MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians

Velde, K. Joeri van der; Imhann, Floris; Charbon, Bart; Pang, Chao; Enckevort, David van; Slofstra, Mariska; Barbieri, Ruggero; Alberts, Rudi; Hendriksen, Dennis; Kelpin, Fleur D.L.; Haan, Mark de; Boer, Tommy de; Haakma, Sido; Stroomberg, Connor; Scholtens, Salome; Geijn, Gert-Jan van de; Festen, Eleonora A. M.; Weersma, Rinse K.; Swertz, Morris A.

doi:10.1093/bioinformatics/bty742

Cited by 86 publications

(70 citation statements)

References 19 publications

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“…In order to ensure findability of biobanking resources, BBMRI-ERIC operates two main services: (1) the BBMRI-ERIC Directory, 4 which works on aggregate metadata descriptors of biobanks and their collections, and (2) the BBMRI-ERIC Locator, 5 which works as a federated search tool allowing to query individual-level data at the sites of origin while returning aggregated results.…”

Section: Covid-19 Resources In the Bbmri-eric Networkmentioning

confidence: 99%

“…7 The Directory data model is being continuously optimized based on the feedback from and needs of the research and biobanking communities. The model is also being deployed in national nodes to collect the relevant information, using the underlying MOLGENIS platform [4] to quickly configure locally needed extensions. In order to maximize findability, the Directory also serves as a data source for aggregation services such as FAIRSharing.org and Google dataset search.…”

Section: Covid-19 Resources In the Bbmri-eric Networkmentioning

confidence: 99%

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BBMRI-ERIC’s contributions to research and knowledge exchange on COVID-19

et al. 2020

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During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.

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Section: Covid-19 Resources In the Bbmri-eric Networkmentioning

confidence: 99%

Section: Covid-19 Resources In the Bbmri-eric Networkmentioning

confidence: 99%

BBMRI-ERIC’s contributions to research and knowledge exchange on COVID-19

et al. 2020

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“…We further checked and removed samples that did not show consistent sex information compared to genotypes on the X chromosome, between reported familial information and observed identity-by-descent sharing with family members, and between genotypes available from previous studies (Tigchelaar et al 2015;Francioli et al 2015). A detailed description of the process can be found at the following link: https://covid19research.nl (van der Velde et al 2019). After quality checks, a total of 36,339 samples and 571,420 autosomal and X-chromosome markers were available for analysis.…”

Section: Genotyping Datamentioning

confidence: 99%

Lack of association between genetic variants at ACE2 and TMPRSS2 genes involved in SARS-CoV-2 infection and human quantitative phenotypes

Lopera

Graaf

Lanting

et al. 2020

Preprint

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Coronavirus disease 2019 (COVID-19) shows a wide variation in expression and severity of symptoms, from very mild or no symptomes, to flu-like symptoms, and in more severe cases, to pneumonia, acute respiratory distress syndrome and even death. Large differences in outcome have also been observed between males and females. The causes for this variability are likely to be multifactorial, and to include genetics. The SARS-CoV-2 virus responsible for the infection uses the human receptor angiotensin converting enzyme 2 (ACE2) for cell invasion, and the serine protease TMPRSS2 for S protein priming. Genetic variation in these two genes may thus modulate an individual's genetic predisposition to infection and virus clearance. While genetic data on COVID-19 patients is being gathered, we carried out a phenome-wide association scan (PheWAS) to investigate the role of these genes in other human phenotypes in the general population. We examined 178 quantitative phenotypes including cytokines and cardio-metabolic biomarkers, as well as 58 medications in 36,339 volunteers from the Lifelines population biobank, in relation to 1,273 genetic variants located in or near ACE2 and TMPRSS2. While none reached our threshold for significance, we observed a suggestive association of polymorphisms within the ACE2 gene with (1) the use of angiotensin II receptor blockers (ARBs) combination therapies (p=5.7x10 -4 ), an association that is significantly stronger in females (p diff =0.01), and (2) with the use of non-steroid antiinflammatory and antirheumatic products (p=5.5x10 -4 ). While these associations need to be confirmed in larger sample sizes, they suggest that these variants play a role in diseases such as hypertension and chronic inflammation that are often observed in the more severe COVID-19 cases.

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“…MOLGENIS Research is an open‐source web application to collect, manage, analyze, visualize, and share large and complex biomedical datasets, without the need for advanced bioinformatics skills .…”

Section: Miscellaneous Tools Of Interestmentioning

confidence: 99%

Tools and resources for metabolomics research community: A 2017–2018 update

Misra¹,

Mohapatra

2018

Electrophoresis

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The scale at which MS‐ and NMR‐based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences—ranging from biomedical to agricultural—is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017–2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

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MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians

Cited by 86 publications

References 19 publications

BBMRI-ERIC’s contributions to research and knowledge exchange on COVID-19

BBMRI-ERIC’s contributions to research and knowledge exchange on COVID-19

Lack of association between genetic variants at ACE2 and TMPRSS2 genes involved in SARS-CoV-2 infection and human quantitative phenotypes

Tools and resources for metabolomics research community: A 2017–2018 update

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