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

MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians

MOTIVATION

The volume and complexity of biological data increases rapidly. Many clinical professionals and biomedical researchers without a bioinformatics background are generating big '-omics' data, but do not always have the tools to manage, process or publicly share these data.

RESULTS

Here we present MOLGENIS Research, an open-source web-application to collect, manage, analyze, visualize and share large and complex biomedical datasets, without the need for advanced bioinformatics skills.

AVAILABILITY AND IMPLEMENTATION

MOLGENIS Research is freely available (open source software). It can be installed from source code (see http://github.com/molgenis), downloaded as a precompiled WAR file (for your own server), setup inside a Docker container (see http://molgenis.github.io), or requested as a Software-as-a-Service subscription. For a public demo instance and complete installation instructions see http://molgenis.org/research.

What incentives increase data sharing in health and medical research? A systematic review

BACKGROUND

The foundation of health and medical research is data. Data sharing facilitates the progress of research and strengthens science. Data sharing in research is widely discussed in the literature; however, there are seemingly no evidence-based incentives that promote data sharing.

METHODS

A systematic review (registration: 10.17605/OSF.IO/6PZ5E) of the health and medical research literature was used to uncover any evidence-based incentives, with pre- and post-empirical data that examined data sharing rates. We were also interested in quantifying and classifying the number of opinion pieces on the importance of incentives, the number observational studies that analysed data sharing rates and practices, and strategies aimed at increasing data sharing rates.

RESULTS

Only one incentive (using open data badges) has been tested in health and medical research that examined data sharing rates. The number of opinion pieces (n = 85) out-weighed the number of article-testing strategies (n = 76), and the number of observational studies exceeded them both (n = 106).

CONCLUSIONS

Given that data is the foundation of evidence-based health and medical research, it is paradoxical that there is only one evidence-based incentive to promote data sharing. More well-designed studies are needed in order to increase the currently low rates of data sharing.

MOLGENIS/connect: a system for semi-automatic integration of heterogeneous phenotype data with applications in biobanks

Motivation: While the size and number of biobanks, patient registries and other data collections are increasing, biomedical researchers still often need to pool data for statistical power, a task that requires time-intensive retrospective integration.

Results: To address this challenge, we developed MOLGENIS/connect, a semi-automatic system to find, match and pool data from different sources. The system shortlists relevant source attributes from thousands of candidates using ontology-based query expansion to overcome variations in terminology. Then it generates algorithms that transform source attributes to a common target DataSchema. These include unit conversion, categorical value matching and complex conversion patterns (e.g. calculation of BMI). In comparison to human-experts, MOLGENIS/connect was able to auto-generate 27% of the algorithms perfectly, with an additional 46% needing only minor editing, representing a reduction in the human effort and expertise needed to pool data.

Availability and Implementation: Source code, binaries and documentation are available as open-source under LGPLv3 from http://github.com/molgenis/molgenis and www.molgenis.org/connect.

Contact: m.a.swertz@rug.nl

Supplementary information:Supplementary data are available at Bioinformatics online.

BiobankConnect: software to rapidly connect data elements for pooled analysis across biobanks using ontological and lexical indexing

OBJECTIVE

Pooling data across biobanks is necessary to increase statistical power, reveal more subtle associations, and synergize the value of data sources. However, searching for desired data elements among the thousands of available elements and harmonizing differences in terminology, data collection, and structure, is arduous and time consuming.

MATERIALS AND METHODS

To speed up biobank data pooling we developed BiobankConnect, a system to semi-automatically match desired data elements to available elements by: (1) annotating the desired elements with ontology terms using BioPortal; (2) automatically expanding the query for these elements with synonyms and subclass information using OntoCAT; (3) automatically searching available elements for these expanded terms using Lucene lexical matching; and (4) shortlisting relevant matches sorted by matching score.

RESULTS

We evaluated BiobankConnect using human curated matches from EU-BioSHaRE, searching for 32 desired data elements in 7461 available elements from six biobanks. We found 0.75 precision at rank 1 and 0.74 recall at rank 10 compared to a manually curated set of relevant matches. In addition, best matches chosen by BioSHaRE experts ranked first in 63.0% and in the top 10 in 98.4% of cases, indicating that our system has the potential to significantly reduce manual matching work.

CONCLUSIONS

BiobankConnect provides an easy user interface to significantly speed up the biobank harmonization process. It may also prove useful for other forms of biomedical data integration. All the software can be downloaded as a MOLGENIS open source app from http://www.github.com/molgenis, with a demo available at http://www.biobankconnect.org.

Next generation phenotyping using the unified medical language system

Background

Structured information within patient medical records represents a largely untapped treasure trove of research data. In the United States, privacy issues notwithstanding, this has recently become more accessible thanks to the increasing adoption of electronic health records (EHR) and health care data standards fueled by the Meaningful Use legislation. The other side of the coin is that it is now becoming increasingly more difficult to navigate the profusion of many disparate clinical terminology standards, which often span millions of concepts.

Objective

The objective of our study was to develop a methodology for integrating large amounts of structured clinical information that is both terminology agnostic and able to capture heterogeneous clinical phenotypes including problems, procedures, medications, and clinical results (such as laboratory tests and clinical observations). In this context, we define phenotyping as the extraction of all clinically relevant features contained in the EHR.

Methods

The scope of the project was framed by the Common Meaningful Use (MU) Dataset terminology standards; the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), RxNorm, the Logical Observation Identifiers Names and Codes (LOINC), the Current Procedural Terminology (CPT), the Health care Common Procedure Coding System (HCPCS), the International Classification of Diseases Ninth Revision Clinical Modification (ICD-9-CM), and the International Classification of Diseases Tenth Revision Clinical Modification (ICD-10-CM). The Unified Medical Language System (UMLS) was used as a mapping layer among the MU ontologies. An extract, load, and transform approach separated original annotations in the EHR from the mapping process and allowed for continuous updates as the terminologies were updated. Additionally, we integrated all terminologies into a single UMLS derived ontology and further optimized it to make the relatively large concept graph manageable.

Results

The initial evaluation was performed with simulated data from the Clinical Avatars project using 100,000 virtual patients undergoing a 90 day, genotype guided, warfarin dosing protocol. This dataset was annotated with standard MU terminologies, loaded, and transformed using the UMLS. We have deployed this methodology to scale in our in-house analytics platform using structured EHR data for 7931 patients (12 million clinical observations) treated at the Froedtert Hospital. A demonstration limited to Clinical Avatars data is available on the Internet using the credentials user “jmirdemo” and password “jmirdemo”.

Conclusions

Despite its inherent complexity, the UMLS can serve as an effective interface terminology for many of the clinical data standards currently used in the health care domain.

An overview and online registry of microvillus inclusion disease patients and their MYO5B mutations

Microvillus inclusion disease (MVID) is one of the most severe congenital intestinal disorders and is characterized by neonatal secretory diarrhea and the inability to absorb nutrients from the intestinal lumen. MVID is associated with patient-, family-, and ancestry-unique mutations in the MYO5B gene, encoding the actin-based motor protein myosin Vb. Here, we review the MYO5B gene and all currently known MYO5B mutations and for the first time methodologically categorize these with regard to functional protein domains and recurrence in MYO7A associated with Usher syndrome and other myosins. We also review animal models for MVID and the latest data on functional studies related to the myosin Vb protein. To congregate existing and future information on MVID geno-/phenotypes and facilitate its quick and easy sharing among clinicians and researchers, we have constructed an online MOLGENIS-based international patient registry (www.MVID-central.org). This easily accessible database currently contains detailed information of 137 MVID patients together with reported clinical/phenotypic details and 41 unique MYO5B mutations, of which several unpublished. The future expansion and prospective nature of this registry is expected to improve disease diagnosis, prognosis, and genetic counseling.

MEFV mutations affecting pyrin amino acid 577 cause autosomal dominant autoinflammatory disease

OBJECTIVES

Autoinflammatory disorders are disorders of the innate immune system. Standard genetic testing provided no correct diagnosis in a female patient from a non-consanguineous family of British descent with a colchicine-responsive autosomal dominant periodic fever syndrome. We aimed to unravel the genetic cause of the symptoms.

METHODS

Whole exome sequencing was used to screen for novel sequence variants, which were validated by direct Sanger sequencing. Ex vivo stimulation with peripheral blood mononuclear cells was performed to study the functional consequences of the mutation. mRNA and cytokine levels were measured by quantitative PCR and ELISA, respectively.

RESULTS

Whole exome sequencing revealed a novel missense sequence variant, not seen in around 6800 controls, mapping to exon 8 of the MEFV gene (c.1730C>A; p.T577N), co-segregating perfectly with disease in this family. Other mutations at the same amino acid (c.1730C>G; p.T577S and c.1729A>T; p.T577S) were found in a family of Turkish descent, with autosomal dominant inheritance of familial Mediterranean fever (FMF)-like phenotype, and a Dutch patient, respectively. Moreover, a mutation (c.1729A>G; p.T577A) was detected in two Dutch siblings, who had episodes of inflammation of varying severity not resembling FMF. Peripheral blood mononuclear cells from one patient of the index family showed increased basal interleukin 1β mRNA levels and cytokine responses after lipopolysaccharide stimulation. Responses normalised with colchicine treatment.

CONCLUSIONS

Heterozygous mutations at amino acid position 577 of pyrin can induce an autosomal dominant autoinflammatory syndrome. This suggests that T577, located in front of the C-terminal B30.2/SPRY domain, is crucial for pyrin function.

Mutations in potassium channel kcnd3 cause spinocerebellar ataxia type 19

OBJECTIVE

To identify the causative gene for the neurodegenerative disorder spinocerebellar ataxia type 19 (SCA19) located on chromosomal region 1p21-q21.

METHODS

Exome sequencing was used to identify the causal mutation in a large SCA19 family. We then screened 230 ataxia families for mutations located in the same gene (KCND3, also known as Kv4.3) using high-resolution melting. SCA19 brain autopsy material was evaluated, and in vitro experiments using ectopic expression of wild-type and mutant Kv4.3 were used to study protein localization, stability, and channel activity by patch-clamping.

RESULTS

We detected a T352P mutation in the third extracellular loop of the voltage-gated potassium channel KCND3 that cosegregated with the disease phenotype in our original family. We identified 2 more novel missense mutations in the channel pore (M373I) and the S6 transmembrane domain (S390N) in 2 other ataxia families. T352P cerebellar autopsy material showed severe Purkinje cell degeneration, with abnormal intracellular accumulation and reduced protein levels of Kv4.3 in their soma. Ectopic expression of all mutant proteins in HeLa cells revealed retention in the endoplasmic reticulum and enhanced protein instability, in contrast to wild-type Kv4.3 that was localized on the plasma membrane. The regulatory β subunit Kv channel interacting protein 2 was able to rescue the membrane localization and the stability of 2 of the 3 mutant Kv4.3 complexes. However, this either did not restore the channel function of the membrane-located mutant Kv4.3 complexes or restored it only partially.

INTERPRETATION

KCND3 mutations cause SCA19 by impaired protein maturation and/or reduced channel function.

The facial evolution: looking backward and moving forward

Three-dimensional (3D) facial analysis is ideal for high-resolution, nonionizing, noninvasive objective, high-throughput phenotypic, and phenomic studies. It is a natural complement to (epi)genetic technologies to facilitate advances in the understanding of rare and common diseases. The face is uniquely reflective of the primordial tissues, and there is evidence supporting the application of 3D facial analysis to the investigation of variation and disease including studies showing that the face can reflect systemic health, provides diagnostic clues to disorders, and that facial variation reflects biological pathways. In addition, facial variation has been related to evolutionary factors. The purpose of this review is to look backward to suggest that knowledge of human evolution supports, and may instruct, the application and interpretation of studies of facial morphology for documentation of human variation and investigation of its relationships with health and disease. Furthermore, in the context of advances of deep phenotyping and data integration, to look forward to suggest approaches to scalable implementation of facial analysis, and to suggest avenues for future research and clinical application of this technology.

Data management strategies for multinational large-scale systems biology projects

Good accessibility of publicly funded research data is essential to secure an open scientific system and eventually becomes mandatory [Wellcome Trust will Penalise Scientists Who Don’t Embrace Open Access. The Guardian 2012]. By the use of high-throughput methods in many research areas from physics to systems biology, large data collections are increasingly important as raw material for research. Here, we present strategies worked out by international and national institutions targeting open access to publicly funded research data via incentives or obligations to share data. Funding organizations such as the British Wellcome Trust therefore have developed data sharing policies and request commitment to data management and sharing in grant applications. Increased citation rates are a profound argument for sharing publication data. Pre-publication sharing might be rewarded by a data citation credit system via digital object identifiers (DOIs) which have initially been in use for data objects. Besides policies and incentives, good practice in data management is indispensable. However, appropriate systems for data management of large-scale projects for example in systems biology are hard to find. Here, we give an overview of a selection of open-source data management systems proved to be employed successfully in large-scale projects.

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.

Deep phenotyping for precision medicine

In medical contexts, the word "phenotype" is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The emerging field of precision medicine aims to provide the best available care for each patient based on stratification into disease subclasses with a common biological basis of disease. The comprehensive discovery of such subclasses, as well as the translation of this knowledge into clinical care, will depend critically upon computational resources to capture, store, and exchange phenotypic data, and upon sophisticated algorithms to integrate it with genomic variation, omics profiles, and other clinical information. This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation.

Workshop on laboratory protocol standards for the Molecular Methods Database

Management of data to produce scientific knowledge is a key challenge for biological research in the 21st century. Emerging high-throughput technologies allow life science researchers to produce big data at speeds and in amounts that were unthinkable just a few years ago. This places high demands on all aspects of the workflow: from data capture (including the experimental constraints of the experiment), analysis and preservation, to peer-reviewed publication of results. Failure to recognise the issues at each level can lead to serious conflicts and mistakes; research may then be compromised as a result of the publication of non-coherent protocols, or the misinterpretation of published data. In this report, we present the results from a workshop that was organised to create an ontological data-modelling framework for Laboratory Protocol Standards for the Molecular Methods Database (MolMeth). The workshop provided a set of short- and long-term goals for the MolMeth database, the most important being the decision to use the established EXACT description of biomedical ontologies as a starting point.

Knowledge engineering for health: a new discipline required to bridge the "ICT gap" between research and healthcare

Despite vast amount of money and research being channeled toward biomedical research, relatively little impact has been made on routine clinical practice. At the heart of this failure is the information and communication technology "chasm" that exists between research and healthcare. A new focus on "knowledge engineering for health" is needed to facilitate knowledge transmission across the research-healthcare gap. This discipline is required to engineer the bidirectional flow of data: processing research data and knowledge to identify clinically relevant advances and delivering these into healthcare use; conversely, making outcomes from the practice of medicine suitably available for use by the research community. This system will be able to self-optimize in that outcomes for patients treated by decisions that were based on the latest research knowledge will be fed back to the research world. A series of meetings, culminating in the "I-Health 2011" workshop, have brought together interdisciplinary experts to map the challenges and requirements for such a system. Here, we describe the main conclusions from these meetings. An "I4Health" interdisciplinary network of experts now exists to promote the key aims and objectives, namely "integrating and interpreting information for individualized healthcare," by developing the "knowledge engineering for health" domain.