MOLGENIS EMX2 database at http://umcgresearchdatacatalogue.nl
Temporary catalogue schema for rdf harvesting
General information describing an endpoint
DataCatalogueFlat
Resource of data and/or samples. Use 'resource type' to specify
Definition of a sample set
Subpopulations defined for this resource
An organisation, department or individual
Listing of contact persons per resource
Definition of a dataset
External identifier(s) for this resource
Links between resources, e.g. data source and databank
Publications based on doi
Mapping from collected resources to standard/harmonised resources, optionally including ETL syntaxes
Number of participants per cohort age group, optionally divided per sex
Definition of a variable
Definition of a collection event for a resource
Documentation attached to a resource
Research organisations, departments and research groups
Listing of variables from another resource that are re-used by a resource
Listing of categorical value+label definition in case of a categorical variable
Mappings from collected or source datasets to harmonised or target datasets
Quantitative information on the resource
Internal identifier(s) for this resource
Mappings from collected or source variables to target or harmonised variables, optionally including ETL syntax
A unique proper name or character sequence that identifies this particular person. In Beacon v2: id, in FAIR Genomes: PersonalIdentifier. In RD3: subjectID.
This row is an instance of one or more of these class(es) other than the Endpoint table.
A name given to the resource.
An account of the resource version.
An account of the resource.
An entity responsible for making the resource available.
A language of the resource.
A legal document giving official permission to do something with the resource.
An established standard to which the described resource conforms.
Information about rights held in and over the resource.
Information about who access the resource or an indication of its security status.
Relevant contact information for the cataloged resource
A keyword or tag describing a resource.
A main category of the resource. A resource can have multiple themes.
A description of the service end-point, including its operations, parameters etc.
With the definition of the FDP as a type of metadata service that serves metadata catalogs, the relation between the Metadata Service and dcat:Catalog is represented by this predicate.
The relation should point to an URI that contains the version of the FDP specifications for which this instance of the FDP conforms to
Set to true if the resource onboarded to the EJP-RD Virtual Platform contains personal data, personal data meaning data related to identified or identifiable persons (as per GDPR definition), otherwise false. Must be String as workaround for current FDP reference implementation limitations.
This property must be attached to every entity that you wish the EJP-RD Virtual Platform to explore.
The DCAT type such as dcat:Catalog as computed field
FDP endpoint to associated with the catalogue
LDP membership relation
General information
Do you want your metadata to be compatible with HRICore v2.0.1, so that the metadata can be shared with the Dutch National Health Data Catalogue?
Internal identifier
Persistent identifier
Name used in international projects
If different from above, name in the national language
Acronym, if applicable
Type of resource, e.g. registry, cohort, biobank
If other, describe the type of resource
Type of catalogue
Type of cohort
Type of clinical study
Type of registry or health record
Type of network
Link to the website or homepage
Short description
Keywords to increase findability of this resource. Try to use words that are not used in the description. Keywords are not displayed on the resource page
Internal identifier(s) for this resource (e.g. UMCG register number)
External identifier(s) for this resource (e.g. EUPASS number or BBMRI persistent identifer)
The year when the resource started, e.g. when data or samples started to be collected or extracted, or when the resource was established
The year the resource ceased, e.g. when data or sample collection was stopped
Description of time span
Contact e-mail address
Logo of the resource, for use on homepages etc.
Status of the study
The specification of the catalog metadata schema
This relation points to the URI of the custom relation/predicate used to relate the FAIR Data Point with its member catalogs. The object of this property MUST be fdp-o:metadataCatalog.
When the data of the resource was first published
When the data of the resource was last modified
Structure of the resource
The study design of this cohort, i.e. cross-sectional or longitudinal
Short description of the study design of this resource, if applicable
A schematic depiction of the study design of this resource
Retrospective or prospective data collection
Describe the process of collection and recording of data
Description of the reason why the data bank is sustained by the organisation (e.g. for surveillance, clinical purposes, financial or administrative purposes, research purposes)
What triggers the creation of a record in the data bank? e.g. planned collection events, hospital discharge, specialist encounter, dispensation of a medicinal product, recording of a congenital anomaly
Based on the record trigger or promt, what is the unit(s) of observation of a record (e.g. person, prescription)?
List of cohorts or subpopulations defined in this resource, if applicable
List of data/sample collection events defined for this collection, if applicable
List of other data collections included in this network, e.g. cohorts, biobanks or real world data sources. Do not list other networks or catalogues here
List of other collections included in this resource
Description of the population that is sampled in this resource
Total number of individuals for which data is collected
Number of individuals for which samples are collected
Provide a summary description of the underlying population from which participants are sampled
Select population of interest
If population of interest is 'Other', please specify which other population has been studied
Countries where data/samples from this resource largely originate from
Geographical regions where data/samples from this resource largely originate from
Which population age groups are captured in this resource? Select all that are relevant
Minimum age of participants within this resource
Maximum age of participants within this resource
Inclusion criteria applied to the participants of this resource
Other inclusion criteria applied to the participants of this resource
Exclusion criteria applied to the participants of this resource
Other exclusion criteria applied to the participants of this resource
Select the possible causes / events that trigger the registration of a person in the data source
If other, specify the causes of entry to the underlying population
Select the possible causes / events that trigger the de-registration of a person in the data source
If other, specify the causes of exit from the underlying population
Is information on a specific disease subpopulation collected (e.g. as in a disease-specific registry or cohort)?
Is information on specific cancer subtype(s) collected? If yes, select topology specifications
Is information on specific cancer subtype(s) collected? If yes, select morphology specifications
Estimated percentage of the population covered by the data source in the catchment area. Please describe the denominator
Description of the population covered by the data source in the catchment area whose data are not collected, where applicable (e.g. people who are registered only for private care)
Counts of e.g. samples defined for this resource
List of relevant collaborations
Organisations involved in this resource and their role(s)
From organisations involved, point out the organisation that published this resource
From organisations involved, point out the organisation(s) that created this resource
Persons that contributed to this resource
From people involved, point out the primary contact point for this resource
List of networks, consortia or catalogues included in this network
The consortia or networks that this resource is part of
Data model and contents
List of datasets contained in this resouce if applicable
List of sample sets contained in this resource if applicable
Areas of information that were collected
If 'other', specify additional quality of life measures
If 'other', what cause of death vocabulary is used?
If 'other', what indication for use vocabulary is used?
If 'other', what genetic data vocabulary is used?
If 'other', description of the setting of care
If 'other', description of the medicinal product vocabulary
If 'other', what prescriptions vocabulary is used?
If 'other', what dispensings vocabulary is used?
If 'other', what procedures vocabulary is used?
If 'other', what biomarker data vocabulary is used?
diagnosis medical event vocabulary other
If 'other', what diagnosis medical event vocabulary is used?
Are a data dictionary and a data model available?
If data on a specific disease is collected, which diseases does the data source collect information on
If the data bank contains biospecimens, what types of specimen
Languages that the records are recorded in (ISO 639)
Can there be multiple entries for a single person in the data bank?
Data linkage
Is there a unique identifier for a person in the data bank?
Describe the variable that is used as a unique identifier for a person in the data bank. If the unique identifier is not at level of a person (for example hospital encounter), describe how this is translated to an individual level
Is the data from this data source linked to other data sources?
Linkage options with additional data resources that are available for this resource
Can this data source be linked to other data sources?
List of resources that are or can be linked to this main resource
Access and validation information
What type of informed consent was given for data resource?
Is informed consent required for use of the data for research purposes?
If 'other', describe the conditions when informed consent is required
Information that indicates whether the resource is publicly accessible, has access restrictions or is not public. A default is set for non-public access, because most health datasets contain sensitive information and rules and licenses apply to get access.
Codes defining data access terms and conditions
Codes defining data use terms and conditions
Description of data access terms and use conditions
Does a fee apply to gain access to data of this resource?
Can identifiable data be accessed in the data bank (including patient/practitioner name/practice name)?
If yes above, what is the route to access or process this information? What permission is required?
Can individual patients/practitioners/practices be contacted in the data bank?
If yes to the above, what is the route to access or process this information? What permission is required?
Can (an extract of) the data bank be accessed with permission by a third party?
If above is 'yes', describe the conditions under which third-party access may be granted
Can (an extract of) the data bank be accessed with permission by a non-EU/EEA institution?
If yes to the above, describe the conditions under which non-EU/EEA access may be granted
If the data bank contains biospecimens (e.g. tissue samples), can these be retrieved?
If yes to the above, describe the conditions under which permission to retrieve biospecimens may be granted
If available, provide a link to documents or webpages that describe the overall governance of the data source bank (governing data access or utilisation for research purposes by existing DAPs)
Is an approval needed to publish the results of a study using the data
Information on the regularity of updates and time lags
Select whether this resource is a closed dataset or whether new data is released continuously or at a termly basis
Description of the release cycle of this resource
Total number of unique records captured in the data bank (most recent count)
Refreshing rate (in months)
Average number of days between refresh of data bank with new records
How many days is the lag time after refresh before a record can be extracted? (e.g. a lag time may occur if the originator conducts quality checks)
If data are refreshed on fixed dates (e.g. every June and December), when are the refreshes scheduled? Select all that apply from the following:
Date of last update/refresh
Are records preserved in the data bank indefinitely?
If 'no' to the above, for how long (in years) are records preserved in the data bank?
List of relevant studies conducted using the data bank
Is there a standard operating procedure document that defines the processes and procedures for data capture and management?
Has the data source successfully undergone a formal qualification process (e.g. from the EMA, ISO or other certifications)?
Has the data source successfully undergone a qualification process (e.g. from the EMA)? If yes, describe the qualification(s) granted
Are external parties allowed to audit the data? For example, is it possible for an external party to audit the quality or validity of the data source?
Describe the completeness of the data bank (e.g. variables with more or fewer missing values)
Describe any changes in completeness of the data bank (e.g. variables with more or fewer missing values) that have occurred over time
What methods or processes are applied to check completeness of the data bank?
Describe the quality of the data bank (e.g. variables with more or fewer missing values)
Describe any changes in quality of the data bank that have occurred over time
Can validity of the data in the data bank be verified, e.g. by review of origin medical charts?
How often are data quality checks and validation steps conducted on the data bank?
What methods or processes are applied for data quality checks and validation steps conducted on the data bank?
What methods or processes are applied to correct illogical values in the data bank?
If available, provide a link to a publication of the data quality check and validation results
Use of standard data models and ontologies
Overview of dataset mappings available
If not in list above, give the name of cdm(s) used by this data source
Are data mapped to standardised vocabularies during ETL to the CDM? If yes, what vocabularies are used for events, such as diagnoses?
If other, what other vocabularies are used?
Other information
Publication(s) about this resource
Specify the main financial support sources for the data source in the last three years. Select all that apply
The source of funding for the study. Select all that apply
Statement listing funding that was obtained for this resource
Citation regulation for this resource
Acknowledgement statement and citation regulation for this resource
A statement of any changes in ownership and custody of a resource since its creation that are significant for its authenticity, integrity, and interpretation
Descriptive document(s) available for this resource, e.g. informed consent
Any other information that needs to be disclosed for this resource
A main category of the resource. A resource can have multiple themes, for health data theme must at least take the value 'Health', which is set as a default
The legislation that mandates the creation or management of the resource. For health datasets, the value must include the data governance act, which is set as a default. URI's to other legislations may be included
When was the data collection planned to start?
When was the data collection actually start?
When was the data analysis planned to start?
When did the data analysis actually start?
Data sources that provided data into this study
Codes of the medical conditions studied
Date on which the study data was extracted
Methodological aspects
A brief summary of the analysis method (e.g. risk estimation, measures of risk, internal/external validity)
A short description of the study objective
A brief summary of the results of the study on study completion (e.g. from abstract)
Resouce that these samples are part of
Sample set name
Defines the types of samples in the sample set
Resource this subpopulation is part of
Subpopulation name, e.g. 'mothers in first trimester','newborns'
Persistent identifier for this subpopulation
Subpopulation description
Keywords to increase findability of this subpopulation. Try to use words that are not used in the description. Keywords are not displayed on the subpopulation page
Number of participants in this subpopulation
Total number of unique individuals per age(group), gender and year
Year of first included participant
Year of last included participant. Leave empty if data collection is ongoing
Age groups within this subpopulation
Minimum age of participants within this subpopulation
Maximum age of participants within this subpopulation
Disease groups within this subpopulation, based on ICD-10 and ORPHA code classifications
Comorbidity within this subpopulation, based on ICD-10 classification
Countries where data from this subpopulation largely originate from
Geographical regions where data from this subpopulation largely originate from
Inclusion criteria applied to this subpopulation
Other inclusion criteria applied to this subpopulation
Exclusion criteria applied to this subpopulation
Other exclusion criteria applied to this subpopulation
Contact point for this subpopulation. Value copied from Resources table
Organisation that published this subpopulation. Value copied from Resources table
Organisation(s) that created this subpopulation. Value copied from Resources table
When the data for this subpopulation was first published
When the data for this subpopulation was last modified
A main category of the subpopulation. A subpopulation can have multiple themes, for health data theme must at least take the value 'Health', which is set as a default
The legislation that mandates the creation or management of the subpopulation. For health datasets, the value must include the data governance act, which is set as a default. URI's to other legislations may be included
Resource this Agent is associated with
Internal identifier. Ideally fill out acronym for an organisation (e.g. UMCG) and full name for a person (e.g. Ada Lovelace)"
Type of agent: person or organisation
Name of the individual
Organisation name from Research Organization Registry (ROR).
If your organisation is not listed, please look up in ROR: https://ror.org/ and preferably provide ROR id, otherwise fill out your organisation name.
Name of department within the organisation
A website representing this Agent
E-mail address to contact this organisation or individual
Logo of the organisation
Type(s) of contribution or role in the resource
Organisation name, copied value from ROR ontology for DCAT conform RDF generation
ROR id, copied value from ROR ontology for DCAT conform RDF generation
Organisation website, copied value from ROR ontology for DCAT conform RDF generation
Resource the contact is affiliated with
Description of the role
First name of the contact person
Last name of the contact person
Display name of the contact person
Surname prefix, if applicable
Initials of the contact person
Title of the contact person
Affiliated organisation of the contact person
Contact's email address
Orcid of the contact person
Link to contact's homepage
Contact's photograph
Description of contact's expertise
Resource of which the dataset is part
Unique dataset name in the model
Short human readable description
Defines the type of dataset, e.g. genomic
Defines what each record in this table describes
Enables grouping of table list by topic and to display tables in a tree
Description of the role/function of this table
Count of the number of records in this table
Common dataset models this dataset has been mapped into
Source datasets that have been mapped to this harmonised dataset
When this dataset was introduced, if applicable
When this dataset was removed, if applicable
Resource that this external identifier belongs to
External identifier
External identifier type
If 'other', enter external identifier type
Main resource for this linkage
Linked resource
The linkage method that was used to link resources
If a single variable (or linkage key) is used to link resources, provide a name and description of the variable
If a single variable is used to link resources, is the variable a unique identifier?
Provide a high-level description of the completeness of linkages that are currently available between resources (max 100 words)
Does the data source constitute of linked data sources?
Resource that this publication is part of
Digital object identifier
Publication title
Whether this publication is a marker or design publication
Formatted reference string (we aim to have this autogenerated)
Data resource that is being mapped to a (common) data model
Version of the resource that is being mapped
Model being mapped to
Version of the model being mapped to
If cdm is not under targets, which cdm was mapped to? (default = not applicable)
Mapping status
The frequency of the ETL process, in months
Link to ETL specification documentation
Documentation concerning ETL details
Resource id
Subpopulations for these counts
Select the age group for these counts
Total number
Number of females
Number of males
Resource that this variable is part of
Dataset that this variable is part of
Name of the variable, unique within a dataset
Refer to variable definition in other datasets
Human readable name, if applicable
Description of the variable
In case of protocolised data collection this defines the moment(s) in time this variable is collected on
Data type, e.g. string, integer, decimal, date, datetime, etc.
In case of a continuous variable, the unit that the variable values are expressed in
When this variable was introduced
When this variable was removed, if applicable
Listing of variables from other resources that are reused by this resource
The repeat unit, in case the variable was repeatedly collected over time, e.g. month or week
The first repeat unit
The last repeat unit
Example values for this variable
Listing of variable values, filled out in Variable values table
Keywords that will make the variable easier to find
Vocabularies used to express the values of this variable
Any other information on this variable
Listing of harmonised variables this variable was mapped to, filled out in Variable mappings table
Resource this collection event is part of
Name of the collection event
Persistent identifier for this collection event
Description of the collection event
Subpopulations that are targetted by this collection event
Keywords to increase findability of this collection event. Try to use words that are not used in the description. Keywords are not displayed on the collection event page
Start date of data collection
End date of data collection. Leave empty if collection is ongoing
Age groups included in this data collection event
Number of participants sampled in this data collection event
Areas of information that were extracted in this data collection event
Methods of data collection used in this collection event
Samples that were collected in this collection event
Standardized tools, e.g. surveys, questionnaires, instruments used to collect data for this collection event
If 'other', please specify
Name 10-20 relevant variables that were collected in this collection event
Contact point for this collection event. Value copied from Resources table
Organisation that published this collection event. Value copied from Resources table
Organisation(s) that created this collection event. Value copied from Resources table
When the data for this collection event was first published
When the data for this collection event was last modified
A main category of the collection event. A collection event can have multiple themes, for health data theme must at least take the value 'Health', which is set as a default
The legislation that mandates the creation or management of the collection event. For health datasets, the value must include the data governance act, which is set as a default. URI's to other legislations may be included
The resource this documentation is for
Document name
Type of documentation
Description of the document
Hyperlink to the source of the documentation
Optional file attachment containing the documentation
Whether the organisation is the lead organisation of this resource
Resource reusing the variable
Variable from another resource that is reused
Resource the variable belongs to
Dataset the variable belongs to
Variable name
Variable value, e.g. '1'
Variable label, e.g. 'Yes'
Order that the variable values should be displayed in
Whether this variable value indicates that data is missing, e.g. '-1' for 'NA'
Reference to ontology term that defines this categorical value
When this variable value was introduced, if applicable
When this variable value was removed, if applicable
Id of the resource being mapped from
Name of the dataset being mapped from
Network being mapped to
Name of the dataset being mapped to, or harmonised with
Order in which table ETLs should be executed for this source-target combination
Human readible description of the mapping
Formal definition of the mapping, ideally in executable code
Select the relevant age group for this quantitative information
Total number of unique individuals with records captured in the data source (most recent count). In the catalogue, this will accommodate counts per year
Number of unique, active, or currently registered individuals with records captured in the data source (most recent count). In the catalogue, this will accommodate counts per year
Number of unique individuals with records of biological samples (e.g., blood, urine) (most recent count). In the catalogue, this will accommodate counts per year
Median years for which unique individuals with records captured in the data source are observable (most recent count)
Median time for which unique individuals with records captured in the data source are observable (most recent count)
Median age of individuals within data source
Proportion of females in the data source
Resource that this internal identifier belongs to
Internal identifier type
If 'other', enter internal identifier type
Id of the resource that contains the source variable(s)
Name of the dataset that contains the source variable(s)
Optional, source variable that was mapped from. You may also indicate that a mapping to a target variable was not done and leave this field empty (match = not available)
Optional, variables from other source datasets. Initially one may only define mapping between releases
Id of the resource that contains the target variable
Name of the dataset that contains the target variable
Target variable that was mapped to
In case of mapping to a repeated variable, specify which repeats were mapped to
Harmonisation match, i.e., complete, partial, na
Additional notes or comments
The Academic General Practitioner Network (AHON), part of the Department of Primary and Long-term Care of the University Medical Center Groningen (UMCG), manages the AHON-database that comprises routinely collected care data from affiliated registration practices in the North of the Netherlands. The design of the AHON-database allows researchers to use it for both cross-sectional and longitudinal research.
Linkage with other datasets is possible using pseudonyms generated by ZorgTTP (as preferred trusted third party). Until now we have experience in linking data from NKR, CBS, GGD, Certe, and Lifelines. Linkage with other (health related) registries might also be possible.
Any researcher can request a dataset for scientific research with the intention of publication in a national or international journal. However, studies funded by a health insurance company or by the pharmaceutical industry will not be granted access to the database. See also https://huisartsgeneeskunde-umcg.nl/system/files/factsheet_ahon-database_information_for_researchers.pdf
Every three months new data are added to the longitudinal AHON-database.
This research has been conducted using the AHON-database, with the support of [list of AHON personnel that supported the research]. The AHON-database is an ongoing longitudinal database which aims to give insight into medical care provided in general practices in the North of the Netherlands. Starting in 1998, pseudonymized patient records have been added to the AHON-database every three months from a growing number of participating general practices. Patients are informed by their general practices via folders, posters, and websites of their participation in the AHON-database. Individual patients can fill out an opt-out form in order to not have their data recorded in the AHON-database.
Research in acute care faces many challenges, including enrollment challenges, legal limitations in data sharing, limited funding, and lack of singular ownership of the domain of acute care. To overcome some of these challenges, the Center of Acute Care of the University Medical Center Groningen in the Netherlands, has established a de novo data-, image- and biobank named "Acutelines". Acutelines is initiated to improve recognition and treatment of acute diseases and obtain insight in the consequences of acute diseases, including factors predicting its outcome. Thereby, Acutelines contributes to development of personalized treatment and improves prediction of patient outcomes after an acute admission.
Included are adult ED patients with the highest and second highest urgency triage categories of the Emergency Severity Index (ESI) triage system, and patients with the third highest category when arriving by (Helicopter) emergency medical service. In addition, patients with several specific conditions regardless their triage category are included: sepsis, shock, syncope, anaphylaxis, acute renal failure, intoxications, asthma/COPD exacerbations, deep venous thrombosis or pulmonary embolism, gastrointestinal bleeding and patients who are bleeding (any source) while using vitamin-K antagonist or DOAC.
Based on the type of data and/or biomaterials used from Acutelines, we offer a proposed section of text describing the methods to include in the paper. Further requirements (negotiable): sharing raw data generated in the project with Acutelines for further scientific analysis and with the right to publish by Acutelines, sharing scientific output in the form of (a) co-authorship(s) and/or intellectual property, lay-person summary for website and/or social media for outreach purposes.
The Genome of the Netherlands (GoNL) Project characterizes DNA sequence variation, common and rare, for SNVs, short insertions and deletions (indels) and larger deletions in 769 individuals of Dutch ancestry selected from five biobanks under the auspices of the Dutch hub of the Biobanking and Biomolecular Research Infrastructure (BBMRI-NL). The samples come from a representative sample of 250 trio-families from all provinces in the Netherlands. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. Samples where contributed by LifeLines (http://lifelines.nl/lifelines-research/general), The Leiden Longevity Study (http://www.healthy-ageing.nl; http://www.langleven.net), The Netherlands Twin Registry (NTR: http://www.tweelingenregister. org), The Rotterdam studies, (http://www.erasmus-epidemiology.nl/rotterdamstudy) and the Genetic Research in Isolated Populations program (http://www.epib.nl/research/geneticepi/research.html#gip). The sequencing was carried out in collaboration with the Beijing Institute for Genomics (BGI). The analysis was done by a consortium lead by UMCG, LUMC, Erasmus MC, VU university and UMCU, see http://www.nlgenome.nl. Funding for the project was provided by the Netherlands Organization for Scientific Research under award number 184021007, dated July 9, 2009 and made available as a Rainbow Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL).
MeDALL- (Mechanisms of the Development of ALLergy) is a collaborative project supported by the European Union under the Health Cooperation Work Programme of the 7th Framework programme (grant agreement number 261357).MeDALL epigenetics study includes illumina 450k methylation measurements from 4 cohorts: Infancia y Medio Ambiente (INMA), Etudes des Déterminants pré et postnatals précoces du développement et de la santé de l’ENfant (EDEN), Children’s Allergy Environment Stockholm Epidemiology study(BAMSE), and Prevention and Incidence of Asthma and Mite Allergy (PIAMA)
Single-cell RNA sequencing reveals cell-type specific eQTLs in peripheral blood mononuclear cells
This proof-of-concept study shows that single-cell RNA sequencing in ~25,000 peripheral blood mononuclear cells (PBMCs) of 45 donors allows for the identification of cell type-specific cis expression quantitative trait loci effects (cis-eQTLs). Moreover, the significant top-eQTL effects show consistent allelic direction (94%) with previously published whole blood deepSAGE data. These cell type-specific eQTLs may give us additional insight into the downstream consequences of genetic risk factors for immune-mediated diseases.
Gene expression by RNAseq in bronchial biopsies of asthmatics, asthma in remission and healthy subjects
The study focus was differential expression in bronchial biopsies between persistent asthma, asthma in remission and healthy controls using RNAseq.
DNA methylation in bronchial biopsies of asthmatics, asthma in remission and healthy subjects
The study focus was differential methylation in bronchial biopsies between persistent asthma, asthma in remission and healthy controls using Illumina 450k arrays.
Celiac disease (CeD) is a common immune-mediated disease of the small intestine that is triggered by exposure to dietary gluten. While the HLA locus plays a major role in disease susceptibility, 39 non-HLA loci were also identified in a study of 24,269 individuals. We now build on this earlier study by adding 4,125 additional Caucasian samples including an Argentinian cohort genotyped on the Immunochip. In doing so, we not only confirm the previous associations, we also identify two novel independent genome-wide significant associations.
Epigenome-wide association study of asthma remission in whole blood and nasal epithelium
Asthma is a chronic respiratory disease which is not curable, yet some patients experience spontaneous remission.In this study, we hypothesized that epigenetic mechanisms may be involved in asthma remission.
The Dutch Microbiome Project: Defining the (un)healthy gut microbiome
The Dutch Microbiome Project (DMP) data includes raw data generated by shotgun metagenomic sequencing of faecal samples of 8,208 Dutch individuals, processed microbiome data analysed using Biobakery2 tools (including taxonomy, pathway, virulence factors and antibiotic resistance gene profiles), and basic phenotypes (age and sex, BMI). Data is made available in two randomly assigned batches to facilitate the data access and maintenance.
Access to this dataset requires a minimal access procedure (data access form at https://forms.gle/eHeBdXJMXbVvCJRc8 or email request to the data access committee (DAC) of this study, listed at DAC EGAC00001001996, https://ega-archive.org/dacs/EGAC00001001996). This access procedure is to ensure that the data is being requested for research/scientific purposes only and thus complies with the informed consent signed by Lifelines participants, which specifies that the collected data will not be used for commercial purposes. Submitted data access forms will be evaluated by the DAC and Lifelines, and a response to requests will be given within two weeks. For requests from verified academic parties, access will be given without further delay. For requests from commercial parties, Lifelines will perform a pre-DPIA (Data Privacy Impact Assessment) to assess the risks of the proposed processing of personal data (e.g. purpose, storage, access, archiving, etc.) with respect to the GDPR (EU privacy laws) subject rights. Based on the outcome of the pre-DPIA, Lifelines will decide whether sharing data with the commercial entity is allowed and/or whether additional measures have to be taken.
Nasal DNA methylation at three CpG sites predicts childhood allergic disease
We used machine learning models to systematically integrate large-scale multi-omics data from genomics, DNA methylation in blood, and nasal and environmental factors. We firstly evaluated the prediction power of each omics layer and identified that the prediction power was largely attributed to nasal methylation. We further built a prediction model based on only three nasal CpG sites that can predict childhood allergic disease.
Gluten-free diet intervention: 11 individuals, 3 time points (T1: 1 week before intervention, T2: at the intervention; T3: 5 weeks after intervention. Fecal samples taken at each time point were sujbject of VLP enrichment and routine metagenomics sequencing
Single-cell RNA sequencing dissects gene-environment interactions on gene expression and regulation in immune cells.
This study aims to dissect gene-environment interactions on gene expression and regulation in immune cells using scRNA-seq data from 120 donors being stimulated with 3 pathogens, for 2 different timepoints. More specifically, we identified context- and cell-type-specific eQTLs and co-expression QTLs in PBMCs.
The Gut Microbiome of liver transplant recipients – Cross-sectional + Longitudinal (renal and liver)
TransplantLines is designed as a single-center, prospective cohort study and biobank including all different types of solid organ transplant recipients as well as living organ donors. In the TransplantLines gut microbiome study the gut microbiome of solid organ transplant recipients is characterized and linked to clinical phenotypes. This batch contains the cross-sectional data from liver transplant recipients and longitudinal data from renal and liver transplant recipients.
Single cell whole genome sequencing of high hyperdiploid acute lymphoblastic leukemia
This dataset was collected from viable bone marrow cells obtained at diagnosis from nine patients with high hyperdiploid ALL and one normal bone marrow sample. All samples were subjected to low pass single cell whole genome sequencing with the median sequencing coverage of 0.02x. Single nuclei in G0/G1 phase were isolated using a fluorescence-activated cell sorting (FACS) cytometer. DNA libraries were constructed and associated next-generation sequencing was carried out by European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Further details regarding the DNA libraries construction are available by Bos et. al., 2019 (https://link.springer.com/protocol/10.1007/978-1-4939-8931-7_15). The dataset has been used for copy number aberrations analysis.
scRNA-seq samples from healthy and asthma donors part 1.
Bacteriophage Immunoprecipitation Sequencing (PhIP-Seq) of LLD participants. Libraries and protocol were developed in the Weizmann Institute of Science. Two different libraries were used, containing microbial and other immunological peptides respectively.
Single-cell dissection of the immune response after a myocardial infarction
The immune response plays a pivotal role in initiating cardiac repair and remodeling in both the acute and chronic phase after ST-Elevated myocardial infarction (STEMI). To dissect the longitudinal character of the immune response in STEMI patients over time, we performed single-cell RNA-sequencing (scRNA-seq) on a total of 95,995 peripheral blood mononuclear cells (PBMCs) collected from 38 STEMI patients at hospital admission, 24 hours (acute phase) and 6-8 weeks (chronic phase) after STEMI. Additionally, comparison to healthy controls was performed using previously generated scRNA-seq data on 33,878 PBMCs from 38 age- and sex-matched healthy controls from the Dutch population cohort Lifelines DEEP.
Inflammatory bowel diseases (IBD), e.g. Crohn’s disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases. A comprehensive overview of an IBD-specific antibody epitope repertoire is, however, lacking. We leveraged a high-throughput phage-displayed immunoprecipitation sequencing (PhIP-seq) workflow to identify antibodies against 344,000 antimicrobial, immune and food antigens in 497 IBD patients.
RSV infection of primary bronchial epithelial cells in asthma
Primary bronchial epithelial cells derived from healthy donors and asthma patients were differentiated in Air-Liquid-Interface condition and infected with the respiratory syncytial virus. Single-cell RNA sequencing analysis of these cultures was used to study the transcriptomic response of the cells to RSV.
CS Baby Biome randomized control trial aims to investigate if the timing of intrapartum antibiotics given to mother influences the infant gut microbiome composition. The study was performed in women delivering via elective CS, who received antibiotics prior to skin incision, or after umbilical cord clamping.
The Lifelines-NEXT is a cohort of newborns followed up from birth up to the age of 1 year and later. The cohort includes approximately 1,500 newborns and their mothers from the 3 provinces of the Northern Netherlands.
Neoadjuvant immune checkpoint blockade in women with mismatch repair deficient endometrial cancer
Neoadjuvant immune checkpoint blockade (ICB) has shown unprecedented activity in mismatch repair deficient (MMRd) colorectal cancers, but its effectiveness in MMRd endometrial cancer (EC) remains unknown. In this investigator-driven, phase I, feasibility study (NCT04262089), 10 women with MMRd EC of any grade, planned for primary surgery, received two cycles of neoadjuvant pembrolizumab (200 mg IV) every three weeks. A pathologic response (primary objective) was observed in 5/10 patients, with 2 patients showing a major pathologic response. No patient achieved a complete pathologic response. A partial radiologic response (secondary objective) was observed in 3/10 patients, 5/10 patients had stable disease and 2/10 patients were non-evaluable on Magnetic Resonance Imaging (MRI). All patients completed treatment without severe toxicity (exploratory objective). At median duration of follow-up of 22.5 months, two non-responders experienced disease recurrence. In-depth analysis of the loco-regional and systemic immune response (predefined exploratory objective) showed that monoclonal T cell expansion significantly correlated with treatment response. Tumour-draining lymph nodes displayed clonal overlap with intra-tumoural T cell expansion. All pre-specified endpoints, efficacy in terms of pathologic response as primary endpoint, radiologic response as secondary outcome and safety and tolerability as exploratory endpoint, were reached. Neoadjuvant ICB with pembrolizumab proved safe and induced pathologic, radiologic, and immunologic responses in MMRd EC, warranting further exploration of extended neoadjuvant treatment.
Lifelines NEXT (LLNEXT) is a birth cohort designed to study the effects of intrinsic and extrinsic determinants on health and disease as epidemiological research has shown there is a strong relationship between pre-conceptional, prenatal, birth and early-life factors and lifelong health. It is embedded within the Lifelines cohort study, a prospective three-generation population-based cohort study recording the health and health-related aspects of 167,729 individuals living in Northern Netherlands. The nesting of the study within Lifelines enables us to include pre-conceptional transgenerational data which can be used to identify other factors that influence health in other families in a four-generation design. LLNEXT included 1.450 pregnant women and their children residing in the northern provinces of The Netherlands. The women are followed from the third month of their pregnancy. As of 2018, partners were also invited to enroll in LLNEXT. Standardized protocols and guidelines are available upon request.
Upon giving informed concent, the LLNEXT participants are asked to fill in questionnaires and donate biomaterials at several time points during pregnacy, at birth and after delivery of themselves and their child.
20240318_LLNEXT_Collected materials_questionnaires.png
Pregnant Living in the northern provinces of the Netherlands (Groningen, Friesland, Drenthe) Willing and able to fill in (digital) questionnaires Willing and able to collect biomaterials from themselves and the enrolled child
Lifelines NEXT is currently collecting data. Until then, data is being added to the database and biobank until collection is completed.
The following funding agencies were used to build the LLNEXT cohort and generate samples. LLNEXT The data collection of the core study was funded by a grant from the UMCG Hereditary Metabolic Diseases Fund and by grants awarded to Alexandra Zhernakova: VIDI Grant [016.178.056] from the Netherlands Organization for Scientific Research [NWO], European Research Council [ERC] starting Grant [ERC-715772], and Gravitation program EXPOSOME-NL (NWO grant number 024.004.017). Dependent on the research question and the data used, the recipient shall acknowledge the corresponding funds that made the recipients’ research possible. Living Lab Newborn Funding for the Newborn project, including inclusion of fathers, was provided by the European Union, the Northern Netherlands Alliance (SNN), the provinces of Friesland and Groningen and the municipality of Groningen. Furthermore, Philips provided continuous measurement devices for this add-on initiative. Nestlé NEXT LLNEXT is powered by Nestlé. The collaboration with Nestlé (Nestlé NEXT) is awarded with the Public-Private Partnerships (PPP) Allowance made available by Health~Holland (Top Sector Life Sciences & Health).
The data and biological samples used in this manuscript are provided by LLNEXT. The authors are grateful for the participation of all the parents and children in LLNEXT. We also thank the LLNEXT team and maternity care providers for recruiting participants, collecting material during and after childbirth and building and maintaining the cohort.
We have established a hospital-based data-biobank, entitled OncoLifeS (Oncological Life Study: Living well as a cancer survivor) to link routinely collected clinical data with preserved biological specimens and quality of life assessments. The OncoLifeS data-biobank has been designed to facilitate cancer research by providing clear phenotypic and genotypic data in a clinical context. Observational study involves collecting routine clinical data about carefully selected patient groups, and this can be combined with genetics and omics data from biological specimens. Indeed, understanding cancer heterogeneity, its temporal evolution over time, and the outcomes of guided treatment depend on accurate data about patient characteristics and their clinical management. Linking these routine clinical data to preserved biological specimens can then enable reproducible research capable of discovering biomarkers of cancer and/or treatment response, evaluating personalized treatment approaches, and increasing patient awareness. Including quality of life assessments may provide relevant information on how cancer and its treatment affects the subjective well-being of patients.
Adolescents and young adults with cancer
Hereditary increased risk of breast and ovarian cancer
European reference network on rare cancers (EURACAN)
European General Data Protection Regulation
PACS (Picture Archiving and Communication System)
Dutch Medical Research Involving Human Subject Act (WMO)
Human Tissue and Medical Research: code of conduct for responsible use
Participants diagnosed with cancer or an increased risk for cancer.
There is consent for linking with various databases and registers, for example general practitioners, pharmacy, Palga, Lifelines, IKNL.
Interested stakeholders can submit a research proposal OncoLifeS the coordinator of the OncoLifeS data-biobank and the involved tumor working group. The OncoLifeS scientific advisory board, which comprises representatives of each participating tumor working group, will then review the proposal. This board then advises the OncoLifeS steering committee on whether to approve requests for the use of clinical data and/or biomaterials. If approved, the project coordinator retrieves anonymized data or biomaterials from the OncoLifeS database or biobank. A protected workspace is provided for access to and analysis of data regarding cancer biomarkers, treatment response, and treatment effects for different patient outcomes, including quality of life. This can also be used to give health care providers performance-related feedback and to monitor oncological care quality. If biomaterials are requested, agreements are made on how, where, and by whom they will be analyzed, and results from these analyses will be added to the workspace. On this workstation, the researcher will have access to the set of requested data. In addition, software is provided for data analysis.
Access is given to the requested variables. It is possible to add data to the OncoLifeS data.
This metadata catalogue was commissioned by the University Medical Centre Groningen (UMCG). The aim of the UMCG Research Data Catalogue is to increase the visibility of UMCG data and sample resources and thereby stimulate their findability, accessibility and reuse, three major pillars of the FAIR principles. The UMCG Research Data Catalogue includes a wide variety of data and sample resources, from large population-based cohorts and clinical biobanks to studies and everything in between. Some resources date back all the way to 1962. The catalogue was developed by the Genomics Coordination Centre (GCC), the Cohort and Biobank Coordination Hub (CBCH) and the Digital Competence Center (DCC) of the UMCG.
Children of pregnant women included in LLNEXT. They are followed until one year after birth.
Pregnant women included in LLNEXT. They are followed from the third months of pregnancy until one year after delivery.
Pregnant Residing in Groningen, Friesland or Drenthe (northern provinces of The Netherlands) Willing and able to fill in questionnaires Willing and able to donate biomaterials
Partners of pregnant women included in LLNEXT. They are asked to fill in three questionnaires, provide antropometic measures and donate a blood sample.
Partner of LLNEXT mother Willing and able to fill in questionnaires Willing and able to donate biomaterials
Patients with a brain tumor or other neuro-oncological disorders, such as tumors in or near the spinal cord, or damage to the nervous system
OncoLifeS OncoLifeS Hereditary Breast and Ovarian Cancer (historical)
OncoLifeS Hereditary Breast and Ovarian Cancer (historical)
Patients with A “mutation,” or harmful genetic change, in either BRCA1 or BRCA2 gives a woman an increased lifetime risk of developing breast and ovarian cancers.
Active patients on July 1st, 2024: 391000.
OncoLifeS OncoLifeS Medical oncology/immunotherapy
OncoLifeS Medical oncology/immunotherapy
Oncology patients treated with immunotherapy.
OncoLifeS OncoLifeS Hepato Pancreato Biliary Tumors
OncoLifeS Hepato Pancreato Biliary Tumors
Patients with tumors in the liver, pancreas, gallbladder and bile ducts.
Registratie Netwerk Groningen (RNG), predecessor of AHON, with information from 3 GP practices collected during 1998 - 2013.
OncoLifeS OncoLifeS Esophageal and Stomach Carcinoma
OncoLifeS Esophageal and Stomach Carcinoma
Patients with oesophageal, gastric cancer and Barrett's esophagus.
OncoLifeS OncoLifeS Neuro-endocrine oncology
Oncology patients with a neuroendocrine (NET) tumor.
Patients with germ cell tumors (seminoma and non-seminoma)) of the testis
OncoLifeS OncoLifeS Gynecological oncology
Tumors of the female reproductive system, such as ovarian cancer, vaginal cancer, cervical cancer, vulva cancer.
OncoLifeS OncoLifeS Adolescents and young adults (AYA)
OncoLifeS Adolescents and young adults (AYA)
Adolescents and young adults (AYAs) diagnosed with cancer between the ages of 18 and 39.
OncoLifeS OncoLifeS Anaplastic thyroid carcinoma
Patients with an Anaplastic thyroid carcinoma (ATC).
The type of cancer in the cortex of the adrenal gland.
OncoLifeS OncoLifeS Bone and soft-tissue oncology
Bone and Soft Tissue Tumors, such as sarcomas and Gastro Intestinale Stroma Tumor (GIST).
OncoLifeS OncoLifeS Hematological oncology
Oncology patients with a hematologic tumor, such as lymph node cancer, plasma cell tumors, leukemia, myelodysplastic syndromes (MDS), myeloproliferative disorders (MPN), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL) and multiple myeloma (MM).
Patients with small intestine, colon, rectum and anal cancer and patients with a hereditary predisposition to colorectal cancer.
Patients with a squamous cell carcinoma diagnosis
OncoLifeS OncoLifeS Hereditary Breast and Ovarian Cancer
OncoLifeS Hereditary Breast and Ovarian Cancer
Include cancers in the larynx, throat, lips, mouth, nose and salivary glands.
Patients that arrive through the emergency department with where medical data will be available for reuse, and extra data and biomaterials will be collected for future research.
Referred to ED to internal medicine, nephrology, urology, geriatric medicine, oncology, rheumatology, hematology, pulmonary medicine, MDL, or emergency medicine department + one or more of the following criteria: triage color red or orange; anaphylaxis; (early) sepsis; diabetic ketoacidosis or hyperglycemic hyperosmolar syndrome; exacerbation asthma or COPD; intoxication; systolic blood pressure < 100 mmHg; respiratory rate > 22/min; GCS score <15 and/or delirium
Patients that arrive through the emergency department where medical data will be available for reuse
Patients that arrive through the emergency department with where medical data will be available for reuse and extra will be collected for future research.
Referred to ED to internal medicine, nephrology, urology, geriatric medicine, oncology, rheumatology, hematology, pulmonary medicine, MDL, or emergency medicine department + one or more of the following criteria: triage color yellow combined with transport by ambulance or helicopter (urgency A1 or A2); syncope.
UMCG Cohort and Biobank Coordination Hub
EGAS00001006999 Lifelines Data Management
EGAS00001005027 Lifelines Data Management
EGAS00001006258 TransplantLines investigators
Lifelines NEXT: a prospective birth cohort adding the next generation to the three‑generation Lifelines cohort study
AHON https://doi.org/10.1093/ije/dyae021
Data Resource Profile: Registry of electronic health records of general practices in the north of The Netherlands (AHON)
Mortality in patients with Dupuytren’s disease in the first 5 years after diagnosis: a population-based survival analysis
A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study
AHON https://doi.org/10.1016/j.jpsychores.2024.111859
Which patients are at risk of developing symptom diagnoses that persist for more than a year in primary care? Development and external validation of a prediction model
Artificial Intelligence for early detection of lung cancer in General Practitioners’ clinical notes
Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study
Anti-vertigo drug prescribing for patients with vestibular symptoms in primary care
The OncoLifeS data-biobank for oncology: a comprehensive repository of clinical data, biological samples, and the patient's perspective
WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene.
RNA-Seq in 296 phased trios provides a high-resolution map of genomic imprinting.
A SNP panel for identification of DNA and RNA specimens.
A high-quality human reference panel reveals the complexity and distribution of genomic structural variants.
Transmission of human mtDNA heteroplasmy in the Genome of the Netherlands families: support for a variable-size bottleneck.
Genome-wide patterns and properties of de novo mutations in humans.
Characteristics of de novo structural changes in the human genome.
Whole-genome sequence variation, population structure and demographic history of the Dutch population.
Improved imputation quality of low-frequency and rare variants in European samples using the 'Genome of The Netherlands'.
The Genome of the Netherlands: design, and project goals.
The genomic landscape of reference genomes of cultivated human gut bacteria.
Systemic antibody responses against gut microbiota flagellins implicate shared and divergent immune reactivity in Crohn's disease and chronic fatigue syndrome.
The emerging landscape of dynamic DNA methylation in early childhood.
Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data.
Hierarchical progressive learning of cell identities in single-cell data.
Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching.
Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution.
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.
Smoking increases expression of the SARS-CoV-2 spike protein-binding long ACE2 isoform in bronchial epithelium.
Gene signatures from scRNA-seq accurately quantify mast cells in biopsies in asthma.
Genetic regulation of gene expression of MIF family members in lung tissue.
Systematic Prioritization of Candidate Genes in Disease Loci Identifies TRAFD1 as a Master Regulator of IFNγ Signaling in Celiac Disease.
Immunochip meta-analysis in European and Argentinian populations identifies two novel genetic loci associated with celiac disease.
Epigenome-wide association study identifies DNA methylation markers for asthma remission in whole blood and nasal epithelium.
Host genetic regulation of human gut microbial structural variation.
Impact of occupational pesticide exposure on the human gut microbiome.
Gut dysbiosis associates with cytokine production capacity in viral-suppressed people living with HIV.
Omics approaches in asthma research: Challenges and opportunities.
Genetic, parental and lifestyle factors influence telomere length.
Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure.
Characterizing the allele-specific gene expression landscape in high hyperdiploid acute lymphoblastic leukemia with BASE.
Clonal origin and development of high hyperdiploidy in childhood acute lymphoblastic leukaemia.
Airway epithelial cell response to RSV is mostly impaired in goblet and multiciliated cells in asthma.
Lifelines NEXT: a prospective birth cohort adding the next generation to the three-generation Lifelines cohort study.
Neoadjuvant immune checkpoint blockade in women with mismatch repair deficient endometrial cancer: a phase I study.
LIFELINES_NEXT LIFELINES_NEXT LIFELINES_NEXT Partners Adult (18+ years)
LIFELINES_NEXT LIFELINES_NEXT LIFELINES_NEXT Children Infant (0-23 months)
LIFELINES_NEXT LIFELINES_NEXT LIFELINES_NEXT Mothers Adult (18+ years)
AHON AHON AHON General population All ages
AHON AHON AHON General population Adult (18+ years)
AHON AHON AHON General population Aged (65+ years)
OncoLifeS OncoLifeS OncoLifeS Hepato Pancreato Biliary Tumors Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Hematological oncology Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Testicular oncology Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Medical oncology/immunotherapy Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Skin cancer Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Neuro-endocrine oncology Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Neuro-oncology Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Colorectal cancer Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Adolescents and young adults (AYA) Adult (25-44 years)
OncoLifeS OncoLifeS OncoLifeS Adolescents and young adults (AYA) Young adult (18-24 years)
OncoLifeS OncoLifeS OncoLifeS Esophageal and Stomach Carcinoma Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Adrenal cancer Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Lung oncology Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Head&Neck cancer Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Hereditary Breast and Ovarian Cancer Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Hereditary Breast and Ovarian Cancer (historical) Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Bone and soft-tissue oncology Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Anaplastic thyroid carcinoma Adult (18+ years)
OncoLifeS OncoLifeS OncoLifeS Gynecological oncology Adult (18+ years)
Data collection at gestational age of 18 weeks
Reproductive Origins of Adult Health and Disease (ROAHD)
EGAS00001002169 EGAS00001002169 MeDALL epigenetics data
EGAS00001002560 EGAS00001002560 LLDeep DAG2+: scRNA-seq in PBMCs
EGAS00001002560 LLDeep DAG2+: scRNA-seq in PBMCs
Single cell transcriptomics of PBMCs of 47 donors from the Lifelines Deep cohort (general population, Northern part of the Netherlands). Cells of five or six different donors were pooled together in one sample pool, resulting in eight different sample pools. In total, 28.855 cells were captured and their transcriptomes were sequenced to an average depth of 74k. Genotype data was available for each donor, which allowed us to use the Demuxlet method that uses variable SNPs between the pooled individuals to determine which cell belongs to which individual. Since genotype information is lacking of 2 individuals, the transcriptome of only 45 individuals could be retrieved.
EGAS00001002560 EGAS00001002560 LLDeep DAG2+ scRNA-seq in PBMCs
EGAS00001002560 LLDeep DAG2+ scRNA-seq in PBMCs
Data collection at infant's age of 2 months
Data collection at infant's age of 6 months
EGAS00001003735 EGAS00001003735 Gene expression by RNAseq in bronchial biopsies of asthmatics, asthma in remission and healthy subjects
EGAS00001003735 Gene expression by RNAseq in bronchial biopsies of asthmatics, asthma in remission and healthy subjects
The study focus was differential expression in bronchial biopsies between persistent asthma, asthma in remission and healthy controls using RNAseq. There were 184 samples that passed QC. RNA samples were processed using the TruSeq Stranded Total RNA Sample Preparation Kit (Illumina, San Diego, CA), using an automated procedure in a Caliper Sciclone NGS Workstation (PerkinElmer, Waltham, MA). In this procedure, all cytoplasmic and mitochondria rRNA was removed (RiboZero Gold kit). The obtained cDNA fragment libraries were loaded in pools of multiple samples unto an Illumina HiSeq2500 sequencer using default parameters for paired-end sequencing (2 × 100 bp). Data are available as 221 pairs of FASTQ-files. Note that several samples are associated with multiple sequence runs.
EGAS00001003739 1506_Methylation_M.vd.Berge
Infinium HumanMethylation450 BeadChip array (450k array)
EGAS00001004766 EGAS00001004766 Epigenome-wide association study of asthma remission in whole blood and nasal epithelium
EGAS00001004766 Epigenome-wide association study of asthma remission in whole blood and nasal epithelium
Clinical remission (ClinR) was defined as the absence of asthma symptoms and medication for at least 12 months, and complete remission (ComR) was defined as ClinR with normal lung function and absence of airway hyperresponsiveness. We analyzed differential DNA methylation of ClinR and ComR comparing to persistent asthma (PersA) in whole blood samples (n=72) and nasal brushing samples (n=97) in a longitudinal cohort of well characterized asthma patients.
EGAS00001000644 EGAS00001000644 GoNL release 5 raw SNV calls
EGAS00001000644 GoNL release 5 raw SNV calls
These files contain a total of 20.4M SNVs and the complete information output by the GATK UnifiedGenotyper v1.4 on all 767 GoNL samples. These calls are not trio-aware and all genotypes were reported regardless of their quality. Both filtered and passing calls are reported in these files. Filtered calls include (1) calls failing our VQSR threshold and (2) calls in the GoNL inaccessible genome.
EGAS00001000644 EGAS00001000644 GoNL release 5 haplotype panel
EGAS00001000644 GoNL release 5 haplotype panel
The samples in this panel come from 250 families: 248 parents-child trios and 2 parent-child duos. As the children do not provide additional haplotypes or population information, they were excluded from the panel. The samples present in the release are composed of 248 couples, 2 single individuals and 1 sample composed from the 2 haplotypes from the duo's children transmitted by their missing parent. The composed sample is named gonl-220c_223c.The files contain a total of 18.9M SNVs and 1.1M INDELs in autosomal chromosomes. They were generated by phasing/imputing the SNVs (a) and INDELs (b) using MVNCall. Only sites passing filters are reported. Sites filtered as part of the GoNL inaccessible genome were kept (but flagged as filtered) and still may contain true positive calls but should be used with care as they are located in parts of the genome that are less well captured (systematic under or over-covered or low-mapping quality)
EGAS00001000644 EGAS00001000644 GoNL aligned sequence data in BAM format.
EGAS00001000644 GoNL aligned sequence data in BAM format.
We mapped the data to the UCSC human reference genome build 37 using BWA 0.5.9-r16. We first mapped each read pair separately using bwa aln. Then we used bwa sampe to map the paired reads together to a BAM9 file. The BAM file was then sorted by genomic position and indexed using PicardTools-1.32 SortSam. To prevent PCR artifacts from influencing the downstream analysis of our data, we used Picard to mark the duplicate reads, which were ignored in downstream analysis. We used GATK IndelRealigner on our data around known indels (from 1KG Pilot). The IndelRealigner creates all possible read alignments using the source and computes the likelihood of the data containing the indel based on the read pileup. Whenever the maximum likelihood contains an indel, the reads are realigned accordingly. Each base is associated with a phred-scaled base quality score. Calibration of Phred scores is crucial as they are used in some of the downstream analysis models. We used GATK to recalibrate the base qualities with respect to (i) the base cycle, (ii) original quality score, and (iii) dinucleotide context. To minimize issues stemming from mapping problems around indels, we decided to undergo a second round of indel realignment using the GATK IndelRealigner by family rather than by individual. For this second round, we considered two sources of possible indels: 1KG Phase 1 indels and indels aligned by BWA in the GoNL data.
EGAS00001000644 EGAS00001000644 Set of Structural Variants and Indels for GoNL samples
EGAS00001000644 Set of Structural Variants and Indels for GoNL samples
These files contain indels and structural variants on 769 GoNL samples (SV release 6, 2016-05-25).
Association results from the Dutch cohort
Association results fromthe Agentinian cohort two
EGAS00001003805 EGAS00001003805 CeD_UnitedKingdom
Association results from the Agentinian cohort one
AcuteLines AcuteLines Follow-up (2 years)
AcuteLines AcuteLines Follow-up (3 monthts)
Association results from the Irish cohort
Results of the celiac diease meta-analysis
Association results from the Italian cohort
LLD PhIP-Seq reads. Oligopeptides were designed at Eran's Segal group at Weizmann Institute of Science. Dataset include 1,783 plasma samples. In 340 participants, a second time point was taken after a 4-year follow-up.
Data collection at gestational age of 32 weeks
Preventie en Incidentie van Astma en Mijt Allergie (PIAMA)
EGAS00001005027 EGAS00001005027 The Dutch Microbiome Project Batch 1
EGAS00001005027 The Dutch Microbiome Project Batch 1
The Dutch Microbiome Project (DMP) data includes shotgun metagenomic sequencing of faecal samples 8,208 Dutch individuals. Paired-end sequencing was performed using Illumina HiSeq 2000 platform. Data is archived in two batches to facilitate easier data access and upload to EGA. Batch 1 of DMP includes 4396 samples.
AcuteLines AcuteLines Follow-up (12 months)
AcuteLines AcuteLines Follow-up (5 years)
EGAS00001000644 EGAS00001000644 GoNL raw sequence data in fastq format
EGAS00001000644 GoNL raw sequence data in fastq format
Raw sequencing data for all samples in fastq format.
AcuteLines AcuteLines Follow-up (lifelong)
Data collection at infant's age of 12 months
LIFELINES_NEXT LIFELINES_NEXT LLNEXT_Partners
Data collection at infant's age of 11 months
Data collection at infant's age of 1 month
Data collection at gestational age of 28 weeks
Data collection at infant's age of 5 months
Data collection at infant's age of 3 months
Data collection at infant's age of 2 years
Data collection at infant's age of 2.5 years
Data collection at infant's age of 3 years
Data collection at infant's age of 4 years
Data collection at infant's age of 5 years
Data collection at infant's age of 6 years
Data collection at infant's age of 9 months
Data collection at gestational age of 12 weeks
Data collection at infant's age of 4 months
EGAS00001005027 EGAS00001005027 The Dutch Microbiome Project Batch 2
EGAS00001005027 The Dutch Microbiome Project Batch 2
The Dutch Microbiome Project (DMP) data includes shotgun metagenomic sequencing of faecal samples 8,208 Dutch individuals. Paired-end sequencing was performed using Illumina HiSeq 2000 platform. Data is archived in two batches to facilitate easier data access and upload to EGA. Batch 2 of DMP includes ~400 samples.
EGAS00001005189 EGAS00001005189 PIAMA DNA methylation dataset
EGAS00001005189 PIAMA DNA methylation dataset
EGAS00001005225 EGAS00001005225 GFD viral enrichment sequencing
EGAS00001005225 GFD viral enrichment sequencing
sequencing of libraires enriched for viral particles
EGAS00001005225 EGAS00001005225 GFD metagenomic sequencing
EGAS00001005225 GFD metagenomic sequencing
Sequencing of fecal metagenomic libraries
Here we present the 1M-scBloodNL study, in which we performed single-cell RNA-seq on 120 individuals of the Northern Netherlands population cohort Lifelines. For each individual peripheral blood mononuclear cells (PBMCs) were sequenced in an unstimulated condition, and after 3 and 24 hour in vitro stimulation with C. albicans (CA), M. tuberculosis (MTB) and P. aeruginosa (PA), totalling approximately 1.3 million cells. scRNA-seq was conducted with the 10X Genomics 3'-end v2 (72 libraries) and v3 (33 libraries) technology. In general, each library contains PBMCs from 8 donors and 2 different stimulation-timepoint combinations. Donors were demultiplexed using a combination of SoupOrCell (https://www.nature.com/articles/s41592-020-0820-1) and genotype information to assign the correct donor to a donor-specific cell cluster.
EGAS00001006258 EGAS00001006258 TransplantLines Gut Microbiome study
EGAS00001006258 TransplantLines Gut Microbiome study
The TransplantLines Gut Microbiome study includes raw data generated by shotgun metagenomic sequencing of fecal samples of solid organ transplant recipients and basic phenotypes (age and sex, BMI).
EGAS00001006347 EGAS00001006347 High hyperdiploid ALL single cell whole genome sequencing
EGAS00001006347 High hyperdiploid ALL single cell whole genome sequencing
This dataset was collected from viable bone marrow cells obtained at diagnosis from nine patients with high hyperdiploid ALL and one normal bone marrow sample. All samples were subjected to low pass single cell whole genome sequencing with the median sequencing coverage of 0.02x. Single nuclei in G0/G1 phase were isolated using a fluorescence-activated cell sorting (FACS) cytometer. DNA libraries were constructed and associated next-generation sequencing was carried out by European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Further details regarding the DNA libraries construction are available by Bos et. al., 2019 (https://link.springer.com/protocol/10.1007/978-1-4939-8931-7_15).
scRNAseq dataset containing 5 healthy donors and 4 asthmatic donors.
EGAS00001007021 EGAS00001007021 Single-cell dissection of the immune response after a myocardial infarction
EGAS00001007021 Single-cell dissection of the immune response after a myocardial infarction
38 STEMI patients at hospital admission, 24 hours (acute phase) and 6-8 weeks (chronic phase) after STEMI
PhIP-Seq experiment conducted in Eran Sigal's group at WIS.
EGAS00001007450 EGAS00001007450 scRNAseq data from ALI cultures of healthy donors and patients with asthma, infected with RSV
EGAS00001007450 scRNAseq data from ALI cultures of healthy donors and patients with asthma, infected with RSV
This dataset contains the count matrices and corresponding metadata for our study on bronchial epithelial cells response to RSV in healthy and in asthma. This scRNAseq data is from primary cells, that have been differentiated in ALI cultures and infected with RSV.
EGAS00001007571 EGAS00001007571 CS Baby Biome gut microbiome
EGAS00001007571 CS Baby Biome gut microbiome
This dataset contains metagenomic sequencing of stool samples of babies from CS Baby Biome project, and their mothers
EGAS50000000133 EGAS50000000133 Lifelines NEXT 16S v3-v4 amplicon sequencing
EGAS50000000133 Lifelines NEXT 16S v3-v4 amplicon sequencing
16S v3-v4 amplicon sequencing of milk, fecal and oral cavity samples, along with sequencing controls, of a subset of newborn infants from Lifelines NEXT cohort
EGAS50000000133 EGAS50000000133 Lifelines NEXT 16S-ITS-23S long read sequencing
EGAS50000000133 Lifelines NEXT 16S-ITS-23S long read sequencing
16S-ITS-23S amplicon long read PacBio sequencing of milk, fecal and oral cavity samples, along with sequencing controls, of a subset of newborn infants from Lifelines NEXT cohort
16S-ITS-23S amplicon long read PacBio sequencing
EGAS50000000133 EGAS50000000133 HMO-microbiome study dataset, 16S sequencing
EGAS50000000133 HMO-microbiome study dataset, 16S sequencing
This dataset contains 16S sequencing of fecal and milk samples for HMO-microbiome Lifelines NEXT study
EGAS50000000133 EGAS50000000133 Lifelines NEXT HMO Data
The dataset contains HMO measurements for 1542 samples from Lifelines NEXT cohort
EGAS50000000483 EGAS50000000483 Patient TSO500 VCF files
EGAS50000000483 Patient TSO500 VCF files
Tumours from MMRd EC patients treated with 2 cycles neoadjuvant ICI. TruSight Oncology 500 (TSO 500) panel for all 10 tumour samples from patients included in the trial.
Data collection at infant's age of 3.5 years
Data collection at infant's age of 10 months
Data collection at infant's age of 2 weeks
Data collection at infant's age of 4.5 years
Data collection at infant's age of 5.5 years
OncoLifeS OncoLifeS Follow-up 6 - 12- 18- 24 months
OncoLifeS Follow-up 6 - 12- 18- 24 months
Brochure with information on Lifelines NEXT that is distibuted to pregnant women who show interest in the study.
LIFELINES_NEXT Overview questionnaires Lifelines NEXT
Overview of subject in Lifelines NEXT questionnaires per time point. Orange lines are questionnaires that have been added to the study at a later time point upon request of collaborators.
AHON Factsheet AHON-database - Information for researchers
Factsheet AHON-database - Information for researchers
OncoLifeS OncoLifeS Template Study Protocol
An infographic about the data and samples Acutelines collects