Processing genome-wide association studies within a repository of heterogeneous genomic datasets

Background Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions. Results To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multisample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals. Conclusions As a result of our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows

From a Conceptual Model to a Knowledge Graph for Genomic Datasets

Data access at genomic repositories is problematic, as data is described by heterogeneous and hardly comparable metadata. We previously introduced a unified conceptual schema, collected metadata in a single repository and provided classical search methods upon them. We here propose a new paradigm to support semantic search of integrated genomic metadata, based on the Genomic Knowledge Graph, a semantic graph of genomic terms and concepts, which combines the original information provided by each source with curated terminological content from specialized ontologies. Commercial knowledge-assisted search is designed for transparently supporting keyword-based search without explaining inferences; in biology, inference understanding is instead critical. For this reason, we propose a graph-based visual search for data exploration; some expert users can navigate the semantic graph along the conceptual schema, enriched with simple forms of homonyms and term hierarchies, thus understanding the semantic reasoning behind query results

Exploiting Conceptual Modeling for Searching Genomic Metadata: A Quantitative and Qualitative Empirical Study

Providing a common data model for the metadata of several heterogenous genomic data sources is hard, as they do not share any standard or agreed practice for metadata description. Two years ago we managed to discover a subset of common metadata present in most sources and to organize it as a smart genomic conceptual model (GCM); the model has been instrumental to our efforts in the development of a major software pipeline for data integration. More recently, we developed a user-friendly search interface, based on a simplified version of GCM. In this paper, we report our evaluation of the effectiveness of this new user interface. Specifically, we present the results of a compendious empirical study to answer the research question: How much is such a simple interface well-understood by a standard user? The target of this study is a mixed population, composed by biologists, bioinformaticians and computer scientists. The result of our empirical study shows that the users were successful in producing search queries starting from their natural language description, as they did it with good accuracy and small error rate. The study also shows that most users were generally satisfied; it provides indications on how to improve our search system and how to continue our effort in integration of genomic sources. We are consequently adapting the user interface, that will be soon opened to public use

Developing Predictive Molecular Maps of Human Disease through Community-based Modeling

The failure of biology to identify the molecular causes of disease has led to disappointment in the rate of development of new medicines. By combining the power of community-based modeling with broad access to large datasets on a platform that promotes reproducible analyses we can work towards more predictive molecular maps that can deliver better therapeutics

Conceptual modeling for genomics: Building an integrated repository of open data

Many repositories of open data for genomics, collected by world-wide consortia, are important enablers of biological research; moreover, all experimental datasets leading to publications in genomics must be deposited to public repositories and made available to the research community. These datasets are typically used by biologists for validating or enriching their experiments; their content is documented by metadata. However, emphasis on data sharing is not matched by accuracy in data documentation; metadata are not standardized across the sources and often unstructured and incomplete. In this paper, we propose a conceptual model of genomic metadata, whose purpose is to query the underlying data sources for locating relevant experimental datasets. First, we analyze the most typical metadata attributes of genomic sources and define their semantic properties. Then, we use a top-down method for building a global-as-view integrated schema, by abstracting the most important conceptual properties of genomic sources. Finally, we describe the validation of the conceptual model by mapping it to three well-known data sources: TCGA, ENCODE, and Gene Expression Omnibus

The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page

Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions