Academic literature on the topic 'Gene ontology enrichment'

Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL http://caps.ncbs.res.in/poeas .

Abstract Summary: High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline. Availability: nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev. Contact: cholmes@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Salvia hispanica L. (commonly known as chia) is gaining popularity worldwide and specially in US as a healthy oil and food supplement for human and animal consumption due to its favorable oil composition, and high protein, fiber, and antioxidant contents. Despite these benefits and its growing public demand, very limited gene sequence information is currently available in public databases. In this project, we generated 90 million high quality 150 bp paired-end sequences from the chia leaf and root tissues. The sequences were de novo assembled into 103,367 contigs with average length of 1,445 bp. The resulted assembly represented 92.2% transcriptome completeness. Around 69% of the assembled contigs were annotated against the uniprot database and represented a diverse array of functional and biological categories. A total of 14,267 contigs showed significant expression difference between the leaf and root tissues, with 6,151 and 8,116 contigs upregulated in the leaf and root, respectively. The sequence data generated in this project will provide valuable resources for future functional genomic research in chia. With the availability of transcriptome sequences, it would be possible to identify genes involved in the important metabolic pathways that give chia its unique nutritional and medicinal properties. Finally, the generated data will contribute to the genetic improvement efforts of chia to better serve the public demand.

Motivation Translational bioinformatics(TBI) has been defined as ‘the development and application of informatics methods that connect molecular entities to clinical entities’ [1], which has emerged as a systems theory approach to bridge the huge wealth of biomedical data into clinical actions using a combination of innovations and resources across the entire spectrum of biomedical informatics approaches [2]. The challenge for TBI is the availability of both comprehensive knowledge based on genes and the corresponding tools that allow their analysis and exploitation. Traditionally, biological researchers usually study one or only a few genes at a time, but in recent years high throughput technologies such as gene expression microarrays, protein mass-spectrometry and next-generation DNA and RNA sequencing have emerged that allow the simultaneous measurement of changes on a genome-wide scale. These technologies usually result in large lists of interesting genes, but meaningful biological interpretation remains a major challenge. Over the last decade, enrichment analysis has become standard practice in the analysis of such gene lists, enabling systematic assessment of the likelihood of differential representation of defined groups of genes compared to suitably annotated background knowledge. The success of such analyses are highly dependent on the availability and quality of the gene annotation data. For many years, genes were annotated by different experts using inconsistent, non-standard terminologies. Large amounts of variation and duplication in these unstructured annotation sets, made them unsuitable for principled quantitative analysis. More recently, a lot of effort has been put into the development and use of structured, domain specific vocabularies to annotate genes. The Gene Ontology is one of the most successful examples of this where genes are annotated with terms from three main clades; biological process, molecular function and cellular component. However, there are many other established and emerging ontologies to aid biological data interpretation, but are rarely used. For the same reason, many bioinformatic tools only support analysis analysis using the Gene Ontology. The lack of annotation coverage and the support for them in existing analytical tools to aid biological interpretation of data has become a major limitation to their utility and uptake. Thus, automatic approaches are needed to facilitate the transformation of unstructured data to unlock the potential of all ontologies, with corresponding bioinformatics tools to support their interpretation. Approaches In this thesis, firstly, similar to the approach in [3,4], I propose a series of computational approaches implemented in a new tool OntoSuite-Miner to address the ontology based gene association data integration challenge. This approach uses NLP based text mining methods for ontology based biomedical text mining. What differentiates my approach from other approaches is that I integrate two of the most wildly used NLP modules into the framework, not only increasing the confidence of the text mining results, but also providing an annotation score for each mapping, based on the number of pieces of evidence in the literature and the number of NLP modules that agreed with the mapping. Since heterogeneous data is important in understanding human disease, the approach was designed to be generic, thus the ontology based annotation generation can be applied to different sources and can be repeated with different ontologies. Secondly, in respect of the second challenge proposed by TBI, to increase the statistical power of the annotation enrichment analysis, I propose OntoSuite-Analytics, which integrates a collection of enrichment analysis methods into a unified open-source software package named topOnto, in the statistical programming language R. The package supports enrichment analysis across multiple ontologies with a set of implemented statistical/topological algorithms, allowing the comparison of enrichment results across multiple ontologies and between different algorithms. Results The methodologies described above were implemented and a Human Disease Ontology (HDO) based gene annotation database was generated by mining three publicly available database, OMIM, GeneRIF and Ensembl variation. With the availability of the HDO annotation and the corresponding ontology enrichment analysis tools in topOnto, I profiled 277 gene classes with human diseases and generated ‘disease environments’ for 1310 human diseases. The exploration of the disease profiles and disease environment provides an overview of known disease knowledge and provides new insights into disease mechanisms. The integration of multiple ontologies into a disease context demonstrates how ‘orthogonal’ ontologies can lead to biological insight that would have been missed by more traditional single ontology analysis.

Ontologies provide an organization of language, in the form of a network or graph, which is amenable to computational analysis while remaining human-readable. Although they are used in a variety of disciplines, ontologies in the biomedical field, such as Gene Ontology, are of interest for their role in organizing terminology used to describe—among other concepts—the functions, locations, and processes of genes and gene-products. Due to the consistency and level of automation that ontologies provide for such annotations, methods for finding enriched biological terminology from a set of differentially identified genes in a tissue or cell sample have been developed to aid in the elucidation of disease pathology and unknown biochemical pathways. However, despite their immense utility, biomedical ontologies have significant limitations and caveats. One major issue is that gene annotation enrichment analyses often result in many redundant, individually enriched ontological terms that are highly specific and weakly justified by statistical significance. These large sets of weakly enriched terms are difficult to interpret without manually sorting into appropriate functional or descriptive categories. Also, relationships that organize the terminology within these ontologies do not contain descriptions of semantic scoping or scaling among terms. Therefore, there exists some ambiguity, which complicates the automation of categorizing terms to improve interpretability. We emphasize that existing methods enable the danger of producing incorrect mappings to categories as a result of these ambiguities, unless simplified and incomplete versions of these ontologies are used which omit problematic relations. Such ambiguities could have a significant impact on term categorization, as we have calculated upper boundary estimates of potential false categorizations as high as 121,579 for the misinterpretation of a single scoping relation, has_part, which accounts for approximately 18% of the total possible mappings between terms in the Gene Ontology. However, the omission of problematic relationships results in a significant loss of retrievable information. In the Gene Ontology, this accounts for a 6% reduction for the omission of a single relation. However, this percentage should increase drastically when considering all relations in an ontology. To address these issues, we have developed methods which categorize individual ontology terms into broad, biologically-related concepts to improve the interpretability and statistical significance of gene-annotation enrichment studies, meanwhile addressing the lack of semantic scoping and scaling descriptions among ontological relationships so that annotation enrichment analyses can be performed across a more complete representation of the ontological graph. We show that, when compared to similar term categorization methods, our method produces categorizations that match hand-curated ones with similar or better accuracy, while not requiring the user to compile lists of individual ontology term IDs. Furthermore, our handling of problematic relations produces a more complete representation of ontological information from a scoping perspective, and we demonstrate instances where medically-relevant terms--and by extension putative gene targets--are identified in our annotation enrichment results that would be otherwise missed when using traditional methods. Additionally, we observed a marginal, yet consistent improvement of statistical power in enrichment results when our methods were used, compared to traditional enrichment analyses that utilize ontological ancestors. Finally, using scalable and reproducible data workflow pipelines, we have applied our methods to several genomic, transcriptomic, and proteomic collaborative projects.

Colorectal cancer is the third most common cancer and the leading cause of cancer deaths in Western industrialised countries. Despite recent advances in the screening, diagnosis, and treatment of colorectal cancer, an estimated 608,000 people die every year due to colon cancer. Our current knowledge of colorectal carcinogenesis indicates a multifactorial and multi-step process that involves various genetic alterations and several biological pathways. The identification of molecular markers with early diagnostic and precise clinical outcome in colon cancer is a challenging task because of tumour heterogeneity. This Ph.D.-thesis presents the molecular and cellular mechanisms leading to colorectal cancer. A systematical review of the literature is conducted on Microarray Gene expression profiling, gene ontology enrichment analysis, microRNA and system Biology and various bioinformatics tools. We aimed this study to stratify a colon tumour into molecular distinct subtypes, identification of novel diagnostic targets and prediction of reliable prognostic signatures for clinical practice using microarray expression datasets. We performed an integrated analysis of gene expression data based on genetic, epigenetic and extensive clinical information using unsupervised learning, correlation and functional network analysis. As results, we identified 267-gene and 124-gene signatures that can distinguish normal, primary and metastatic tissues, and also involved in important regulatory functions such as immune-response, lipid metabolism and peroxisome proliferator-activated receptors (PPARs) signalling pathways. For the first time, we also identify miRNAs that can differentiate between primary colon from metastatic and a prognostic signature of grade and stage levels, which can be a major contributor to complex transcriptional phenotypes in a colon tumour.

Im Bereich der Lebenswissenschaften steht eine große und wachsende Menge heterogener Datenquellen zur Verfügung, welche häufig in quellübergreifenden Analysen und Auswertungen miteinander kombiniert werden. Um eine einheitliche und strukturierte Erfassung von Wissen sowie einen formalen Austausch zwischen verschiedenen Applikationen zu erleichtern, kommen Ontologien und andere strukturierte Vokabulare zum Einsatz. Sie finden Anwendung in verschiedenen Domänen wie der Molekularbiologie oder Chemie und dienen zumeist der Annotation realer Objekte wie z.B. Gene oder Literaturquellen. Unterschiedliche Ontologien enthalten jedoch teilweise überlappendes Wissen, so dass die Bestimmung einer Abbildung (Ontologiemapping) zwischen ihnen notwendig ist. Oft ist eine manuelle Mappingerstellung zwischen großen Ontologien kaum möglich, weshalb typischerweise automatische Verfahren zu deren Abgleich (Matching) eingesetzt werden. Aufgrund neuer Forschungserkenntnisse und Nutzeranforderungen verändern sich die Ontologien kontinuierlich weiter. Die Evolution der Ontologien hat wiederum Auswirkungen auf abhängige Daten wie beispielsweise Annotations- und Ontologiemappings, welche entsprechend aktualisiert werden müssen. Im Rahmen dieser Arbeit werden neue Methoden und Algorithmen zum Umgang mit der Evolution ontologie-basierter Mappings entwickelt. Dabei wird die generische Infrastruktur GOMMA zur Verwaltung und Analyse der Evolution von Ontologien und Mappings genutzt und erweitert. Zunächst wurde eine vergleichende Analyse der Evolution von Ontologiemappings für drei Subdomänen der Lebenswissenschaften durchgeführt. Ontologien sowie Mappings unterliegen teilweise starken Änderungen, wobei die Evolutionsintensität von der untersuchten Domäne abhängt. Insgesamt zeigt sich ein deutlicher Einfluss von Ontologieänderungen auf Ontologiemappings. Dementsprechend können bestehende Mappings infolge der Weiterentwicklung von Ontologien ungültig werden, so dass sie auf aktuelle Ontologieversionen migriert werden müssen. Dabei sollte eine aufwendige Neubestimmung der Mappings vermieden werden. In dieser Arbeit werden zwei generische Algorithmen zur (semi-) automatischen Adaptierung von Ontologiemappings eingeführt. Ein Ansatz basiert auf der Komposition von Ontologiemappings, wohingegen der andere Ansatz eine individuelle Behandlung von Ontologieänderungen zur Adaptierung der Mappings erlaubt. Beide Verfahren ermöglichen die Wiederverwendung unbeeinflusster, bereits bestätigter Mappingteile und adaptieren nur die von Änderungen betroffenen Bereiche der Mappings. Eine Evaluierung für sehr große, biomedizinische Ontologien und Mappings zeigt, dass beide Verfahren qualitativ hochwertige Ergebnisse produzieren. Ähnlich zu Ontologiemappings werden auch ontologiebasierte Annotationsmappings durch Ontologieänderungen beeinflusst. Die Arbeit stellt einen generischen Ansatz zur Bewertung der Qualität von Annotationsmappings auf Basis ihrer Evolution vor. Verschiedene Qualitätsmaße erlauben die Identifikation glaubwürdiger Annotationen beispielsweise anhand ihrer Stabilität oder Herkunftsinformationen. Eine umfassende Analyse großer Annotationsdatenquellen zeigt zahlreiche Instabilitäten z.B. aufgrund temporärer Annotationslöschungen. Dementsprechend stellt sich die Frage, inwieweit die Datenevolution zu einer Veränderung von abhängigen Analyseergebnissen führen kann. Dazu werden die Auswirkungen der Ontologie- und Annotationsevolution auf sogenannte funktionale Analysen großer biologischer Datensätze untersucht. Eine Evaluierung anhand verschiedener Stabilitätsmaße erlaubt die Bewertung der Änderungsintensität der Ergebnisse und gibt Aufschluss, inwieweit Nutzer mit einer signifikanten Veränderung ihrer Ergebnisse rechnen müssen. Darüber hinaus wird GOMMA um effiziente Verfahren für das Matching sehr großer Ontologien erweitert. Diese werden u.a. für den Abgleich neuer Konzepte während der Adaptierung von Ontologiemappings benötigt. Viele der existierenden Match-Systeme skalieren nicht für das Matching besonders großer Ontologien wie sie im Bereich der Lebenswissenschaften auftreten. Ein effizienter, kompositionsbasierter Ansatz gleicht Ontologien indirekt ab, indem existierende Mappings zu Mediatorontologien wiederverwendet und miteinander kombiniert werden. Mediatorontologien enthalten wertvolles Hintergrundwissen, so dass sich die Mappingqualität im Vergleich zu einem direkten Matching verbessern kann. Zudem werden generelle Strategien für das parallele Ontologie-Matching unter Verwendung mehrerer Rechenknoten vorgestellt. Eine größenbasierte Partitionierung der Eingabeontologien verspricht eine gute Lastbalancierung und Skalierbarkeit, da kleinere Teilaufgaben des Matchings parallel verarbeitet werden können. Die Evaluierung im Rahmen der Ontology Alignment Evaluation Initiative (OAEI) vergleicht GOMMA und andere Systeme für das Matching von Ontologien in verschiedenen Domänen. GOMMA kann u.a. durch Anwendung des parallelen und kompositionsbasierten Matchings sehr gute Ergebnisse bezüglich der Effektivität und Effizienz des Matchings, insbesondere für Ontologien aus dem Bereich der Lebenswissenschaften, erreichen
In the life sciences, there is an increasing number of heterogeneous data sources that need to be integrated and combined in comprehensive analysis tasks. Often ontologies and other structured vocabularies are used to provide a formal representation of knowledge and to facilitate data exchange between different applications. Ontologies are used in different domains like molecular biology or chemistry. One of their most important applications is the annotation of real-world objects like genes or publications. Since different ontologies can contain overlapping knowledge it is necessary to determine mappings between them (ontology mappings). A manual mapping creation can be very time-consuming or even infeasible such that (semi-) automatic ontology matching methods are typically applied. Ontologies are not static but underlie continuous modifications due to new research insights and changing user requirements. The evolution of ontologies can have impact on dependent data like annotation or ontology mappings. This thesis presents novel methods and algorithms to deal with the evolution of ontology-based mappings. Thereby the generic infrastructure GOMMA is used and extended to manage and analyze the evolution of ontologies and mappings. First, a comparative evolution analysis for ontologies and mappings from three life science domains shows heavy changes in ontologies and mappings as well as an impact of ontology changes on the mappings. Hence, existing ontology mappings can become invalid and need to be migrated to current ontology versions. Thereby an expensive redetermination of the mappings should be avoided. This thesis introduces two generic algorithms to (semi-) automatically adapt ontology mappings: (1) a composition-based adaptation relies on the principle of mapping composition, and (2) a diff-based adaptation algorithm allows for individually handling change operations to update mappings. Both approaches reuse unaffected mapping parts, and adapt only affected parts of the mappings. An evaluation for very large biomedical ontologies and mappings shows that both approaches produce ontology mappings of high quality. Similarly, ontology changes may also affect ontology-based annotation mappings. The thesis introduces a generic evaluation approach to assess the quality of annotation mappings based on their evolution. Different quality measures allow for the identification of reliable annotations, e.g., based on their stability or provenance information. A comprehensive analysis of large annotation data sources shows numerous instabilities, e.g., due to the temporary absence of annotations. Such modifications may influence results of dependent applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. The question arises to what degree ontology and annotation changes may affect such analyses. Based on different stability measures the evaluation assesses change intensities of application results and gives insights whether users need to expect significant changes of their analysis results. Moreover, GOMMA is extended by large-scale ontology matching techniques. Such techniques are useful, a.o., to match new concepts during ontology mapping adaptation. Many existing match systems do not scale for aligning very large ontologies, e.g., from the life science domain. One efficient composition-based approach indirectly computes ontology mappings by reusing and combining existing mappings to intermediate ontologies. Intermediate ontologies can contain useful background knowledge such that the mapping quality can be improved compared to a direct match approach. Moreover, the thesis introduces general strategies for matching ontologies in parallel using several computing nodes. A size-based partitioning of the input ontologies enables good load balancing and scalability since smaller match tasks can be processed in parallel. The evaluation of the Ontology Alignment Evaluation Initiative (OAEI) compares GOMMA and other systems in terms of matching ontologies from different domains. Using the parallel and composition-based matching, GOMMA can achieve very good results w.r.t. efficiency and effectiveness, especially for ontologies from the life science domain

Spinal Muscular Atrophy (SMA) is among the most common genetic neurological diseases that cause infant mortality. SMA is caused by deletion or mutations in the survival motor neuron 1 gene (SMN1), which are expected to generate alterations in RNA transcription, or splicing and most importantly reductions in mRNA transport within the axons of motor neurons (MNs). SMA ultimately results in the selective degeneration of MNs in spinal cord, but the underlying reason is still not clear entirely. The aim of this study is to investigate splicing abnormalities in SMA, and to identify genes presenting differential splicing possibly involved in the pathogenesis of SMA at genome-wide level. We performed RNA-Sequencing data analysis on 2 SMA patients and 2 controls, with 2 biological replicates each sample, derived from their induced Pluripotent Stem Cells-differentiated-MNs. Three types of analyses were executed. Firstly, differential expression analysis was performed to identify possibly mis-regulated genes using Cufflinks. Secondly, alternative splicing analysis was conducted to find differentially-used exons (DUEs; using DEXSeq) as splicing patterns are known to be altered in MNs by the suboptimal levels of SMN protein. Thirdly, we did RNA-binding protein (RBP) - motif discovery for the set of identified alternative cassette-DUEs, to pinpoint possible mechanisms of such alterations, specific to MNs. The gene ontology enrichment analysis of significant DEGs and alternative cassette-DUEs revealed various interesting terms including axon-guidance, muscle-contraction, microtubule-based transport, axon-cargo transport, synapse etc. which suggests their involvement in SMA. Further, promising results were obtained from motif analysis which has identified 22 RBPs out of which 7 RBPs namely, PABPC1, PABPC3, PABPC4, PABPC5, PABPN1, SART3 and KHDRBS1 are known for mRNAs stabilization and mRNA transport across MN-axon. Five RBPs from PABP family are known to interact directly with SMN protein that enhance mRNA transport in MNs. To validate our results specific wet-lab experiments are required, involving precise recognition of RNA-binding sites correspondent with our findings. Our work has provided a promising set of putative targets which might offer potential therapeutic role towards treating SMA. During the course of our study, we have observed that current methods for an effective understanding of differential splicing events within the transcriptomic landscape at high resolution are insufficient. To address this problem, we developed a computational model which has a potential to precisely estimate the “transcript expression levels” within a given gene locus by disentangling mature and nascent transcription contributions for each transcript at per base resolution. We modeled exonic and intronic read coverages by applying a non-linear computational model and estimated expression for each transcript, which best approximated the observed expression in total RNA-Seq data. The performance of our model was good in terms of computational processing time and memory usage. The application of our model is in the detection of differential splicing events. At exon level, differences in the ratio of the sum of mature and the sum of nascent transcripts over all the transcripts in a gene locus gives an indication of differential splicing. We have implemented our model in R-statistical language.

Thousands of biomedical articles are published every year containing many newly discovered biological interactions and functions. Manually reading and classifying this information is a difficult and laborious task. Literature mining contains mechanisms and tools to automate the process of extracting biological relationships, storing them in biological databases and finally analyse and present them in a biological meaningful way. In the first stage of literature mining, articles are parsed and get segmented, sentences separated, tokenized and finally annotated by part of speech tags (POS). POS tagging is the most challenging part because the training corpus is relatively small compared to the large number of biological names therefore limiting the lexicon. There are a number of solutions to address this problem including extending the lexicon manually or using character features of the word. There is no empirical comparison between different solutions. So we developed a complete list of tools including article parser, segmentation, sentence detector, sentence tokeniser, POS tagger and finally noun phrase detector using JAVA and PostgreSQL technologies. We tailored these tools for biomedical texts, and empirically compared them with other tools and we demonstrated increased efficiency of our tools compared to others. Once biological relationships are extracted they are ready to be stored in databases to be used and shared by others. There a wide range of databases that store annotation data related to genes, proteins and other biological entities. Among them Gene Ontology annotation database is the key database that connects all the other biological entities through a standard vocabulary together. In fact a Gene Ontology (GO) is a controlled vocabulary to annotate proteins based on their molecular function, biological process and cellular components. There are a number of public databases that provide data regarding GO and GO-protein relationships. We collected all relevant data from several public databases and built our specialized updatable GO database on the PostgreSQL platform. GO classification in a particular sample of genes (up/down regulated) or whole genome of a species can reveal the biological mechanisms related to its activity. Moreover, comparing the GO classification of a species under different biological conditions can elucidate its biological pathways, which can result in the discovery of novel genes to be used in therapies. We developed a web server using the PHP MVC framework connected to our specialized GO database. In this web server we developed novel visual and statistical methods to perform GO comparisons among multiple samples and genomes. We also included transcriptome based gene expression levels in GO analysis, resulting in novel meaningful biological reports. This also made comparison of whole genome gene expression across multiple biological conditions possible. Furthermore, we devised a method to dynamically construct and visualize GO regulatory networks for any gene set sample. Such a network can reveal regulatory relationships between genes helping to explain the correlated expression of genes. The topology of such a network classifies genes based on their connections, and can be used as a new method to detect important genes based on their function as well as their connectivity in the network. We demonstrated the efficiency of our developed methods in our web server by several case studies using previously published transcriptome data.
Thesis (Ph.D.) -- University of Adelaide, School of Molecular and Biomedical Science, 2014

Im Bereich der Lebenswissenschaften steht eine große und wachsende Menge heterogener Datenquellen zur Verfügung, welche häufig in quellübergreifenden Analysen und Auswertungen miteinander kombiniert werden. Um eine einheitliche und strukturierte Erfassung von Wissen sowie einen formalen Austausch zwischen verschiedenen Applikationen zu erleichtern, kommen Ontologien und andere strukturierte Vokabulare zum Einsatz. Sie finden Anwendung in verschiedenen Domänen wie der Molekularbiologie oder Chemie und dienen zumeist der Annotation realer Objekte wie z.B. Gene oder Literaturquellen. Unterschiedliche Ontologien enthalten jedoch teilweise überlappendes Wissen, so dass die Bestimmung einer Abbildung (Ontologiemapping) zwischen ihnen notwendig ist. Oft ist eine manuelle Mappingerstellung zwischen großen Ontologien kaum möglich, weshalb typischerweise automatische Verfahren zu deren Abgleich (Matching) eingesetzt werden. Aufgrund neuer Forschungserkenntnisse und Nutzeranforderungen verändern sich die Ontologien kontinuierlich weiter. Die Evolution der Ontologien hat wiederum Auswirkungen auf abhängige Daten wie beispielsweise Annotations- und Ontologiemappings, welche entsprechend aktualisiert werden müssen. Im Rahmen dieser Arbeit werden neue Methoden und Algorithmen zum Umgang mit der Evolution ontologie-basierter Mappings entwickelt. Dabei wird die generische Infrastruktur GOMMA zur Verwaltung und Analyse der Evolution von Ontologien und Mappings genutzt und erweitert. Zunächst wurde eine vergleichende Analyse der Evolution von Ontologiemappings für drei Subdomänen der Lebenswissenschaften durchgeführt. Ontologien sowie Mappings unterliegen teilweise starken Änderungen, wobei die Evolutionsintensität von der untersuchten Domäne abhängt. Insgesamt zeigt sich ein deutlicher Einfluss von Ontologieänderungen auf Ontologiemappings. Dementsprechend können bestehende Mappings infolge der Weiterentwicklung von Ontologien ungültig werden, so dass sie auf aktuelle Ontologieversionen migriert werden müssen. Dabei sollte eine aufwendige Neubestimmung der Mappings vermieden werden. In dieser Arbeit werden zwei generische Algorithmen zur (semi-) automatischen Adaptierung von Ontologiemappings eingeführt. Ein Ansatz basiert auf der Komposition von Ontologiemappings, wohingegen der andere Ansatz eine individuelle Behandlung von Ontologieänderungen zur Adaptierung der Mappings erlaubt. Beide Verfahren ermöglichen die Wiederverwendung unbeeinflusster, bereits bestätigter Mappingteile und adaptieren nur die von Änderungen betroffenen Bereiche der Mappings. Eine Evaluierung für sehr große, biomedizinische Ontologien und Mappings zeigt, dass beide Verfahren qualitativ hochwertige Ergebnisse produzieren. Ähnlich zu Ontologiemappings werden auch ontologiebasierte Annotationsmappings durch Ontologieänderungen beeinflusst. Die Arbeit stellt einen generischen Ansatz zur Bewertung der Qualität von Annotationsmappings auf Basis ihrer Evolution vor. Verschiedene Qualitätsmaße erlauben die Identifikation glaubwürdiger Annotationen beispielsweise anhand ihrer Stabilität oder Herkunftsinformationen. Eine umfassende Analyse großer Annotationsdatenquellen zeigt zahlreiche Instabilitäten z.B. aufgrund temporärer Annotationslöschungen. Dementsprechend stellt sich die Frage, inwieweit die Datenevolution zu einer Veränderung von abhängigen Analyseergebnissen führen kann. Dazu werden die Auswirkungen der Ontologie- und Annotationsevolution auf sogenannte funktionale Analysen großer biologischer Datensätze untersucht. Eine Evaluierung anhand verschiedener Stabilitätsmaße erlaubt die Bewertung der Änderungsintensität der Ergebnisse und gibt Aufschluss, inwieweit Nutzer mit einer signifikanten Veränderung ihrer Ergebnisse rechnen müssen. Darüber hinaus wird GOMMA um effiziente Verfahren für das Matching sehr großer Ontologien erweitert. Diese werden u.a. für den Abgleich neuer Konzepte während der Adaptierung von Ontologiemappings benötigt. Viele der existierenden Match-Systeme skalieren nicht für das Matching besonders großer Ontologien wie sie im Bereich der Lebenswissenschaften auftreten. Ein effizienter, kompositionsbasierter Ansatz gleicht Ontologien indirekt ab, indem existierende Mappings zu Mediatorontologien wiederverwendet und miteinander kombiniert werden. Mediatorontologien enthalten wertvolles Hintergrundwissen, so dass sich die Mappingqualität im Vergleich zu einem direkten Matching verbessern kann. Zudem werden generelle Strategien für das parallele Ontologie-Matching unter Verwendung mehrerer Rechenknoten vorgestellt. Eine größenbasierte Partitionierung der Eingabeontologien verspricht eine gute Lastbalancierung und Skalierbarkeit, da kleinere Teilaufgaben des Matchings parallel verarbeitet werden können. Die Evaluierung im Rahmen der Ontology Alignment Evaluation Initiative (OAEI) vergleicht GOMMA und andere Systeme für das Matching von Ontologien in verschiedenen Domänen. GOMMA kann u.a. durch Anwendung des parallelen und kompositionsbasierten Matchings sehr gute Ergebnisse bezüglich der Effektivität und Effizienz des Matchings, insbesondere für Ontologien aus dem Bereich der Lebenswissenschaften, erreichen.
In the life sciences, there is an increasing number of heterogeneous data sources that need to be integrated and combined in comprehensive analysis tasks. Often ontologies and other structured vocabularies are used to provide a formal representation of knowledge and to facilitate data exchange between different applications. Ontologies are used in different domains like molecular biology or chemistry. One of their most important applications is the annotation of real-world objects like genes or publications. Since different ontologies can contain overlapping knowledge it is necessary to determine mappings between them (ontology mappings). A manual mapping creation can be very time-consuming or even infeasible such that (semi-) automatic ontology matching methods are typically applied. Ontologies are not static but underlie continuous modifications due to new research insights and changing user requirements. The evolution of ontologies can have impact on dependent data like annotation or ontology mappings. This thesis presents novel methods and algorithms to deal with the evolution of ontology-based mappings. Thereby the generic infrastructure GOMMA is used and extended to manage and analyze the evolution of ontologies and mappings. First, a comparative evolution analysis for ontologies and mappings from three life science domains shows heavy changes in ontologies and mappings as well as an impact of ontology changes on the mappings. Hence, existing ontology mappings can become invalid and need to be migrated to current ontology versions. Thereby an expensive redetermination of the mappings should be avoided. This thesis introduces two generic algorithms to (semi-) automatically adapt ontology mappings: (1) a composition-based adaptation relies on the principle of mapping composition, and (2) a diff-based adaptation algorithm allows for individually handling change operations to update mappings. Both approaches reuse unaffected mapping parts, and adapt only affected parts of the mappings. An evaluation for very large biomedical ontologies and mappings shows that both approaches produce ontology mappings of high quality. Similarly, ontology changes may also affect ontology-based annotation mappings. The thesis introduces a generic evaluation approach to assess the quality of annotation mappings based on their evolution. Different quality measures allow for the identification of reliable annotations, e.g., based on their stability or provenance information. A comprehensive analysis of large annotation data sources shows numerous instabilities, e.g., due to the temporary absence of annotations. Such modifications may influence results of dependent applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. The question arises to what degree ontology and annotation changes may affect such analyses. Based on different stability measures the evaluation assesses change intensities of application results and gives insights whether users need to expect significant changes of their analysis results. Moreover, GOMMA is extended by large-scale ontology matching techniques. Such techniques are useful, a.o., to match new concepts during ontology mapping adaptation. Many existing match systems do not scale for aligning very large ontologies, e.g., from the life science domain. One efficient composition-based approach indirectly computes ontology mappings by reusing and combining existing mappings to intermediate ontologies. Intermediate ontologies can contain useful background knowledge such that the mapping quality can be improved compared to a direct match approach. Moreover, the thesis introduces general strategies for matching ontologies in parallel using several computing nodes. A size-based partitioning of the input ontologies enables good load balancing and scalability since smaller match tasks can be processed in parallel. The evaluation of the Ontology Alignment Evaluation Initiative (OAEI) compares GOMMA and other systems in terms of matching ontologies from different domains. Using the parallel and composition-based matching, GOMMA can achieve very good results w.r.t. efficiency and effectiveness, especially for ontologies from the life science domain.

The cotton crop is economically important and primarily grown for its fiber. Although the genus Gossypium consists of over 50 species, only four domesticated species produce spinnable fiber. However, the genes determine the molecular phenotype of fiber, and variation in their expression primarily contributes to associated phenotypic changes. Transcriptome analyses can elucidate the similarity or variation in gene expression (GE) among organisms at a given time or a circumstance. Even though several algorithms are available for analyzing such high-throughput data generated from RNA Sequencing (RNA-Seq), a reliable pipeline that includes a combination of tools such as an aligner for read mapping, an assembler for quantitating full-length transcripts, a differential gene expression (DGE) package for identifying differences in the transcripts across the samples, a gene ontology tool for assigning function, and enrichment and pathway mapping tools for finding interrelationships between genes based on their associated functions are needed. Therefore, this chapter first introduces the cotton crop, fiber phenotype, transcriptome, then discusses the basic RNA-Seq pipeline and later emphasizes various transcriptome analyses studies focused on genes associated with fiber quality and its attributes.

Since the emergence of SARS-CoV-2 in November 2019, there has been an exponential production of literature due to worldwide efforts to understand the interactions between the virus and the human body. Using an “in-house” developed script we retrieved gene annotations and identified phenotype enrichments. Human Phenotype Ontology terms were retrieved from the literature using the Onassis R package. This produced both disease-gene and disease-phenotype data as well as data for gene-phenotype interactions. Overall, we retrieved 181 human phenotypes that were identified by both approaches. Further in-depth analysis of these relationships could provide further insights in the molecular mechanisms related with the observed phenotypes, answers and hypotheses for key concepts within COVID-19 research.

Background: Neurodegenerative diseases share progressive loss of neurons and protein misfolding, which ultimately culminates in dementia; many diseases have been identified as causes of early-onset dementia (< 65 years of age) such as Huntington’s disease (HD) and early-onset Alzheimer’s disease (EOAD). Importantly, disease-specific genetic mutations have already been identified for HD and EOAD. Thus, one could suggest that the molecular link between these diseases may arise from alterations at the transcriptomic level, which is yet to be determined. Objective: We aimed at identifying transcriptome similarities between HD and EOAD. Methods: We collected data of the postmortem cerebral cortex from 1 HD and 6 AD microarray studies in the Gene Expression Omnibus. Of note, only subjects with age at death under 65 were selected (HD: n = 158, controls: n = 158; EOAD: n = 65, controls: n = 266). Differential expression and functional enrichment analyses were performed. Results: We identified 1,260 differentially expressed genes and 675 enriched gene ontology terms between HD and EOAD. Conclusion: Our results demonstrate a transcriptomic signature shared by HD and EOAD. Unveiling the similarities between these diseases at the transcriptomic level could advance our knowledge about pathogenesis and may help to develop therapeutic strategies targeting early-onset dementias.