Provenance, propagation and quality of biological annotation

PhD ThesisBiological databases have become an integral part of the life sciences, being used to store, organise and share ever-increasing quantities and types of data. Biological databases are typically centred around raw data, with individual entries being assigned to a single piece of biological data, such as a DNA sequence. Although essential, a reader can obtain little information from the raw data alone. Therefore, many databases aim to supplement their entries with annotation, allowing the current knowledge about the underlying data to be conveyed to a reader. Although annotations come in many di erent forms, most databases provide some form of free text annotation. Given that annotations can form the foundations of future work, it is important that a user is able to evaluate the quality and correctness of an annotation. However, this is rarely straightforward. The amount of annotation, and the way in which it is curated, varies between databases. For example, the production of an annotation in some databases is entirely automated, without any manual intervention. Further, sections of annotations may be reused, being propagated between entries and, potentially, external databases. This provenance and curation information is not always apparent to a user. The work described within this thesis explores issues relating to biological annotation quality. While the most valuable annotation is often contained within free text, its lack of structure makes it hard to assess. Initially, this work describes a generic approach that allows textual annotations to be quantitatively measured. This approach is based upon the application of Zipf's Law to words within textual annotation, resulting in a single value, . The relationship between the value and Zipf's principle of least e ort provides an indication as to the annotations quality, whilst also allowing annotations to be quantitatively compared. Secondly, the thesis focuses on determining annotation provenance and tracking any subsequent propagation. This is achieved through the development of a visualisation - i - framework, which exploits the reuse of sentences within annotations. Utilising this framework a number of propagation patterns were identi ed, which on analysis appear to indicate low quality and erroneous annotation. Together, these approaches increase our understanding in the textual characteristics of biological annotation, and suggests that this understanding can be used to increase the overall quality of these resources

The Gene Ontology Handbook

bioinformatics; biotechnolog

Multi-Informative and Specific Detection of Blood in Fingermarks via MALDI-MS Based Strategies

Currently employed enhancement and detection techniques for blood are not confirmatory due to targeting generic compound classes like proteins. As such, they are not sufficiently specific and are prone to false positives. The aim of this work was to confidently determine whether a crime scene sample is in fact blood and more specifically human blood. To achieve this, an in-solution bottom up proteomic approach was developed, targeting blood-specific proteins and employing MALDI-MS. The work was developed further to devise a protocol for proteomic in situ analysis of bloodied fingermarks with MALDI-MS imaging, enabling the mapping of blood peptides to fingermark ridges and thus establishing a strong link between the suspect and the event of bloodshed. Putative peptide identifications were made for signals originating from a number of different blood-specific proteins, including not only the most abundant blood proteins like haemoglobin, but also several other proteins (e.g. complement C3 and hemopexin). To further validate the method, a blind study was conducted analysing unknown samples ranging from different species' blood and human biofluids to other substances known to produce false positives with conventional techniques. Employing MALDI-MS, it was possible to confidently identify human blood samples of up to 34 years in age. This is potentially a huge step forward in the forensic analysis of suspected blood samples and shows potential for re-analysis of cold case samples or samples of disputed origin. It was found in this study that further optimisation of the data analysis approach is required for provenance determination of animal blood samples. Traditionally, establishing the order of deposition of fingermarks associated with blood is difficult and subjective. Infinite focus microscopy was investigated for its potential to facilitate quantitative differentiation between the different deposition scenarios. However, results were highly dependent on the surface of deposition and thus the technique was shown to be unsuitable due to the wide range of surfaces potentially encountered in a forensic investigation

Inferring Hierarchical Orthologous Groups

The reconstruction of ancestral evolutionary histories is the cornerstone of most phylogenetic analyses. Many applications are possible once the evolutionary history is unveiled, such as identifying taxonomically restricted genes (genome barcoding), predicting the function of unknown genes based on their evolutionary related genes gene ontologies, identifying gene losses and gene gains among gene families, or pinpointing the time in evolution where particular gene families emerge (sometimes referred to as “phylostratigraphy”). Typically, the reconstruction of the evolutionary histories is limited to the inference of evolutionary relationships (homology, orthology, paralogy) and basic clustering of these orthologs. In this thesis, we adopted the concept of Hierarchical Orthology Groups (HOGs), introduced a decade ago, and proposed several improvements both to improve their inference and to use them in biological analyses such as the aforementioned applications. In addition, HOGs are a powerful framework to investigate ancestral genomes since HOGs convey information regarding gene family evolution (gene losses, gene duplications or gene gains). In this thesis, an ancestral genome at a given taxonomic level denotes the last common ancestor genome for the related taxon and its hypothetical ancestral gene composition and gene order (synteny). The ancestral genes composition and ancestral synteny for a given ancestral genome provides valuable information to study the genome evolution in terms of genomic rearrangement (duplication, translocation, deletion, inversion) or of gene family evolution (variation of the gene function, accelerate gene evolution, duplication rich clade). This thesis identifies three major open challenges that composed my three research arcs. First, inferring HOGs is complex and computationally demanding meaning that robust and scalable algorithms are mandatory to generate good quality HOGs in a reasonable time. Second, benchmarking orthology clustering without knowing the true evolutionary history is a difficult task, which requires appropriate benchmark strategies. And third, the lack of tools to handle HOGs limits their applications. In the first arc of the thesis, I proposed two new algorithm refinements to improve orthology inference in order to produce orthologs less sensitive to gene fragmentations and imbalances in the rate of evolution among paralogous copies. In addition, I introduced version 2.0 of the GETHOGs 2.0 algorithm, which infers HOGs in a bottom up fashion, and which has been shown to be both faster and more accurate. In the second arc, I proposed new strategies to benchmark the reconstruction of gene families using detailed cases studies based on evidence from multiple sequence alignments along with reconstructed gene trees, and to benchmark orthology using a simulation framework that provides full control of the evolutionary genomic setup. This work highlights the main challenges in current methods. Third, I created pyHam (python HOG analysis method), iHam (interactive HOG analysis method) and GTM (Graph - Tree - Multiple sequence alignment)—a collection of tools to process, manipulate and visualise HOGs. pyHam offers an easy way to handle and work with HOGs using simple python coding. Embedded at its heart are two visualisation tools to synthesise HOG-derived information: iHam that allow interactive browsing of HOG structure and a tree based visualisation called tree profile that pinpoints evolutionary events induced by the HOGs on a species tree. In addition, I develop GTM an interactive web based visualisation tool that combine for a given gene family (or set of genes) the related sequences, gene tree and orthology graph. In this thesis, I show that HOGs are a useful framework for phylogenetics, with considerable work done to produce robust and scalable inferences. Another important aspect is that our inferences are benchmarked using manual case studies and automated verification using simulation or reference Quest for Orthologs Benchmarks. Lastly, one of the major advances was the conception and implementation of tools to manipulate and visualise HOG. Such tools have already proven useful when investigating HOGs for developmental reasons or for downstream analysis. Ultimately, the HOG framework is amenable to integration of all aspects which can reasonably be expected to have evolved along the history of genes and ancestral genome reconstruction. -- La reconstruction de l'histoire évolutive ancestrale est la pierre angulaire de la majorité des analyses phylogénétiques. Nombreuses sont les applications possibles une fois que l'histoire évolutive est révélée, comme l'identification de gènes restreints taxonomiquement (barcoding de génome), la prédiction de fonction pour les gènes inconnus en se basant sur les ontologies des gènes relatifs evolutionnairement, l'identification de la perte ou de l'apparition de gènes au sein de familles de gènes ou encore pour dater au cours de l'évolution l'apparition de famille de gènes (phylostratigraphie). Généralement, la reconstruction de l'histoire évolutive se limite à l'inférence des relations évolutives (homologie, orthologie, paralogie) ainsi qu'à la construction de groupes d’orthologues simples. Dans cette thèse, nous adoptons le concept des groupes hiérarchiques d’orthologues (HOGs en anglais pour Hierarchical Orthology Groups), introduit il y a plus de 10 ans, et proposons plusieurs améliorations tant bien au niveau de leurs inférences que de leurs utilisations dans les analyses biologiques susmentionnées. Cette thèse a pour but d'identifier les trois problématiques majeures qui composent mes trois axes de recherches. Premièrement, l'inférence des HOGs est complexe et nécessite une puissance computationnelle importante ce qui rend obligatoire la création d'algorithmes robustes et efficients dans l'espace temps afin de maintenir une génération de résultats de qualité rigoureuse dans un temps raisonnable. Deuxièmement, le contrôle de la qualité du groupement des orthologues est une tâche difficile si on ne connaît l'histoire évolutive réelle ce qui nécessite la mise en place de stratégies de contrôle de qualité adaptées. Tertio, le manque d'outils pour manipuler les HOGs limite leur utilisation ainsi que leurs applications. Dans le premier axe de ma thèse, je propose deux nouvelles améliorations de l'algorithme pour l'inférence des orthologues afin de pallier à la sensibilité de l'inférence vis à vis de la fragmentation des gènes et de l'asymétrie du taux d'évolution au sein de paralogues. De plus, j'introduis la version 2.0 de l'algorithme GETHOGs qui utilise une nouvelle approche de type 'bottom-up' afin de produire des résultats plus rapides et plus précis. Dans le second axe, je propose de nouvelles stratégies pour contrôler la qualité de la reconstruction des familles de gènes en réalisant des études de cas manuels fondés sur des preuves apportées par des alignement multiples de séquences et des reconstructions d'arbres géniques, et aussi pour contrôler la qualité de l'orthologie en simulant l'évolution de génomes afin de pouvoir contrôler totalement le matériel génétique produit. Ce travail met en avant les principales problématiques des méthodes actuelles. Dans le dernier axe, je montre pyHam, iHam et GTM - une panoplie d'outils que j’ai créée afin de faciliter la manipulation et la visualisation des HOGs en utilisant un programmation simple en python. Deux outils de visualisation sont directement intégrés au sein de pyHam afin de pouvoir synthétiser l'information véhiculée par les HOGs: iHam permet d’interactivement naviguer dans les HOGs ainsi qu’une autre visualisation appelée “tree profile” utilisant un arbre d'espèces où sont localisés les événements révolutionnaires contenus dans les HOGs. En sus, j'ai développé GTM un outil interactif web qui combine pour une famille de gènes donnée (ou un ensemble de gènes) leurs séquences alignées, leur arbre de gène ainsi que le graphe d'orthologie en relation. Dans cette thèse, je montre que le concept des HOGs est utile à la phylogénétique et qu'un travail considérable a été réalisé dans le but d'améliorer leur inférences de façon robuste et rapide. Un autre point important est que la qualité de nos inférences soit contrôlée en réalisant des études de cas manuellement ou en utilisant le Quest for Orthologs Benchmark qui est une référence dans le contrôle de la qualité de l’orthologie. Dernièrement, une des avancée majeure proposée est la conception et l'implémentation d'outils pour visualiser et manipuler les HOGs. Ces outils s'avèrent déjà utilisés tant pour l'étude des HOGs dans un but d'amélioration de leur qualité que pour leur utilisation dans des analyses biologiques. Pour conclure, on peut noter que tous les aspects qui semblent avoir évolué en relation avec l'histoire évolutive des gènes ou des génomes ancestraux peuvent être intégrés au concept des HOGs

Functional Annotations of Paralogs: A Blessing and a Curse

Gene duplication followed by mutation is a classic mechanism of neofunctionalization, producing gene families with functional diversity. In some cases, a single point mutation is sufficient to change the substrate specificity and/or the chemistry performed by an enzyme, making it difficult to accurately separate enzymes with identical functions from homologs with different functions. Because sequence similarity is often used as a basis for assigning functional annotations to genes, non-isofunctional gene families pose a great challenge for genome annotation pipelines. Here we describe how integrating evolutionary and functional information such as genome context, phylogeny, metabolic reconstruction and signature motifs may be required to correctly annotate multifunctional families. These integrative analyses can also lead to the discovery of novel gene functions, as hints from specific subgroups can guide the functional characterization of other members of the family. We demonstrate how careful manual curation processes using comparative genomics can disambiguate subgroups within large multifunctional families and discover their functions. We present the COG0720 protein family as a case study. We also discuss strategies to automate this process to improve the accuracy of genome functional annotation pipelines

An integrated approach to enhancing functional annotation of sequences for data analysis of a transcriptome

Given the ever increasing quantity of sequence data, functional annotation of new gene sequences persists as being a significant challenge for bioinformatics. This is a particular problem for transcriptomics studies in crop plants where large genomes and evolutionarily distant model organisms, means that identifying the function of a given gene used on a microarray, is often a non-trivial task. Information pertinent to gene annotations is spread across technically and semantically heterogeneous biological databases. Combining and exploiting these data in a consistent way has the potential to improve our ability to assign functions to new or uncharacterised genes. Methods: The Ondex data integration framework was further developed to integrate databases pertinent to plant gene annotation, and provide data inference tools. The CoPSA annotation pipeline was created to provide automated annotation of novel plant genes using this knowledgebase. CoPSA was used to derive annotations for Affymetrix GeneChips available for plant species. A conjoint approach was used to align GeneChip sequences to orthologous proteins, and identify protein domain regions. These proteins and domains were used together with multiple evidences to predict functional annotations for sequences on the GeneChip. Quality was assessed with reference to other annotation pipelines. These improved gene annotations were used in the analysis of a time-series transcriptomics study of the differential responses of durum wheat varieties to water stress. Results and Conclusions: The integration of plant databases using the Ondex showed that it was possible to increase the overall quantity and quality of information available, and thereby improve the resulting annotation. Direct data aggregation benefits were observed, as well as new information derived from inference across databases. The CoPSA pipeline was shown to improve coverage of the wheat microarray compared to the NetAffx and BLAST2GO pipelines. Leverage of these annotations during the analysis of data from a transcriptomics study of the durum wheat water stress responses, yielded new biological insights into water stress and highlighted potential candidate genes that could be used by breeders to improve drought response

An integrated approach to enhancing functional annotation of sequences for data analysis of a transcriptome

Given the ever increasing quantity of sequence data, functional annotation of new gene sequences persists as being a significant challenge for bioinformatics. This is a particular problem for transcriptomics studies in crop plants where large genomes and evolutionarily distant model organisms, means that identifying the function of a given gene used on a microarray, is often a non-trivial task. Information pertinent to gene annotations is spread across technically and semantically heterogeneous biological databases. Combining and exploiting these data in a consistent way has the potential to improve our ability to assign functions to new or uncharacterised genes. Methods: The Ondex data integration framework was further developed to integrate databases pertinent to plant gene annotation, and provide data inference tools. The CoPSA annotation pipeline was created to provide automated annotation of novel plant genes using this knowledgebase. CoPSA was used to derive annotations for Affymetrix GeneChips available for plant species. A conjoint approach was used to align GeneChip sequences to orthologous proteins, and identify protein domain regions. These proteins and domains were used together with multiple evidences to predict functional annotations for sequences on the GeneChip. Quality was assessed with reference to other annotation pipelines. These improved gene annotations were used in the analysis of a time-series transcriptomics study of the differential responses of durum wheat varieties to water stress. Results and Conclusions: The integration of plant databases using the Ondex showed that it was possible to increase the overall quantity and quality of information available, and thereby improve the resulting annotation. Direct data aggregation benefits were observed, as well as new information derived from inference across databases. The CoPSA pipeline was shown to improve coverage of the wheat microarray compared to the NetAffx and BLAST2GO pipelines. Leverage of these annotations during the analysis of data from a transcriptomics study of the durum wheat water stress responses, yielded new biological insights into water stress and highlighted potential candidate genes that could be used by breeders to improve drought response

Services for biological network feature detection

The complex environment of a living cell contains many molecules interacting in a variety of ways. Examples include the physical interaction between two proteins, or the biochemical interaction between an enzyme and its substrate. A challenge of systems biology is to understand the network of interactions between biological molecules, derived experimentally or computationally. Sophisticated dynamic modelling approaches provide detailed knowledge about single processes or individual pathways. However such methods are far less tractable for holistic cellular models, which are instead represented at the level of network topology. Current network analysis packages tend to be standalone desktop tools which rely on local resources and whose operations are not easily integrated with other software and databases. A key contribution of this thesis is an extensible toolkit of biological network construction and analysis operations, developed as web services. Web services are a distributed technology that enable machine-to-machine interaction over a network, and promote interoperability by allowing tools deployed on heterogeneous systems to interface. A conceptual framework has been created, which is realised practically through the proposal of a common graph format to standardise network data, and the investigation of open-source deployment technologies. Workflows are a graph of web services, allowing analyses to be carried out as part of a bigger software pipeline. They may be constructed using web services within the toolkit together with those from other providers, and can be saved, shared and reused, allowing biologists to construct their own complex queries over various tools and datasets, or execute pre-constructed workflows designed by expert bioinformaticians. Biologically relevant results have been produced as a result of this approach. One very interesting hypothesis has been generated regarding the regulation of yeast glycolysis by a protein found to interact with seven glycolytic enzymes. This has implied a potentially novel regulatory mechanism whereby the protein in question binds these enzymes to form an 'energy production unit'. Also of interest are workflows which identify termini (system inputs and outputs), and cycles, which are crucial for acquiring a physiological perspective on network behaviour

A framework for the management of changing biological experimentation

There is no point expending time and effort developing a model if it is based on data that is out of date. Many models require large amounts of data from a variety of heterogeneous sources. This data is subject to frequent and unannounced changes. It may only be possible to know that data has fallen out of date by reconstructing the model with the new data but this leads to further problems. How and when does the data change and when does the model need to be rebuilt? At best, the model will need to be continually rebuilt in a desperate attempt to remain current. At worst, the model will be producing erroneous results. The recent advent of automated and semi-automated data-processing and analysis tools in the biological sciences has brought about a rapid expansion of publicly available data. Many problems arise in the attempt to deal with this magnitude of data; some have received more attention than others. One significant problem is that data within these publicly available databases is subject to change in an unannounced and unpredictable manner. Large amounts of complex data from multiple, heterogeneous sources are obtained and integrated using a variety of tools. These data and tools are also subject to frequent change, much like the biological data. Reconciling these changes, coupled with the interdisciplinary nature of in silico biological experimentation, presents a significant problem. We present the ExperimentBuilder, an application that records both the current and previous states of an experimental environment. Both the data and metadata about an experiment are recorded. The current and previous versions of each of these experimental components are maintained within the ExperimentBuilder. When any one of these components change, the ExperimentBuilder estimates not only the impact within that specific experiment, but also traces the impact throughout the entire experimental environment. This is achieved with the use of keyword profiles, a heuristic tool for estimating the content of the experimental component. We can compare one experimental component to another regardless of their type and content and build a network of inter-component relationships for the entire environment. Ultimately, we can present the impact of an update as a complete cost to the entire environment in order to make an informed decision about whether to recalculate our results