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.

BiobankUniverse: automatic matchmaking between datasets for biobank data discovery and integration.

MOTIVATION: Biobanks are indispensable for large-scale genetic/epidemiological studies, yet it remains difficult for researchers to determine which biobanks contain data matching their research questions. RESULTS: To overcome this, we developed a new matching algorithm that identifies pairs of related data elements between biobanks and research variables with high precision and recall. It integrates lexical comparison, Unified Medical Language System ontology tagging and semantic query expansion. The result is BiobankUniverse, a fast matchmaking service for biobanks and researchers. Biobankers upload their data elements and researchers their desired study variables, BiobankUniverse automatically shortlists matching attributes between them. Users can quickly explore matching potential and search for biobanks/data elements matching their research. They can also curate matches and define personalized data-universes. AVAILABILITY AND IMPLEMENTATION: BiobankUniverse is available at http://biobankuniverse.com or can be downloaded as part of the open source MOLGENIS suite at http://github.com/molgenis/molgenis. CONTACT: m.a.swertz@rug.nl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

WormQTLHD--a web database for linking human disease to natural variation data in C. elegans.

Interactions between proteins are highly conserved across species. As a result, the molecular basis of multiple diseases affecting humans can be studied in model organisms that offer many alternative experimental opportunities. One such organism-Caenorhabditis elegans-has been used to produce much molecular quantitative genetics and systems biology data over the past decade. We present WormQTL(HD) (Human Disease), a database that quantitatively and systematically links expression Quantitative Trait Loci (eQTL) findings in C. elegans to gene-disease associations in man. WormQTL(HD), available online at http://www.wormqtl-hd.org, is a user-friendly set of tools to reveal functionally coherent, evolutionary conserved gene networks. These can be used to predict novel gene-to-gene associations and the functions of genes underlying the disease of interest. We created a new database that links C. elegans eQTL data sets to human diseases (34 337 gene-disease associations from OMIM, DGA, GWAS Central and NHGRI GWAS Catalogue) based on overlapping sets of orthologous genes associated to phenotypes in these two species. We utilized QTL results, high-throughput molecular phenotypes, classical phenotypes and genotype data covering different developmental stages and environments from WormQTL database. All software is available as open source, built on MOLGENIS and xQTL workbench.

Evaluation of CADD Scores in Curated Mismatch Repair Gene Variants Yields a Model for Clinical Validation and Prioritization.

Next-generation sequencing in clinical diagnostics is providing valuable genomic variant data, which can be used to support healthcare decisions. In silico tools to predict pathogenicity are crucial to assess such variants and we have evaluated a new tool, Combined Annotation Dependent Depletion (CADD), and its classification of gene variants in Lynch syndrome by using a set of 2,210 DNA mismatch repair gene variants. These had already been classified by experts from InSiGHT's Variant Interpretation Committee. Overall, we found CADD scores do predict pathogenicity (Spearman's ρ = 0.595, P < 0.001). However, we discovered 31 major discrepancies between the InSiGHT classification and the CADD scores; these were explained in favor of the expert classification using population allele frequencies, cosegregation analyses, disease association studies, or a second-tier test. Of 751 variants that could not be clinically classified by InSiGHT, CADD indicated that 47 variants were worth further study to confirm their putative pathogenicity. We demonstrate CADD is valuable in prioritizing variants in clinically relevant genes for further assessment by expert classification teams.

SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data.

There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi- automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA's applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90 000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re)coding tasks and we believe it will prove useful for many more projects. Database URL: http://molgenis.org/sorta or as an open source download from http://www.molgenis.org/wiki/SORTA.

Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels.

BACKGROUND: RNA-sequencing (RNA-seq) is a powerful technique for the identification of genetic variants that affect gene-expression levels, either through expression quantitative trait locus (eQTL) mapping or through allele-specific expression (ASE) analysis. Given increasing numbers of RNA-seq samples in the public domain, we here studied to what extent eQTLs and ASE effects can be identified when using public RNA-seq data while deriving the genotypes from the RNA-sequencing reads themselves. METHODS: We downloaded the raw reads for all available human RNA-seq datasets. Using these reads we performed gene expression quantification. All samples were jointly normalized and subjected to a strict quality control. We also derived genotypes using the RNA-seq reads and used imputation to infer non-coding variants. This allowed us to perform eQTL mapping and ASE analyses jointly on all samples that passed quality control. Our results were validated using samples for which DNA-seq genotypes were available. RESULTS: 4,978 public human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though these data originated from many different laboratories, samples reflecting the same cell type clustered together, suggesting that technical biases due to different sequencing protocols are limited. In a joint analysis on the 1,262 samples with high quality genotypes, we identified cis-eQTLs effects for 8,034 unique genes (at a false discovery rate ≤0.05). eQTL mapping on individual tissues revealed that a limited number of samples already suffice to identify tissue-specific eQTLs for known disease-associated genetic variants. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels. CONCLUSIONS: By deriving and imputing genotypes from RNA-seq data, it is possible to identify both eQTLs and ASE effects. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become especially relevant for studying the effects of tissue-specific and rare pathogenic genetic variants to aid clinical interpretation of exome and genome sequencing.