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Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

Abstract Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.

AIMS:Likely pathogenic/pathogenic variants in genes encoding desmosomal proteins play an important role in the pathophysiology of arrhythmogenic right ventricular cardiomyopathy (ARVC). However, for a substantial proportion of ARVC patients, the genetic substrate remains unknown. We hypothesized that plectin, a cytolinker protein encoded by the PLEC gene, could play a role in ARVC because it has been proposed to link the desmosomal protein desmoplakin to the cytoskeleton and therefore has a potential function in the desmosomal structure. METHODS:We screened PLEC in 359 ARVC patients and compared the frequency of rare coding PLEC variants (minor allele frequency [MAF]

In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the "ideal" genotype and identify "best-matched" labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a "data cleaning" step before standard data analysis.

Abstract Introduction Rare disease patient data are typically sensitive, present in multiple registries controlled by different custodians, and non-interoperable. Making these data Findable, Accessible, Interoperable, and Reusable (FAIR) for humans and machines at source enables federated discovery and analysis across data custodians. This facilitates accurate diagnosis, optimal clinical management, and personalised treatments. In Europe, twenty-four European Reference Networks (ERNs) work on rare disease registries in different clinical domains. The process and the implementation choices for making data FAIR (‘FAIRification’) differ among ERN registries. For example, registries use different software systems and are subject to different legal regulations. To support the ERNs in making informed decisions and to harmonise FAIRification, the FAIRification steward team was established to work as liaisons between ERNs and researchers from the European Joint Programme on Rare Diseases. Results The FAIRification steward team inventoried the FAIRification challenges of the ERN registries and proposed solutions collectively with involved stakeholders to address them. Ninety-eight FAIRification challenges from 24 ERNs’ registries were collected and categorised into “training” (31), “community” (9), “modelling” (12), “implementation” (26), and “legal” (20). After curating and aggregating highly similar challenges, 41 unique FAIRification challenges remained. The two categories with the most challenges were “training” (15) and “implementation” (9), followed by “community” (7), and then “modelling” (5) and “legal” (5). To address all challenges, eleven types of solutions were proposed. Among them, the provision of guidelines and the organisation of training activities resolved the “training” challenges, which ranged from less-technical “coffee-rounds” to technical workshops, from informal FAIR Games to formal hackathons. Obtaining implementation support from technical experts was the solution type for tackling the “implementation” challenges. Conclusion This work shows that a dedicated team of FAIR data stewards is an asset for harmonising the various processes of making data FAIR in a large organisation with multiple stakeholders. Additionally, multi-levelled training activities are required to accommodate the diverse needs of the ERNs. Finally, the lessons learned from the experience of the FAIRification steward team described in this paper may help to increase FAIR awareness and provide insights into FAIRification challenges and solutions of rare disease registries.

Abstract The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .

Abstract Background The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Diseases (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles. Results Through a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR. Conclusions Within the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.

Objective: This paper reports on the development of a dynamic data management planning questionnaire to guide data stewards of the European Reference Network (ERN) rare disease patient registries to make their data findable, accessible, interoperable, and reusable (FAIR). As part of this work, the questionnaire was validated through expert review and aligned with existing resources on rare diseases and FAIR data management. Materials and Methods: The questionnaire was developed for the Data Stewardship Wizard, a tool for data management planning. Knowledge sources on FAIR data, ERN patient registries, and data management were used to compose questions. Ten domain experts validated the questionnaire. The topics in the questionnaire were aligned with existing knowledge bases. Results: A total of 57 questions were included in the questionnaire. Twenty-three references to the FAIR Cookbook and Research Data Management toolkit for Life Sciences were added. Expert validation provided a total of 166 comments on content, structure, and software-related issues. A public instance of the Data Stewardship Wizard was deployed for use by data stewards of ERN patient registries. Discussion: The questionnaire addresses issues that ERNs encounter when making their registries FAIR and follows the implementation choices made by the European rare disease community. A challenging task for future research is to extend the questionnaire to other types of registries and to validate with users. Conclusion: This smart questionnaire is the first model created for the Data Stewardship Wizard that helps ERN patient registries with making their data FAIR. It will assist data stewards in aligning their efforts and providing guidance on FAIR data.

Abstract Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice .

Background: In the molecular genetic diagnostics of Mendelian disorders, solutions are needed for the major challenge of dealing with the large number of variants of uncertain significance (VUSs) identified using next-generation sequencing (NGS). Recently, promising approaches using constraint metrics to calculate case excess scores (CE), etiological fractions (EF), and gnomAD-derived constraint scores have been reported that estimate the likelihood of rare variants in specific genes or regions that are pathogenic. Our objective is to study the usability of these constraint data into variant interpretation in a diagnostic setting, using our cardiomyopathy cohort.Methods and Results: Patients (N = 2002) referred for clinical genetic diagnostics underwent NGS testing of 55–61 genes associated with cardiomyopathies. Previously classified likely pathogenic (LP) and pathogenic (P) variants were used to validate the use of data from CE, EF, and gnomAD constraint analyses for (re)classification of associated variant types in specific cardiomyopathy subtype-related genes. The classifications corroborated in 94% (354/378) of cases. Next, we reclassified 23 unique VUSs to LP, increasing the diagnostic yield by 1.2%. In addition, 106 unique VUSs (5.3% of patients) were prioritized for co-segregation or functional analyses.Conclusions: Our analysis confirms that the use of constraint metrics data can improve variant interpretation, and we, therefore, recommend using constraint scores on other cohorts and disorders and its inclusion in variant interpretation protocols.

A genetic diagnosis remains unattainable for many individuals with a rare disease because of incomplete knowledge about the genetic basis of many diseases. Here, the authors present the web-based tool GADO (GeneNetwork Assisted Diagnostic Optimization) that uses public RNA-seq data for prioritization of candidate genes.

Background: Genetic disorders are a substantial cause of infant morbidity and mortality and are frequently suspected in neonatal intensive care units. Non-specific clinical presentation or limitations to physical examination can result in a plethora of genetic testing techniques, without clear strategies on test ordering. Here, we review our 2-years experiences of rapid genetic testing of NICU patients in order to provide such recommendations.Methods: We retrospectively included all patients admitted to the NICU who received clinical genetic consultation and genetic testing in our University hospital. We documented reasons for referral for genetic consultation, presenting phenotypes, differential diagnoses, genetic testing requested and their outcomes, as well as the consequences of each (rapid) genetic diagnostic approach. We calculated diagnostic yield and turnaround times (TATs).Results: Of 171 included infants that received genetic consultation 140 underwent genetic testing. As a result of testing as first tier, 13/14 patients received a genetic diagnosis from QF-PCR; 14/115 from SNP-array; 12/89 from NGS testing, of whom 4/46 were diagnosed with a small gene panel and 8/43 with a large OMIM-morbid based gene panel. Subsequent secondary or tertiary analysis and/or additional testing resulted in five more diagnoses. TATs ranged from 1 day (QF-PCR) to a median of 14 for NGS and SNP-array testing, with increasing TAT in particular when many consecutive tests were performed. Incidental findings were detected in 5/140 tested patients (3.6%).Conclusion: We recommend implementing a broad NGS gene panel in combination with CNV calling as the first tier of genetic testing for NICU patients given the often unspecific phenotypes of ill infants and the high yield of this large panel.