneidade nas anotações em termos de cobertura e especificidade em espaços de sequências biológicas. Ao comparar grupos de sequências semelhantes (tais como famílias proteícas) esta heterogeneidade pode introduzir dificuldades quanto à interpretação da semelhança e coerência funcional nesses grupos. Uma maneira de mitigar essas dificuldades é a extensão da anotação dentro das famílias proteícas em análise. Esta tese postula que famílias proteícas podem ser usadas como bases de conhecimento para a sua própria extensão de anotação através do uso de análises de coerência funcional apropriadas. Portanto, uma framework modular para a análise de coerência funcional e extensão de anotação em famílias proteícas foi proposta. A framework incluí um módulo proposto para a análise de coerência funcional baseado em visualização de grafos, enriquecimento de termos e outras estatísticas. Neste trabalho o módulo foi implementado e disponibilizado como uma aplicação web, GRYFUN que pode ser acedida em http://xldb.di.fc.ul.pt/gryfun/. Adicionalmente, quatro métricas foram desenvolvidas para aferir aspectos distinctos da coerência e completude de anotação em famílias proteícas em conjunção com métricas já existentes. Portanto, o uso da framework completa por curadores, como uma estratégia de anotação semi-automática, é capaz de potenciar a extensão de anotação.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/48035/200

Computational functional annotation of crop genomics using hierarchical orthologous groups

Improving agronomically important traits, such as yield, is important in order to meet the ever growing demands of increased crop production. Knowledge of the genes that have an effect on a given trait can be used to enhance genomic selection by prediction of biologically interesting loci. Candidate genes that are strongly linked to a desired trait can then be targeted by transformation or genome editing. This application of prioritisation of genetic material can accelerate crop improvement. However, the application of this is currently limited due to the lack of accurate annotations and methods to integrate experimental data with evolutionary relationships. Hierarchical orthologous groups (HOGs) provide nested groups of genes that enable the comparison of highly diverged and similar species in a consistent manner. Over 2,250 species are included in the OMA project, resulting in over 600,000 HOGs. This thesis provides the required methodology and a tool to exploit this rich source of information, in the HOGPROP algorithm. The potential of this is then demonstrated in mining crop genome data, from metabolic QTL studies and utilising Gene Ontology (GO) annotations as well as ChEBI terms (Chemical Entities of Biological Interest) in order to prioritise candidate causal genes. Gauging the performance of the tool is also important. When considering GO annotations, the CAFA series of community experiments has provided the most extensive benchmarking to-date. However, this has not fully taken into account the incomplete knowledge of protein function – the open world assumption (OWA). This will require extra negative annotations, for which one such source has been identified based on expertly curated gene phylogenies. These negative annotations are then utilised in the proposed, OWA-compliant, improved framework for benchmarking. The results show that current benchmarks tend to focus on the general terms, which means that conclusions are not merely uninformative, but misleading

Accurate Prediction Methods on Biomolecular Data

With the recent advancements in sequencing technologies, molecular biologists are producing ever-increasing amounts of biomolecular data. Extracting useful information from these massive data sets requires efficient and effective data mining and machine learning methods. In this dissertation, we explore the use of supervised machine learning (ML) to solve some challenging classification problems in molecular biology.First, we devise an ML model for classifying cancer types from very sparse somatic point mutation data. Accumulation of mutation and epigenetic modifications in somatic cells results in various cancer. For this purpose, we propose a method called mClass for efficient feature (gene) ranking that uses clustering, normalized mutual information and logistic regression. We show that somatic mutation data has sufficient discriminative power for cancer type classification.Next, we address the problem of gene essentiality prediction in microbes. Essential genes are significant to identify since their function is vital for the survival of the organism. Our proposed deep learning architecture called DeeplyEssential exclusively uses features extracted from the primary sequence of genes and their corresponding proteins, to maximize the utility and practicality of the tool. DeeplyEssential achieved state-of-the-art performance over previously proposed methods as well as expose and study a hidden performance bias affected previous models.Finally, we consider the problem of predicting the enhancer regions in the human genome from chromatin data. Enhancers contribute to the transcription of target genes. We propose a convolutional neural network framework named Epi2En that takes advantage of epigenetic ChIP-seq data. Epi2En's classification performance is not only very strong on cross-validation experiments, but also when testing across different cell-lines

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Physics based supervised and unsupervised learning of graph structure

Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to understand graph structure. I investigate four phenomenon or laws in nature: (1) Brownian motion, (2) Gauss\u27s law, (3) feedback loops, and (3) neural synapses, to discover patterns in graphs