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 data sets, 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

Alberts, Rudi

Barbieri, Ruggero

Charbon, Bart

de Boer, Tommy

de Haan, Mark

Festen, Eleonora A M

Haakma, Sido

Hendriksen, Dennis

Imhann, Floris

Kelpin, Fleur

Pang, Chao

Scholtens, Salome

Slofstra, Mariska

Stroomberg, Connor

Swertz, Morris A

van de Geijn, Gert-Jan

van der Velde, K Joeri

van Enckevort, David

Weersma, Rinse K

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 data sets, 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.</p

Proceedings - University of Groningen

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MOLGENIS Research:Advanced bioinformatics data software for non-bioinformaticians

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University of Groningen

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MOLGENIS Research: Advanced bioinformatics data software for non-bioinformaticians

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 University of Groningen
MOLGENIS Research
van der Velde, K Joeri; Imhann, Floris; Charbon, Bart; Pang, Chao; van Enckevort, David;
Slofstra, Mariska; Barbieri, Ruggero; Alberts, Rudi; Hendriksen, Dennis; Kelpin, Fleur
Published in:
Bioinformatics (Oxford, England)
DOI:
10.1093/bioinformatics/bty742
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from
it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date:
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Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
van der Velde, K. J., Imhann, F., Charbon, B., Pang, C., van Enckevort, D., Slofstra, M., ... Swertz, M. A.
(2019). MOLGENIS Research: Advanced bioinformatics data software for non-bioinformaticians.
Bioinformatics (Oxford, England), 35(6), 1076-1078. https://doi.org/10.1093/bioinformatics/bty742
Copyright
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Download date: 13-07-2019
Bioinformatics, YYYY, 0–0 
doi: 10.1093/bioinformatics/xxxxx 
Advance Access Publication Date: DD Month YYYY 
Databases and ontologies 
 
Applications note 
MOLGENIS Research: Advanced bioinformat-
ics data software for non-bioinformaticians 
K. Joeri van der Velde1,2, Floris Imhann2,3, Bart Charbon1, Chao Pang1, Da-
vid van Enckevort1, Mariska Slofstra1, Ruggero Barbieri2,3, Rudi Alberts2,3, 
Dennis Hendriksen1, Fleur Kelpin1, Mark de Haan1, Tommy de Boer1, Sido 
Haakma1, Connor Stroomberg1, Salome Scholtens1, Gert-Jan van de Geijn1, 
Eleonora A. M. Festen2,3, Rinse K. Weersma3 and Morris A. Swertz1,2,* 
1University of Groningen and University Medical Center Groningen, Genomics Coordination Center, 
Groningen, The Netherlands, 2University of Groningen and University Medical Center Groningen, 
Department of Genetics, Groningen, The Netherlands, 3University of Groningen and University Med-
ical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, The Nether-
lands. 
*To whom correspondence should be addressed. 
Associate Editor: XXXXXXX 
Received on XXXXX; revised on XXXXX; accepted on XXXXX  
Abstract 
Motivation: The volume and complexity of biological data increases rapidly. Many clinical profes-
sionals 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, man-
age, analyze, visualize and share large and complex biomedical data sets, without the need for ad-
vanced 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.	
Contact:	m.a.swertz@rug.nl  
 
 
1 Introduction  
In order to improve human health, biomedical scientists are increasingly 
using large and complex data sets to discover biological mechanisms. 
Large numbers of patients and control participants are screened with 
questionnaires, biomedical measurements, high-throughput techniques 
such as next-generation sequencing of the genome, the transcriptome and 
the microbiome (Ginsburg, 2014), resulting in large quantities of pheno-
typic and molecular data (Bowdin et al., 2014). However, many clinical 
professionals and biomedical researchers do not always have the proper 
tools to process, manage, analyze, visualize and publicly share these data 
(Jagadish, 2015) while complying to 'FAIR' (Findable, Accessible, In-
teroperable, and Reusable) (Wilkinson et al., 2016) and 'ELSI' (Ethical, 
Legal and Social Implications) principles. 
Several challenges arise when developing software for big da-
ta used by biomedical researchers (Raghupathi and Raghupathi, 2014). 
The first challenge is data capture and data management. Data systems 
need to be adaptable enough to not only handle today’s data, but also be 
able seamlessly capture tomorrow’s data formats (M A Swertz et al., 
2010). Current systems are often too strict in terms of importing new 
data types. As a consequence, systems must sometimes even be taken 
offline for database redesign (Morris A Swertz et al., 2010; Adamusiak 
et al., 2012). Therefore, a good system needs to allow continuous use 
while databases can be redesigned and unforeseen data types can be 
© The Author(s) 2018. Published by Oxford University Press. 
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the 
original work is properly cited. 
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used. The second challenge is to integrate and analyze the data. Biologi-
cal data is complex and heterogeneous by nature, leading to incompatible 
data, disorganized systems, and missed opportunities (Jagadish, 2015; 
Auffray et al., 2016). Data integration solutions are needed to understand 
the interaction of environmental effects with molecular measurements 
resulting in certain phenotypes (Stieb et al., 2017), by combining multi-
ple -omics layers (Suravajhala et al., 2016) with clinical data (Higdon et 
al., 2015). The third and most difficult challenge is to create user inter-
faces that are easy to understand and interpret the data, but elaborate 
enough to allow for comprehensive queries, analyses and visualizations 
needed for biomedical 'big data' research. 
Here, we present MOLGENIS Research, designed to over-
come the aforementioned challenges and follow the natural flow of bio-
medical research: collect, manage, analyze, visualize and share data. 
2 Features 
MOLGENIS Research is a life science data solution built on top of the 
MOLGENIS platform. The MOLGENIS platform allows the develop-
ment of various apps for specific tasks and to upload data models and 
settings to tailor the platform to a specific use. Below, we present a col-
lection of apps and settings that together form the MOLGENIS solution 
for Research. These apps are grouped into the five categories that repre-
sent the typical flow of research data: (i) Collect: gathering or entering of 
data into the database; (ii) Manage: inspecting and handling of data 
inside the database; (iii) Analyze: detect patterns and differences in the 
data using algorithms and statistical tests; (iv) Visualize: creating graphs 
and other visualizations; and (v) Share: making data, visualizations, and 
results available to others. 
3.1 Collect 
MOLGENIS Research offers several ways to enter or upload data into 
the system. The typical way to add data is to use either the Single-click 
Importer app or the more advanced Step-by-step Importer app. Both 
importer apps accept files in the EMX (Entity Model Extensible) format, 
and they are well-documented at https://molgenis.gitbooks.io. EMX is a 
flexible spreadsheet format for tabular data. It allows data modeling at 
runtime with definitions for each column in a table, meaning the data in 
the columns are not predefined or locked in place, yet data consistency is 
checked and preserved. EMX-formats with XLSX-, ZIP-, and TSV-
extensions can be uploaded. A more specialized importer accepts VCF 
and VCF.GZ files for the quick import of genomic data, as well as OWL 
and OBO formats for importing ontological data. Additional community 
standard formats can become supported in the future depending on user 
needs. The Remote File Ingest app can access remote servers and secure-
ly import data directly over the web. Via the Questionnaire app, data can 
be collected instantly from study participants and imported in the data-
base. Answers filled in by participants are stored directly in the 
MOLGENIS Research database. Finally, manual data entry can be per-
formed in the Data Explorer app by adding rows or columns in database 
tables. 
3.2 Manage 
After data collection, MOLGENIS Research has apps to inspect, organ-
ize, permit and customize the data. The primary data management app is 
the Data Explorer which acts as a table viewer. Here, columns can be 
selected and sorted, and data rows are visible. In addition, data sets can 
be placed in a hierarchical folder structure via the ‘Package’ system 
table. See Figure 1 for an impression of the Data Explorer. Using the 
Navigator app, data sets can be browsed and viewed in their folder struc-
ture. Finally, the Metadata manager enables super users to modify the 
underlying data structure itself to keep up with advancing insights and 
address unforeseen requirements. 
 
Fig. 1. Screenshot of the MOLGENIS Research graphical user interface. Shown here 
is the Data Explorer app, a central place in MOLGENIS Research to enter, enrich, filter, 
analyse and export data sets. 
3.3 Analyze 
MOLGENIS Research enables bioionformaticians to add data analysis 
tools. For example, the Data Explorer is often used to analyze data. 
Here, the Filter Wizard can be used to run queries. In a recent project, 
the Descriptive Statistics app was added to automatically creates all 
descriptive statistics often needed to present in the first table of a manu-
script. The Descriptive Statistics app automatically recognizes whether 
data is continuous, binary or categorical, whether it is normally distribut-
ed or not and whether there are too many missing values. Based on the 
outcome it provides, means, medians, counts and percentages.  
Use of R, Python and REST APIs allows adding additional data 
analysis and connections to other data systems. There is a Scripts app in 
which Javascript, R and Python scripts can be stored and run by others. 
These scripts can be written by bioinformaticians, but can be easily run 
or repeated by researchers without data analysis skills. Using these, 
specialized tools can be built, (e.g. the GAVIN method (van der Velde et 
al., 2017)) to automatically classify pathogenicity of genomic variants. 
Several examples of such add-on analysis tools are showcased in the 
demo. 
3.4 Visualize 
When genome data is opened, the Genome Browser app automatically 
visualizes genomic loci using the interactive Dalliance genome browser 
(Down et al., 2011). More scripts are available on the MOLGENIS web-
site and Github repository to generate value distribution plots and con-
sensus in multiple categorical values. Custom reports and visualizations 
can be added via a templating system (Freemarker) that loads a single 
row or whole data set for user-specified formatting rules, and that uses 
aforementioned scripting capabilities (see Analysis).  
3.5 Share 
To support collaborations, MOLGENIS Research has different ways to 
share and connect the data and to achieve a number of FAIR metrics 
(Wilkinson, Sansone, et al., 2017) such as ensuring identifier uniqueness 
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and persistance, indexing its data tables, offering HTTP access and au-
thorization, and tools to connect data to FAIR vocabularies such as on-
tologies (Pang et al., 2014). For joint analysis of data sets, we have de-
veloped Mapping Service tools to make both columns (Pang et al., 2014) 
and values (Pang et al., 2015) interoperable between data sets so they 
can be merged. The Tag Wizard app can assign meaning to data columns 
using ontologies, which can be integrated across different data sets using 
the Mapping Service app. Data sets and variables can be made findable 
without exposing (sensitive) data values by creating a catalogue from a 
combination of raw data, curated data or interesting results collected in 
the system. Others can browse this catalogue before contacting or sub-
mitting a request for access. MOLGENIS Research supports the com-
plete data access and request workflow designed by the data owner. 
Super users can also create FAIR endpoints (Wilkinson, Verborgh, et al., 
2017) based on definitions of Metadata, Catalog, Dataset, Distribution 
and Response, which ensures your data is machine-findable and thereby 
has increased findability. 
3 Implementation 
MOLGENIS Research is implemented using open and freely usable 
industry standards. It is available under the GNU Lesser General Public 
License v3.0 (https://www.gnu.org/licenses/lgpl-3.0.en.html). It is writ-
ten in Java 1.8 (https://java.com), supported by the Spring MVC frame-
work (https://spring.io). It uses Apache Maven 
(https://maven.apache.org) to manage dependencies, and runs on an 
Apache Tomcat (http://tomcat.apache.org) webserver. Data is stored in a 
PostgreSQL database (https://www.postgresql.org) and indexed by Elas-
ticSearch (https://www.elastic.co) for high performance and horizontal 
scaling ability by data replication and sharding, respectively. Final stor-
age and query performance depends on specific hardware and software 
configuration. Its graphical user interface is composed of Bootstrap 
(https://getbootstrap.com), Vue (https://vuejs.org) and Freemarker tem-
plates (https://freemarker.apache.org). FAIR endpoints are implemented 
in W3C RDF 1.1 Turtle (https://www.w3.org/TR/turtle). 
4 Conclusion 
We have built MOLGENIS Research, a web application for the biomedi-
cal field to work with multi-omics data sets without being dependent on 
bioinformaticians. MOLGENIS Research enables researchers to more 
efficiently collect, manage, analyze, visualize and share data, as well as 
offering support to make data FAIR in a flexible and safe way. 
MOLGENIS Research offers all the advantages of a true database system 
with detailed data management and access control options, while at the 
same time being able to grow ‘organically’ by allowing data to be dy-
namically shaped based on what is needed in practice, and adding cus-
tom extensions such as visualizations and algorithms into a running 
system without downtime. It can be used as a project database from day 
one as there is no need to design a data model upfront. 
Currently, MOLGENIS Research has been adopted by several 
research projects, including 1000IBD, 500FG and LifeLines. The 
1000IBD database (http://1000ibd.org) contains a range of clinical and 
research phenotypes for up to 2,000 patients per -omics type, which 
includes quantifications of 12,000+ microbiome OTUs, 400+ immu-
nochip markers, and ~300 RNA-seq experiments. The 500FG database 
(https://hfgp.bbmri.nl) contains microbiome, metabolomics, cytokine, 
QTL, cell staining, serum Ig and flow cytometry data for around 500 
individuals. Identifier codes for individuals serve as foreign keys that can 
link data tables together for data integration and analysis. Lastly, the 
LifeLines data catalogue (https://catalogue.lifelines.nl) contains the 
metadata for around 40,000 data items available for researchers such as 
questionnaires, measurements and (blood and urine) sample analyses 
from a longitudinal study of 167,000 individuals. We expect more pro-
jects to follow soon, and gladly invite everyone to help us in expanding 
and evolving the MOLGENIS Research solution to serve all popular 
research needs. We strongly encourage interested users to try the demo, 
download and install MOLGENIS Research at 
http://molgenis.org/research. 
Acknowledgements 
We thank Benjamin Kant for feedback and comments. 
Funding We	 thank	 BBMRI-NL	 for	 sponsoring	 the	 development	 of	 the	 software	 de-scribed	 in	 this	manuscript	via	a	voucher.	BBMRI-NL	 is	a	 research	 infrastruc-ture	financed	by	the	Netherlands	Organization	for	Scientific	Research	(NWO),	grant	 number	 184.033.111.	 We	 also	 thank	 NWO	 VIDI	 grant	 number	917.164.455.		
Conflict	of	Interest:	none	declared. 
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   University of GroningenMOLGENIS Researchvan der Velde, K Joeri; Imhann, Floris; Charbon, Bart; Pang, Chao; van Enckevort, David;Slofstra, Mariska; Barbieri, Ruggero; Alberts, Rudi; Hendriksen, Dennis; Kelpin, FleurPublished in:Bioinformatics (Oxford, England)DOI:10.1093/bioinformatics/bty742IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2019Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van der Velde, K. J., Imhann, F., Charbon, B., Pang, C., van Enckevort, D., Slofstra, M., ... Swertz, M. A.(2019). MOLGENIS Research: Advanced bioinformatics data software for non-bioinformaticians.Bioinformatics (Oxford, England), 35(6), 1076-1078. https://doi.org/10.1093/bioinformatics/bty742CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 13-11-2019Bioinformatics, YYYY, 0–0 doi: 10.1093/bioinformatics/xxxxx Advance Access Publication Date: DD Month YYYY Databases and ontologies  Applications note MOLGENIS Research: Advanced bioinformat-ics data software for non-bioinformaticians K. Joeri van der Velde1,2, Floris Imhann2,3, Bart Charbon1, Chao Pang1, Da-vid van Enckevort1, Mariska Slofstra1, Ruggero Barbieri2,3, Rudi Alberts2,3, Dennis Hendriksen1, Fleur Kelpin1, Mark de Haan1, Tommy de Boer1, Sido Haakma1, Connor Stroomberg1, Salome Scholtens1, Gert-Jan van de Geijn1, Eleonora A. M. Festen2,3, Rinse K. Weersma3 and Morris A. Swertz1,2,* 1University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands, 2University of Groningen and University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands, 3University of Groningen and University Med-ical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, The Nether-lands. *To whom correspondence should be addressed. Associate Editor: XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX  Abstract Motivation: The volume and complexity of biological data increases rapidly. Many clinical profes-sionals 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, man-age, analyze, visualize and share large and complex biomedical data sets, without the need for ad-vanced 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.	Contact:	m.a.swertz@rug.nl    1 Introduction  In order to improve human health, biomedical scientists are increasingly using large and complex data sets to discover biological mechanisms. Large numbers of patients and control participants are screened with questionnaires, biomedical measurements, high-throughput techniques such as next-generation sequencing of the genome, the transcriptome and the microbiome (Ginsburg, 2014), resulting in large quantities of pheno-typic and molecular data (Bowdin et al., 2014). However, many clinical professionals and biomedical researchers do not always have the proper tools to process, manage, analyze, visualize and publicly share these data (Jagadish, 2015) while complying to 'FAIR' (Findable, Accessible, In-teroperable, and Reusable) (Wilkinson et al., 2016) and 'ELSI' (Ethical, Legal and Social Implications) principles. Several challenges arise when developing software for big da-ta used by biomedical researchers (Raghupathi and Raghupathi, 2014). The first challenge is data capture and data management. Data systems need to be adaptable enough to not only handle today’s data, but also be able seamlessly capture tomorrow’s data formats (M A Swertz et al., 2010). Current systems are often too strict in terms of importing new data types. As a consequence, systems must sometimes even be taken offline for database redesign (Morris A Swertz et al., 2010; Adamusiak et al., 2012). Therefore, a good system needs to allow continuous use while databases can be redesigned and unforeseen data types can be © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty742/5085379 by University of Groningen user on 05 September 2018used. The second challenge is to integrate and analyze the data. Biologi-cal data is complex and heterogeneous by nature, leading to incompatible data, disorganized systems, and missed opportunities (Jagadish, 2015; Auffray et al., 2016). Data integration solutions are needed to understand the interaction of environmental effects with molecular measurements resulting in certain phenotypes (Stieb et al., 2017), by combining multi-ple -omics layers (Suravajhala et al., 2016) with clinical data (Higdon et al., 2015). The third and most difficult challenge is to create user inter-faces that are easy to understand and interpret the data, but elaborate enough to allow for comprehensive queries, analyses and visualizations needed for biomedical 'big data' research. Here, we present MOLGENIS Research, designed to over-come the aforementioned challenges and follow the natural flow of bio-medical research: collect, manage, analyze, visualize and share data. 2 Features MOLGENIS Research is a life science data solution built on top of the MOLGENIS platform. The MOLGENIS platform allows the develop-ment of various apps for specific tasks and to upload data models and settings to tailor the platform to a specific use. Below, we present a col-lection of apps and settings that together form the MOLGENIS solution for Research. These apps are grouped into the five categories that repre-sent the typical flow of research data: (i) Collect: gathering or entering of data into the database; (ii) Manage: inspecting and handling of data inside the database; (iii) Analyze: detect patterns and differences in the data using algorithms and statistical tests; (iv) Visualize: creating graphs and other visualizations; and (v) Share: making data, visualizations, and results available to others. 3.1 Collect MOLGENIS Research offers several ways to enter or upload data into the system. The typical way to add data is to use either the Single-click Importer app or the more advanced Step-by-step Importer app. Both importer apps accept files in the EMX (Entity Model Extensible) format, and they are well-documented at https://molgenis.gitbooks.io. EMX is a flexible spreadsheet format for tabular data. It allows data modeling at runtime with definitions for each column in a table, meaning the data in the columns are not predefined or locked in place, yet data consistency is checked and preserved. EMX-formats with XLSX-, ZIP-, and TSV-extensions can be uploaded. A more specialized importer accepts VCF and VCF.GZ files for the quick import of genomic data, as well as OWL and OBO formats for importing ontological data. Additional community standard formats can become supported in the future depending on user needs. The Remote File Ingest app can access remote servers and secure-ly import data directly over the web. Via the Questionnaire app, data can be collected instantly from study participants and imported in the data-base. Answers filled in by participants are stored directly in the MOLGENIS Research database. Finally, manual data entry can be per-formed in the Data Explorer app by adding rows or columns in database tables. 3.2 Manage After data collection, MOLGENIS Research has apps to inspect, organ-ize, permit and customize the data. The primary data management app is the Data Explorer which acts as a table viewer. Here, columns can be selected and sorted, and data rows are visible. In addition, data sets can be placed in a hierarchical folder structure via the ‘Package’ system table. See Figure 1 for an impression of the Data Explorer. Using the Navigator app, data sets can be browsed and viewed in their folder struc-ture. Finally, the Metadata manager enables super users to modify the underlying data structure itself to keep up with advancing insights and address unforeseen requirements.  Fig. 1. Screenshot of the MOLGENIS Research graphical user interface. Shown here is the Data Explorer app, a central place in MOLGENIS Research to enter, enrich, filter, analyse and export data sets. 3.3 Analyze MOLGENIS Research enables bioionformaticians to add data analysis tools. For example, the Data Explorer is often used to analyze data. Here, the Filter Wizard can be used to run queries. In a recent project, the Descriptive Statistics app was added to automatically creates all descriptive statistics often needed to present in the first table of a manu-script. The Descriptive Statistics app automatically recognizes whether data is continuous, binary or categorical, whether it is normally distribut-ed or not and whether there are too many missing values. Based on the outcome it provides, means, medians, counts and percentages.  Use of R, Python and REST APIs allows adding additional data analysis and connections to other data systems. There is a Scripts app in which Javascript, R and Python scripts can be stored and run by others. These scripts can be written by bioinformaticians, but can be easily run or repeated by researchers without data analysis skills. Using these, specialized tools can be built, (e.g. the GAVIN method (van der Velde et al., 2017)) to automatically classify pathogenicity of genomic variants. Several examples of such add-on analysis tools are showcased in the demo. 3.4 Visualize When genome data is opened, the Genome Browser app automatically visualizes genomic loci using the interactive Dalliance genome browser (Down et al., 2011). More scripts are available on the MOLGENIS web-site and Github repository to generate value distribution plots and con-sensus in multiple categorical values. Custom reports and visualizations can be added via a templating system (Freemarker) that loads a single row or whole data set for user-specified formatting rules, and that uses aforementioned scripting capabilities (see Analysis).  3.5 Share To support collaborations, MOLGENIS Research has different ways to share and connect the data and to achieve a number of FAIR metrics (Wilkinson, Sansone, et al., 2017) such as ensuring identifier uniqueness Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty742/5085379 by University of Groningen user on 05 September 2018and persistance, indexing its data tables, offering HTTP access and au-thorization, and tools to connect data to FAIR vocabularies such as on-tologies (Pang et al., 2014). For joint analysis of data sets, we have de-veloped Mapping Service tools to make both columns (Pang et al., 2014) and values (Pang et al., 2015) interoperable between data sets so they can be merged. The Tag Wizard app can assign meaning to data columns using ontologies, which can be integrated across different data sets using the Mapping Service app. Data sets and variables can be made findable without exposing (sensitive) data values by creating a catalogue from a combination of raw data, curated data or interesting results collected in the system. Others can browse this catalogue before contacting or sub-mitting a request for access. MOLGENIS Research supports the com-plete data access and request workflow designed by the data owner. Super users can also create FAIR endpoints (Wilkinson, Verborgh, et al., 2017) based on definitions of Metadata, Catalog, Dataset, Distribution and Response, which ensures your data is machine-findable and thereby has increased findability. 3 Implementation MOLGENIS Research is implemented using open and freely usable industry standards. It is available under the GNU Lesser General Public License v3.0 (https://www.gnu.org/licenses/lgpl-3.0.en.html). It is writ-ten in Java 1.8 (https://java.com), supported by the Spring MVC frame-work (https://spring.io). It uses Apache Maven (https://maven.apache.org) to manage dependencies, and runs on an Apache Tomcat (http://tomcat.apache.org) webserver. Data is stored in a PostgreSQL database (https://www.postgresql.org) and indexed by Elas-ticSearch (https://www.elastic.co) for high performance and horizontal scaling ability by data replication and sharding, respectively. Final stor-age and query performance depends on specific hardware and software configuration. Its graphical user interface is composed of Bootstrap (https://getbootstrap.com), Vue (https://vuejs.org) and Freemarker tem-plates (https://freemarker.apache.org). FAIR endpoints are implemented in W3C RDF 1.1 Turtle (https://www.w3.org/TR/turtle). 4 Conclusion We have built MOLGENIS Research, a web application for the biomedi-cal field to work with multi-omics data sets without being dependent on bioinformaticians. MOLGENIS Research enables researchers to more efficiently collect, manage, analyze, visualize and share data, as well as offering support to make data FAIR in a flexible and safe way. MOLGENIS Research offers all the advantages of a true database system with detailed data management and access control options, while at the same time being able to grow ‘organically’ by allowing data to be dy-namically shaped based on what is needed in practice, and adding cus-tom extensions such as visualizations and algorithms into a running system without downtime. It can be used as a project database from day one as there is no need to design a data model upfront. Currently, MOLGENIS Research has been adopted by several research projects, including 1000IBD, 500FG and LifeLines. The 1000IBD database (http://1000ibd.org) contains a range of clinical and research phenotypes for up to 2,000 patients per -omics type, which includes quantifications of 12,000+ microbiome OTUs, 400+ immu-nochip markers, and ~300 RNA-seq experiments. The 500FG database (https://hfgp.bbmri.nl) contains microbiome, metabolomics, cytokine, QTL, cell staining, serum Ig and flow cytometry data for around 500 individuals. Identifier codes for individuals serve as foreign keys that can link data tables together for data integration and analysis. Lastly, the LifeLines data catalogue (https://catalogue.lifelines.nl) contains the metadata for around 40,000 data items available for researchers such as questionnaires, measurements and (blood and urine) sample analyses from a longitudinal study of 167,000 individuals. We expect more pro-jects to follow soon, and gladly invite everyone to help us in expanding and evolving the MOLGENIS Research solution to serve all popular research needs. We strongly encourage interested users to try the demo, download and install MOLGENIS Research at http://molgenis.org/research. Acknowledgements We thank Benjamin Kant for feedback and comments. Funding We	 thank	 BBMRI-NL	 for	 sponsoring	 the	 development	 of	 the	 software	 de-scribed	 in	 this	manuscript	via	a	voucher.	BBMRI-NL	 is	a	 research	 infrastruc-ture	financed	by	the	Netherlands	Organization	for	Scientific	Research	(NWO),	grant	 number	 184.033.111.	 We	 also	 thank	 NWO	 VIDI	 grant	 number	917.164.455.		Conflict	of	Interest:	none	declared. References Adamusiak,T. et al. (2012) Observ-OM and Observ-TAB: Universal syntax solutions for the integration, search and exchange of phenotype and genotype information. Hum. Mutat., 33, 867–73. Auffray,C. et al. 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(2010) XGAP: a uniform and extensible data model and software platform for genotype and phenotype experiments. Genome Biol., 11, R27. van der Velde,K.J. et al. (2017) GAVIN: Gene-Aware Variant INterpretation for medical sequencing. Genome Biol., 18, 6. Wilkinson,M.D., Sansone,S.-A., et al. (2017) A design framework and exemplar metrics for FAIRness. bioRxiv. Wilkinson,M.D., Verborgh,R., et al. (2017) Interoperability and FAIRness through a novel combination of Web technologies. PeerJ Comput. Sci., 3, e110. Wilkinson,M.D. et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data, 3, 160018.  Downloaded from https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty742/5085379 by University of Groningen user on 05 September 2018

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MOLGENIS Research: Advanced bioinformatics data software for non-bioinformaticians

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