A systems approach delivers a functional microRNA catalog and expanded targets for seizure suppression in temporal lobe epilepsy.

Temporal lobe epilepsy is the most common drug-resistant form of epilepsy in adults. The reorganization of neural networks and the gene expression landscape underlying pathophysiologic network behavior in brain structures such as the hippocampus has been suggested to be controlled, in part, by microRNAs. To systematically assess their significance, we sequenced Argonaute-loaded microRNAs to define functionally engaged microRNAs in the hippocampus of three different animal models in two species and at six time points between the initial precipitating insult through to the establishment of chronic epilepsy. We then selected commonly up-regulated microRNAs for a functional in vivo therapeutic screen using oligonucleotide inhibitors. Argonaute sequencing generated 1.44 billion small RNA reads of which up to 82% were microRNAs, with over 400 unique microRNAs detected per model. Approximately half of the detected microRNAs were dysregulated in each epilepsy model. We prioritized commonly up-regulated microRNAs that were fully conserved in humans and designed custom antisense oligonucleotides for these candidate targets. Antiseizure phenotypes were observed upon knockdown of miR-10a-5p, miR-21a-5p, and miR-142a-5p and electrophysiological analyses indicated broad safety of this approach. Combined inhibition of these three microRNAs reduced spontaneous seizures in epileptic mice. Proteomic data, RNA sequencing, and pathway analysis on predicted and validated targets of these microRNAs implicated derepressed TGF-ß signaling as a shared seizure-modifying mechanism. Correspondingly, inhibition of TGF-ß signaling occluded the antiseizure effects of the antagomirs. Together, these results identify shared, dysregulated, and functionally active microRNAs during the pathogenesis of epilepsy which represent therapeutic antiseizure targets.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies.

FINDbase (http://www.findbase.org) is a comprehensive data repository that records the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants leading mostly to monogenic disorders and pharmacogenomics biomarkers. The database also records the incidence of rare genetic diseases in various populations, all in well-distinct data modules. Here, we report extensive data content updates in all data modules, with direct implications to clinical pharmacogenomics. Also, we report significant new developments in FINDbase, namely (i) the release of a new version of the ETHNOS software that catalyzes development curation of national/ethnic genetic databases, (ii) the migration of all FINDbase data content into 90 distinct national/ethnic mutation databases, all built around Microsoft's PivotViewer (http://www.getpivot.com) software (iii) new data visualization tools and (iv) the interrelation of FINDbase with DruGeVar database with direct implications in clinical pharmacogenomics. The abovementioned updates further enhance the impact of FINDbase, as a key resource for Genomic Medicine applications.

Genotype-phenotype databases: challenges and solutions for the post-genomic era.

The flow of research data concerning the genetic basis of health and disease is rapidly increasing in speed and complexity. In response, many projects are seeking to ensure that there are appropriate informatics tools, systems and databases available to manage and exploit this flood of information. Previous solutions, such as central databases, journal-based publication and manually intensive data curation, are now being enhanced with new systems for federated databases, database publication, and more automated management of data flows and quality control. Along with emerging technologies that enhance connectivity and data retrieval, these advances should help to create a powerful knowledge environment for genotype-phenotype information.

The Finnish disease heritage database (FinDis) update-a database for the genes mutated in the Finnish disease heritage brought to the next-generation sequencing era.

The Finnish Disease Heritage Database (FinDis) (http://findis.org) was originally published in 2004 as a centralized information resource for rare monogenic diseases enriched in the Finnish population. The FinDis database originally contained 405 causative variants for 30 diseases. At the time, the FinDis database was a comprehensive collection of data, but since 1994, a large amount of new information has emerged, making the necessity to update the database evident. We collected information and updated the database to contain genes and causative variants for 35 diseases, including six more genes and more than 1,400 additional disease-causing variants. Information for causative variants for each gene is collected under the LOVD 3.0 platform, enabling easy updating. The FinDis portal provides a centralized resource and user interface to link information on each disease and gene with variant data in the LOVD 3.0 platform. The software written to achieve this has been open-sourced and made available on GitHub (http://github.com/findis-db), allowing biomedical institutions in other countries to present their national data in a similar way, and to both contribute to, and benefit from, standardized variation data. The updated FinDis portal provides a unique resource to assist patient diagnosis, research, and the development of new cures.

VarioML framework for comprehensive variation data representation and exchange.

BACKGROUND: Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. RESULTS: The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. CONCLUSIONS: VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.

Observ-OM and Observ-TAB: Universal syntax solutions for the integration, search, and exchange of phenotype and genotype information.

Genetic and epidemiological research increasingly employs large collections of phenotypic and molecular observation data from high quality human and model organism samples. Standardization efforts have produced a few simple formats for exchange of these various data, but a lightweight and convenient data representation scheme for all data modalities does not exist, hindering successful data integration, such as assignment of mouse models to orphan diseases and phenotypic clustering for pathways. We report a unified system to integrate and compare observation data across experimental projects, disease databases, and clinical biobanks. The core object model (Observ-OM) comprises only four basic concepts to represent any kind of observation: Targets, Features, Protocols (and their Applications), and Values. An easy-to-use file format (Observ-TAB) employs Excel to represent individual and aggregate data in straightforward spreadsheets. The systems have been tested successfully on human biobank, genome-wide association studies, quantitative trait loci, model organism, and patient registry data using the MOLGENIS platform to quickly setup custom data portals. Our system will dramatically lower the barrier for future data sharing and facilitate integrated search across panels and species. All models, formats, documentation, and software are available for free and open source (LGPLv3) at http://www.observ-om.org.

A genome-wide association study of monozygotic twin-pairs suggests a locus related to variability of serum high-density lipoprotein cholesterol.

Genome-wide association analysis on monozygotic twin-pairs offers a route to discovery of gene environment interactions through testing for variability loci associated with sensitivity to individual environment/lifestyle. We present a genome-wide scan of loci associated with intra-pair differences in serum lipid and apolipoprotein levels. We report data for 1,720 monozygotic female twin-pairs from GenomEUtwin project with 2.5 million SNPs, imputed or genotyped, and measured serum lipid fractions for both twins. We found one locus associated with intra-pair differences in high-density lipoprotein cholesterol, rs2483058 in an intron of SRGAP2, where twins carrying the C allele are more sensitive to environmental factors(P=3.98 x 10-8). We followed up the association in further genotyped monozygotic twins (N= 1,261),which showed a moderate association for the variant (P= 0.200, same direction of an effect). In addition,we report a new association on the level of apolipoprotein A-ll (P= 4.03 x 1 o-8).

The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button.

BACKGROUND: There is a huge demand on bioinformaticians to provide their biologists with user friendly and scalable software infrastructures to capture, exchange, and exploit the unprecedented amounts of new *omics data. We here present MOLGENIS, a generic, open source, software toolkit to quickly produce the bespoke MOLecular GENetics Information Systems needed. METHODS: The MOLGENIS toolkit provides bioinformaticians with a simple language to model biological data structures and user interfaces. At the push of a button, MOLGENIS' generator suite automatically translates these models into a feature-rich, ready-to-use web application including database, user interfaces, exchange formats, and scriptable interfaces. Each generator is a template of SQL, JAVA, R, or HTML code that would require much effort to write by hand. This 'model-driven' method ensures reuse of best practices and improves quality because the modeling language and generators are shared between all MOLGENIS applications, so that errors are found quickly and improvements are shared easily by a re-generation. A plug-in mechanism ensures that both the generator suite and generated product can be customized just as much as hand-written software. RESULTS: In recent years we have successfully evaluated the MOLGENIS toolkit for the rapid prototyping of many types of biomedical applications, including next-generation sequencing, GWAS, QTL, proteomics and biobanking. Writing 500 lines of model XML typically replaces 15,000 lines of hand-written programming code, which allows for quick adaptation if the information system is not yet to the biologist's satisfaction. Each application generated with MOLGENIS comes with an optimized database back-end, user interfaces for biologists to manage and exploit their data, programming interfaces for bioinformaticians to script analysis tools in R, Java, SOAP, REST/JSON and RDF, a tab-delimited file format to ease upload and exchange of data, and detailed technical documentation. Existing databases can be quickly enhanced with MOLGENIS generated interfaces using the 'ExtractModel' procedure. CONCLUSIONS: The MOLGENIS toolkit provides bioinformaticians with a simple model to quickly generate flexible web platforms for all possible genomic, molecular and phenotypic experiments with a richness of interfaces not provided by other tools. All the software and manuals are available free as LGPLv3 open source at http://www.molgenis.org.

The phenotype and genotype experiment object model (PaGE-OM): a robust data structure for information related to DNA variation.

Torrents of genotype-phenotype data are being generated, all of which must be captured, processed, integrated, and exploited. To do this optimally requires the use of standard and interoperable "object models," providing a description of how to partition the total spectrum of information being dealt with into elemental "objects" (such as "alleles," "genotypes," "phenotype values," "methods") with precisely stated logical interrelationships (such as "A objects are made up from one or more B objects"). We herein propose the Phenotype and Genotype Experiment Object Model (PaGE-OM; www.pageom.org), which has been tested and implemented in conjunction with several major databases, and approved as a standard by the Object Management Group (OMG). PaGE-OM is open-source, ready for use by the wider community, and can be further developed as needs arise. It will help to improve information management, assist data integration, and simplify the task of informatics resource design and construction for genotype and phenotype data projects.

The federated database--a basis for biobank-based post-genome studies, integrating phenome and genome data from 600,000 twin pairs in Europe.

Integration of complex data and data management represent major challenges in large-scale biobank-based post-genome era research projects like GenomEUtwin (an international collaboration between eight Twin Registries) with extensive amounts of genotype and phenotype data combined from different data sources located in different countries. The challenge lies not only in data harmonization and constant update of clinical details in various locations, but also in the heterogeneity of data storage and confidentiality of sensitive health-related and genetic data. Solid infrastructure must be built to provide secure, but easily accessible and standardized, data exchange also facilitating statistical analyses of the stored data. Data collection sites desire to have full control of the accumulation of data, and at the same time the integration should facilitate effortless slicing and dicing of the data for different types of data pooling and study designs. Here we describe how we constructed a federated database infrastructure for genotype and phenotype information collected in seven European countries and Australia and connected this database setting via a network called TwinNET to guarantee effortless data exchange and pooled analyses. This federated database system offers a powerful facility for combining different types of information from multiple data sources. The system is transparent to end users and application developers, since it makes the set of federated data sources look like a single system. The user need not be aware of the format or site where the data are stored, the language or programming interface of the data source, how the data are physically stored, whether they are partitioned and/or replicated or what networking protocols are used. The user sees a single standardized interface with the desired data elements for pooled analyses.

Genetics in an isolated population like Finland: a different basis for genomic medicine?

A unique genetic background in an isolated population like that of Finland offers special opportunities for genetic research as well as for applying the genetic developments to the health care. On the other hand, the different genetic background may require local attempts to develop diagnostics and treatment as the selection of diseases and mutations differs from that in the other populations. In this review, we describe the experiences of research and health care in this genetic isolate starting from the identification of specific monogenic diseases enriched in the Finnish population all the way to implementing the knowledge of the unique genetic background to genomic medicine at population level.

Conservation of human alternative splice events in mouse.

Human and mouse genomes share similar long-range sequence organization, and have most of their genes being homologous. As alternative splicing is a frequent and important aspect of gene regulation, it is of interest to assess the level of conservation of alternative splicing. We examined mouse transcript data sets (EST and mRNA) for the presence of transcripts that both make spliced-alignment with the draft mouse genome sequence and demonstrate conservation of human transcript-confirmed alternative and constitutive splice junctions. This revealed 15% of alternative and 67% of constitutive splice junctions as conserved; however, these numbers are patently dependent on the extent of transcript coverage. Transcript coverage of conserved splice patterns is found to correlate well between human and mouse. A model, which extrapolates from observed levels of conservation at increasing levels of transcript support, estimates overall conservation of 61% of alternative and 74% of constitutive splice junctions, albeit with broad confidence intervals. Observed numbers of conserved alternative splicing events agreed with those expected on the basis of the model. Thus, it is apparent that many, and probably most, alternative splicing events are conserved between human and mouse. This, combined with the preservation of alternative frame stop codons in conserved frame breaking events, indicates a high level of commonality in patterns of gene expression between these two species.

ASD: the Alternative Splicing Database.

Alternative splicing is widespread in mammalian gene expression, and variant splice patterns are often specific to different stages of development, particular tissues or a disease state. There is a need to systematically collect data on alternatively spliced exons, introns and splice isoforms, and to annotate this data. The Alternative Splicing Database consortium has been addressing this need, and is committed to maintaining and developing a value-added database of alternative splice events, and of experimentally verified regulatory mechanisms that mediate splice variants. In this paper we present two of the products from this project: namely, a database of computationally delineated alternative splice events as seen in alignments of EST/cDNA sequences with genome sequences, and a database of alternatively spliced exons collected from literature. The reported splice events are from nine different organisms and are annotated for various biological features including expression states and cross-species conservation. The data are presented on our ASD web pages (http://www.ebi.ac.uk/asd).

NordicDB: a Nordic pool and portal for genome-wide control data.

A cost-efficient way to increase power in a genetic association study is to pool controls from different sources. The genotyping effort can then be directed to large case series. The Nordic Control database, NordicDB, has been set up as a unique resource in the Nordic area and the data are available for authorized users through the web portal (http://www.nordicdb.org). The current version of NordicDB pools together high-density genome-wide SNP information from ∼5000 controls originating from Finnish, Swedish and Danish studies and shows country-specific allele frequencies for SNP markers. The genetic homogeneity of the samples was investigated using multidimensional scaling (MDS) analysis and pairwise allele frequency differences between the studies. The plot of the first two MDS components showed excellent resemblance to the geographical placement of the samples, with a clear NW-SE gradient. We advise researchers to assess the impact of population structure when incorporating NordicDB controls in association studies. This harmonized Nordic database presents a unique genome-wide resource for future genetic association studies in the Nordic countries.

Geographical structure and differential natural selection among North European populations.

Population structure can provide novel insight into the human past, and recognizing and correcting for such stratification is a practical concern in gene mapping by many association methodologies. We investigate these patterns, primarily through principal component (PC) analysis of whole genome SNP polymorphism, in 2099 individuals from populations of Northern European origin (Ireland, United Kingdom, Netherlands, Denmark, Sweden, Finland, Australia, and HapMap European-American). The major trends (PC1 and PC2) demonstrate an ability to detect geographic substructure, even over a small area like the British Isles, and this information can then be applied to finely dissect the ancestry of the European-Australian and European-American samples. They simultaneously point to the importance of considering population stratification in what might be considered a small homogeneous region. There is evidence from F(ST)-based analysis of genic and nongenic SNPs that differential positive selection has operated across these populations despite their short divergence time and relatively similar geographic and environmental range. The pressure appears to have been focused on genes involved in immunity, perhaps reflecting response to infectious disease epidemic. Such an event may explain a striking selective sweep centered on the rs2508049-G allele, close to the HLA-G gene on chromosome 6. Evidence of the sweep extends over a 8-Mb/3.5-cM region. Overall, the results illustrate the power of dense genotype and sample data to explore regional population variation, the events that have crafted it, and their implications in both explaining disease prevalence and mapping these genes by association.

Data modeling and data communication in GenomEUtwin.

Database infrastructure has become a critical component for competitive life sciences research and discovery. The explosion of data requires that the data are properly loaded, accessed, managed, queried, analyzed, and shared with others. The key purpose of the population-based twin cohorts housed at different institutions in Europe is to gather an extremely large quantity of information from their twin populations, and share it. Longitudinal research over a long period of time, hopefully generations, demands completely new methods and systems to handle the gathering of information and storing. These cohorts bring to the fore problems concerning the need for a standardization of research data and a computer and storage strategy. In the following we describe the preliminary strategy being implemented in the Database Core of GenomEUtwin.