A FAIR approach to genomics

The aim of this thesis was to increase our understanding on how genome information leads to function and phenotype. To address these questions, I developed a semantic systems biology framework capable of extracting knowledge, biological concepts and emergent system properties, from a vast array of publicly available genome information. In chapter 2, Empusa is described as an infrastructure that bridges the gap between the intended and actual content of a database. This infrastructure was used in chapters 3 and 4 to develop the framework. Chapter 3 describes the development of the Genome Biology Ontology Language and the GBOL stack of supporting tools enforcing consistency within and between the GBOL definitions in the ontology (OWL) and the Shape Expressions (ShEx) language describing the graph structure. A practical implementation of a semantic systems biology framework for FAIR (de novo) genome annotation is provided in chapter 4. The semantic framework and genome annotation tool described in this chapter has been used throughout this thesis to consistently, structurally and functionally annotate and mine microbial genomes used in chapter 5-10. In chapter 5, we introduced how the concept of protein domains and corresponding architectures can be used in comparative functional genomics to provide for a fast, efficient and scalable alternative to sequence-based methods. This allowed us to effectively compare and identify functional variations between hundreds to thousands of genomes. In chapter 6, we used 432 available complete Pseudomonas genomes to study the relationship between domain essentiality and persistence. In this chapter the focus was mainly on domains involved in metabolic functions. The metabolic domain space was explored for domain essentiality and persistence through the integration of heterogeneous data sources including six published metabolic models, a vast gene expression repository and transposon data. In chapter 7, the correlation between the expected and observed genotypes was explored using 16S-rRNA phylogeny and protein domain class content as input. In this chapter it was shown that domain class content yields a higher resolution in comparison to 16S-rRNA when analysing evolutionary distances. Using protein domain classes, we also were able to identify signifying domains, which may have important roles in shaping a species. To demonstrate the use of semantic systems biology workflows in a biotechnological setting we expanded the resource with more than 80.000 bacterial genomes. The genomic information of this resource was mined using a top down approach to identify strains having the trait for 1,3-propanediol production. This resulted in the molecular identification of 49 new species. In addition, we also experimentally verified that 4 species were capable of producing 1,3-propanediol. As discussed in chapter 10, the here developed semantic systems biology workflows were successfully applied in the discovery of key elements in symbiotic relationships, to improve functional genome annotation and in comparative genomics studies. Wet/dry-lab collaboration was often at the basis of the obtained results. The success of the collaboration between the wet and dry field, prompted me to develop an undergraduate course in which the concept of the “Moist” workflow was introduced (Chapter 9).</p

Semantic systems biology of prokaryotes : heterogeneous data integration to understand bacterial metabolism

The goal of this thesis is to improve the prediction of genotype to phenotypeassociations with a focus on metabolic phenotypes of prokaryotes. This goal isachieved through data integration, which in turn required the development ofsupporting solutions based on semantic web technologies. Chapter 1 providesan introduction to the challenges associated to data integration. Semantic webtechnologies provide solutions to some of these challenges and the basics ofthese technologies are explained in the Introduction. Furthermore, the ba-sics of constraint based metabolic modeling and construction of genome scalemodels (GEM) are also provided. The chapters in the thesis are separated inthree related topics: chapters 2, 3 and 4 focus on data integration based onheterogeneous networks and their application to the human pathogen M. tu-berculosis; chapters 5, 6, 7, 8 and 9 focus on the semantic web based solutionsto genome annotation and applications thereof; and chapter 10 focus on thefinal goal to associate genotypes to phenotypes using GEMs. Chapter 2 provides the prototype of a workflow to efficiently analyze in-formation generated by different inference and prediction methods. This me-thod relies on providing the user the means to simultaneously visualize andanalyze the coexisting networks generated by different algorithms, heteroge-neous data sets, and a suite of analysis tools. As a show case, we have ana-lyzed the gene co-expression networks of M. tuberculosis generated using over600 expression experiments. Hereby we gained new knowledge about theregulation of the DNA repair, dormancy, iron uptake and zinc uptake sys-tems. Furthermore, it enabled us to develop a pipeline to integrate ChIP-seqdat and a tool to uncover multiple regulatory layers. In chapter 3 the prototype presented in chapter 2 is further developedinto the Synchronous Network Data Integration (SyNDI) framework, whichis based on Cytoscape and Galaxy. The functionality and usability of theframework is highlighted with three biological examples. We analyzed thedistinct connectivity of plasma metabolites in networks associated with highor low latent cardiovascular disease risk. We obtained deeper insights froma few similar inflammatory response pathways in Staphylococcus aureus infec-tion common to human and mouse. We identified not yet reported regulatorymotifs associated with transcriptional adaptations of M. tuberculosis.In chapter 4 we present a review providing a systems level overview ofthe molecular and cellular components involved in divalent metal homeosta-sis and their role in regulating the three main virulence strategies of M. tu-berculosis: immune modulation, dormancy and phagosome escape. With theuse of the tools presented in chapter 2 and 3 we identified a single regulatorycascade for these three virulence strategies that respond to limited availabilityof divalent metals in the phagosome. The tools presented in chapter 2 and 3 achieve data integration throughthe use of multiple similarity, coexistence, coexpression and interaction geneand protein networks. However, the presented tools cannot store additional(genome) annotations. Therefore, we applied semantic web technologies tostore and integrate heterogeneous annotation data sets. An increasing num-ber of widely used biological resources are already available in the RDF datamodel. There are however, no tools available that provide structural overviewsof these resources. Such structural overviews are essential to efficiently querythese resources and to assess their structural integrity and design. There-fore, in chapter 5, I present RDF2Graph, a tool that automatically recoversthe structure of an RDF resource. The generated overview enables users tocreate complex queries on these resources and to structurally validate newlycreated resources. Direct functional comparison support genotype to phenotype predictions.A prerequisite for a direct functional comparison is consistent annotation ofthe genetic elements with evidence statements. However, the standard struc-tured formats used by the public sequence databases to present genome an-notations provide limited support for data mining, hampering comparativeanalyses at large scale. To enable interoperability of genome annotations fordata mining application, we have developed the Genome Biology OntologyLanguage (GBOL) and associated infrastructure (GBOL stack), which is pre-sented in chapter 6. GBOL is provenance aware and thus provides a consistentrepresentation of functional genome annotations linked to the provenance.The provenance of a genome annotation describes the contextual details andderivation history of the process that resulted in the annotation. GBOL is mod-ular in design, extensible and linked to existing ontologies. The GBOL stackof supporting tools enforces consistency within and between the GBOL defi-nitions in the ontology. Based on GBOL, we developed the genome annotation pipeline SAPP (Se-mantic Annotation Platform with Provenance) presented in chapter 7. SAPPautomatically predicts, tracks and stores structural and functional annotationsand associated dataset- and element-wise provenance in a Linked Data for-mat, thereby enabling information mining and retrieval with Semantic Webtechnologies. This greatly reduces the administrative burden of handling mul-tiple analysis tools and versions thereof and facilitates multi-level large scalecomparative analysis. In turn this can be used to make genotype to phenotypepredictions. The development of GBOL and SAPP was done simultaneously. Duringthe development we realized that we had to constantly validated the data ex-ported to RDF to ensure coherence with the ontology. This was an extremelytime consuming process and prone to error, therefore we developed the Em-pusa code generator. Empusa is presented in chapter 8. SAPP has been successfully used to annotate 432 sequenced Pseudomonas strains and integrate the resulting annotation in a large scale functional com-parison using protein domains. This comparison is presented in chapter 9.Additionally, data from six metabolic models, nearly a thousand transcrip-tome measurements and four large scale transposon mutagenesis experimentswere integrated with the genome annotations. In this way, we linked gene es-sentiality, persistence and expression variability. This gave us insight into thediversity, versatility and evolutionary history of the Pseudomonas genus, whichcontains some important pathogens as well some useful species for bioengi-neering and bioremediation purposes. Genome annotation can be used to create GEM, which can be used to betterlink genotypes to phenotypes. Bio-Growmatch, presented in chapter 10, istool that can automatically suggest modification to improve a GEM based onphenotype data. Thereby integrating growth data into the complete processof modelling the metabolism of an organism. Chapter 11 presents a general discussion on how the chapters contributedthe central goal. After which I discuss provenance requirements for data reuseand integration. I further discuss how this can be used to further improveknowledge generation. The acquired knowledge could, in turn, be used to de-sign new experiments. The principles of the dry-lab cycle and how semantictechnologies can contribute to establish these cycles are discussed in chapter11. Finally a discussion is presented on how to apply these principles to im-prove the creation and usability of GEM’s.</p

Metrics and methods for comparative ontology evaluation

While progress has been made toward describing the need for ontology evaluation and offering proposals concerning what properties to measure and how, work remains to develop ontology evaluation as a rigorous discipline. Ontologies as information artifacts have a variety of aspects that can inform their evaluation, both in terms of what is evaluated and the metrics used. Ontology evaluation as a discipline requires (1) having a systematic account of the different aspects of ontologies and the properties relevant to those aspects, (2) critically developing methods for examining those properties, (3) developing comparative metrics that allow ontology engineers to compare the effects of various modeling choices and allow users to compare the merits of existing ontologies, and (4) charting possible pitfalls of evaluation. This paper considers various properties of ontologies that have been proposed and organizes these properties according to different aspects of ontologies. To begin bringing previous work together and to illustrate where pitfalls and potential solutions might enter into a rigorous evaluation, I offer a more in depth (though still partial) analysis of evaluating the correctness of ontologies. I conclude with a discussion of next steps in systematizing ontology evaluation

Will Nano-Butlers Work for Micro-Payments? Innovation in Business Services Model may Reduce Cost of Delivering Global Healthcare Services

This paper represents an emerging view of personalized care and patient-centric systems approach. It integrates biomedical informatics and business services. A potentially innovative model may evolve from this convergence and may serve as a global template to reduce cost of service. The future of global healthcare may increasingly rely on “sense and then, respond” systems but excluding the instances of exception management, necessary for accidents and emergencies. Solutions suggested in this paper are neither complete nor a panacea but are elements that deserve inclusion in the delivery of healthcare that may combine a portfolio of approaches to suit the needs of the community. As a potential future direction to improve analytics in healthcare, the concept of molecular semantics is proposed

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications