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Motivation Each year, the number of published bulk and single-cell RNA-seq datasets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene’s transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell-type identification. Results We present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq datasets, and the first package to conduct a ‘classical’ DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. In addition, we present novel potential DTU applications like the identification of cell-type specific transcript isoforms as biomarkers. Availability and implementation The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/. Supplementary information Supplementary data are available at Bioinformatics online.

BackgroundMedulloblastoma (MB) is a malignant brain tumor in childhood. It comprises 4 subgroups with different clinical behaviors. The aim of this study was to characterize the transcriptomic landscape of MB, both at the level of individual tumors as well as in large patient cohorts.MethodsWe used a combination of single-cell transcriptomics, cell culture models and biophysical methods such as nanoparticle tracking analysis and electron microscopy to investigate intercellular communication in the MB tumor niche.ResultsTumor cells of the sonic hedgehog (SHH)–MB subgroup show a differentiation blockade. These cells undergo extensive metabolic reprogramming. The gene expression profiles of individual tumor cells show a partial convergence with those of tumor-associated glial and immune cells. One possible cause is the transfer of extracellular vesicles (EVs) between cells in the tumor niche. We were able to detect EVs in co-culture models of MB tumor cells and oligodendrocytes. We also identified a gene expression signature, EVS, which shows overlap with the proteome profile of large oncosomes from prostate cancer cells. This signature is also present in MB patient samples. A high EVS expression is one common characteristic of tumors that occur in high-risk patients from different MB subgroups or subtypes.ConclusionsWith EVS, our study uncovered a novel gene expression signature that has a high prognostic significance across MB subgroups.

STUDY QUESTION How can one design and implement a system that provides a comprehensive overview of research results in the field of epi-/genetics of male infertility and germ cells? SUMMARY ANSWER Working at the interface of literature search engines and raw data repositories, the newly developed Male Fertility Gene Atlas (MFGA) provides a system that can represent aggregated results from scientific publications in a standardized way and perform advanced searches, for example based on the conditions (phenotypes) and genes related to male infertility. WHAT IS KNOWN ALREADY PubMed and Google Scholar are established search engines for research literature. Additionally, repositories like Gene Expression Omnibus and Sequence Read Archive provide access to raw data. Selected processed data can be accessed by visualization tools like the ReproGenomics Viewer. STUDY DESIGN, SIZE, DURATION The MFGA was developed in a time frame of 18 months under a rapid prototyping approach. PARTICIPANTS/MATERIALS, SETTING, METHODS In the context of the Clinical Research Unit ‘Male Germ Cells’ (CRU326), a group of around 50 domain experts in the fields of male infertility and germ cells helped to develop the requirements engineering and feedback loops. They provided a set of 39 representative and heterogeneous publications to establish a basis for the system requirements. MAIN RESULTS AND THE ROLE OF CHANCE The MFGA is freely available online at https://mfga.uni-muenster.de. To date, it contains 115 data sets corresponding to 54 manually curated publications and provides an advanced search function based on study conditions, meta-information and genes, whereby it returns the publications’ exact tables and figures that fit the search request as well as a list of the most frequently investigated genes in the result set. Currently, study data for 31 different tissue types, 32 different cell types and 20 conditions are available. Also, ∼8000 and ∼1000 distinct genes have been found to be mentioned in at least 10 and 15 of the publications, respectively. LARGE SCALE DATA Not applicable because no novel data were produced. LIMITATIONS, REASONS FOR CAUTION For the most part, the content of the system currently includes the selected publications from the development process. However, a structured process for the prospective literature search and inclusion into the MFGA has been defined and is currently implemented. WIDER IMPLICATIONS OF THE FINDINGS The technical implementation of the MFGA allows for accommodating a wide range of heterogeneous data from aggregated research results. This implementation can be transferred to other diseases to establish comparable systems and generally support research in the medical field. STUDY FUNDING/COMPETING INTEREST(S) This work was carried out within the frame of the German Research Foundation (DFG) Clinical Research Unit ‘Male Germ Cells: from Genes to Function’ (CRU326). The authors declare no conflicts of interest.

Study question Which single nucleotide polymorphisms (SNPs) are associated with Follicle stimulating hormone (FSH) levels in men with unexplained infertility and can affect FSH action and spermatogenesis? Summary answer We identified a genomic region at chromosome 11p.14.1, including nine SNPs, that are significantly associated with FSH levels in men with unexplained infertility. What is known already FSH action is essential for the initiation and maintenance of human spermatogenesis. One well-studied SNP, FSHB c.-211G>T (rs10835638), is associated with FSHB mRNA transcription and directly affects FSH serum levels, testicular volume and spermatogenesis. Carriers of a T-allele in this promoter are diagnosed with functional secondary hypogonadism with isolated FSH deficiency. Other genetic variants, for example at the FSHR have been shown to slightly modulate FSH action, however the clinical impact in these variants seems to be low. The so far identified FSH-associated SNPs revealed an impact of up to 2.3 % on FSH serum level variance. Study design, size, duration A Genome wide association study (GWAS) was performed on a clinically well characterized cohort of 742 men with unexplained infertility (discovery study). Of the nine identified SNPs, validation was performed for rs11031005 and the already described rs10835638 in an independent cohort of 1123 men with unexplained infertility (validation study). Participants/materials, setting, methods Patients were retrospectively selected from our CeRA database Androbase® applying strict selection criteria; DNA was isolated from stored EDTA-blood samples. Informative genetic variants were identified using Illumina PsychArray v1.3. Illumina®GenomeStudio v2.0, PLINK v1.90 and R 3.6.3 were used to perform quantitative association analysis based on normalized FSH values. The validation study was performed using TaqMan PCR for SNP detection and R 3.6.3 for quantitative association to analyze the impact of each SNP on FSH level. Main results and the role of chance Imputation of the GWAS data revealed 94 SNPs with suggestive significance (p This the first GWAS in men with unexplained infertility. This study shows that not one single SNP, but rather a genomic region has an impact on FSH serum level in men with unexplained male infertility. This effect is even more pronounced in the more severe phenotype of oligozoospermic men. Limitations, reasons for caution The study is restricted to men with unexplained infertility, which might cause a selection bias. Validation and functional evaluation of the eight newly identified SNPs in independent cohorts would emphasize the results more. The sample size of 742 limits detection of loci with smaller effect on FSH levels. Wider implications of the findings The determination of one of the nine SNPs can improve diagnostic precision in identifying men with secondary functional hypogonadism with isolated FSH deficiency. An oligozoospermic subgroup of these men would putatively benefit from FSH treatment and has to be proven in randomized controlled trials. Trial registration number German Research Foundation CRU326

Study question Do DNA methylation changes occur in testicular germ cells (TGCs) from patients with impaired spermatogenesis? Summary answer TGCs from men with cryptozoospermia exhibit altered DNA methylation levels at several genomic regions, many of which are associated with genes involved in spermatogenesis. What is known already In the last 15 years, several studies have described DNA methylation changes in sperm of infertile men. More recently, using whole genome bisulfite sequencing (WGBS) we were able to refute these findings by demonstrating that somatic DNA contamination and genetic variation confound methylation studies in swim-up purified sperm of severely oligozoospermic men. However, it remains unknown whether altered DNA methylation plays a role during the development of the germ cells in the testes of these patients. Study design, size, duration For identifying DNA methylation differences associated with impaired spermatogenesis, we compared the TGC methylomes of men with cryptozoospermia (CZ) and men with obstructive azoospermia (n = 4 each), who had normal spermatogenesis and served as controls (CTR). Study participants were selected among an age-matched cohort of 24 CTR and 10 CZ. The selection was based on similar composition of the TGC suspension evaluated by ploidy analysis and absence of somatic DNA. Participants/materials, setting, methods TGCs were isolated from biopsies after short-term cell culture. Presence of somatic DNA was evaluated by analyzing the DNA methylation levels of H19, MEST, DDX4 and XIST. WGBS was performed at ∼14× coverage. Bioinformatic tools were used to compare global DNA methylation levels, identify differentially methylated regions (DMRs) and functionally annotate the DMRs. Single-cell RNA sequencing (scRNA-seq) was used to associate the DNA methylation changes to gene expression. Main results and the role of chance We could not identify any difference in the global DNA methylation level or at imprinted regions between CZ and CTR samples. However, using stringent filters to identify group-specific methylation differences, we detected 271 DMRs, 238 of which were hypermethylated in CZ (binominal test, p Limitations, reasons for caution The small sample size constitutes a limitation of this study. Furthermore, even though the cellular composition of samples was similar by ploidy analysis, we cannot rule out that the observed DNA methylation changes might be due to differences in the relative proportion of different germ cell types. Wider implications of the findings: Impaired spermatogenesis is associated with DNA methylation changes in testicular germ cells at functionally relevant regions of the genome, which points to an important role of DNA methylation in normal spermatogenesis. The DNA methylation changes may contribute to premature abortion of spermatogenesis and therefore not appear in mature sperm. Trial registration number N/A

Motivation With its capacity for high-resolution data output in one region of interest, chromosome conformation capture combined with high-throughput sequencing (4C-seq) is a state-of-the-art next-generation sequencing technique that provides epigenetic insights, and regularly advances current medical research. However, 4C-seq data are complex and prone to biases, and while specialized programs exist, an unbiased, extensive benchmarking is still lacking. Furthermore, neither substantial datasets with fully characterized ground truth, nor simulation programs for realistic 4C-seq data have been published. Results We conducted a benchmarking study on 66 4C-seq samples from 20 datasets, and developed a novel 4C-seq simulation software, Basic4CSim, to allow for detailed comparisons of 4C-seq algorithms on 50 simulated datasets with 10–120 samples each. Simulations and benchmarking were adapted to address different characteristics of 4C-seq data. Simulated data were compared with published samples to validate simulation settings. We identified differences between 4C-seq algorithms in terms of precision, recall, interaction structure, and run time, and observed general trends. Novel differential pipeline versions of single-sample based 4C-seq algorithms were included in the benchmarking. While no single tool was optimally suited for both near-cis and far-cis, and both single-sample and differential analyses, choosing a high-performing algorithm variant did improve results considerably. For near-cis scenarios, r3Cseq, peakC and FourCSeq offered high precision, while fourSig demonstrated high overall F1 scores in far-cis analyses. Finally, 4C-seq simulations may aid in the development of improved analysis algorithms. Availability and implementation Basic4CSim is available at https://github.com/walter–ca/Basic4CSim. Supplementary information Supplementary data are available at Bioinformatics online.

Rhabdoid tumors (RT) are embryonal neoplasms occurring most frequently in the central nervous system where they are termed atypical teratoid rhabdoid tumor (ATRT). A common hallmark of RT is homozygous loss of the BAF complex subunit SMARCB1. RT patients have a poor prognosis with an overall survival time of 17 months and >60% of patients suffer from relapses. The lack of an optimal treatment strategy could be attributed to the heterogeneity within and between different subgroups of ATRT. Despite the recent advancements in characterizing RT at a molecular level, the cellular origin of RT remains elusive. Thus, this study focused on the identification of the cellular origin of MYC-RT and underlying epigenetic deregulations which account for the cellular heterogeneity in these tumors. We showed that Smarcb1 abrogation in Sox2-positive progenitor cells at E6.5 give rise to RT of the MYC and SHH subgroup in genetically engineered mouse models (GEMM). To uncover distinct cells of origin (COO) for the SHH and MYC subgroups, unbiased computational approaches were used to compare single-cell transcriptomes of GEMMs with single-cell reference maps of murine early embryogenesis. While SHH tumors arise from mid/hindbrain progenitor cells, primordial germ cells (PGCs) emerge as COO of both intracranial and extracranial MYC tumors. PGCs as COO of MYC-RT were validated in vivo by using PGC-specific Smarcb1 knockout mouse model. We further characterized a deregulated transcriptome in MYC-RT compared to PGCs, which is sustained by a subset of epigenetically driven tumor cells. Deregulated expression of genes driving methylation/demethylation processes in MYC tumors and regression of these tumors upon treatment with decitabine in vitro and in vivo, indicates that DNA methylation plays a key role in cellular transformation and development of MYC-RT.

ObjectivesTo systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care.Materials and MethodsMedline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measured by using weighted kappa and solved by consensus. An example set of promising disease entities was generated based on medical effect scores and risk of bias assessment. To summarize technical characteristics of the systems, reported input variables and algorithm types were extracted as well.ResultsSeventy studies were included. Five (7%) reported reduced mortality, 16 (23%) reduced life-threatening events, and 28 (40%) reduced non–life-threatening events, 20 (29%) had no significant impact on patient outcomes, and 1 showed a negative effect (weighted κ: 0.72, P Discussion and ConclusionMost of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized.Registration number on PROSPERO: CRD42016049946.

Atypical teratoid/rhabdoid tumors (ATRT) are pediatric brain neoplasms that are known for their heterogeneity concerning pathophysiology and outcome. The three genetically rather uniform but epigenetically distinct molecular subgroups of ATRT alone do not sufficiently explain the clinical heterogeneity. Therefore, we examined the tumor microenvironment (TME) in the context of tumor diversity. By using multiplex-immunofluorescent staining and single-cell RNA sequencing (scRNA-seq) we unveiled the pan-macrophage marker CD68 as a subgroup-independent negative prognostic marker for survival of ATRT patients. ScRNA-seq analysis of murine ATRT-SHH, ATRT-MYC and extracranial RT (eRT) provide a delineation of the TME, which is predominantly infiltrated by myeloid cells: more specifically a microglia-enriched niche in ATRT-SHH and a bone marrow-derived macrophage infiltration in ATRT-MYC and eRT. Exploring the cell-cell communication of tumor cells with tumor-associated immune cells, we found that Cd68+ tumor-associated macrophages (TAMs) are central to intercellular communication with tumor cells. Moreover, we uncovered distinct tumor phenotypes in murine ATRT-MYC that share genetic traits with TAMs. These intermediary cells considerably increase the intratumoral heterogeneity of ATRT-MYC tumors. In vitro co-culture experiments recapitulated the capability of ATRT-MYC cells to interchange cell material with macrophages extensively, in contrast to ATRT-SHH cells. We found that microglia are less involved in the exchange of information with ATRT cells and that direct contact is a prerequisite for incorporation. A relapse xenograft model implied that intermediary cells are involved in the acquisition of chemotherapy resistance. We show evidence that TAM-tumor cell interaction is one mechanism of chemotherapy resistance and relapse in ATRT.

Rhabdoid tumors (RT) are rare but highly aggressive pediatric neoplasms. These tumors carry homozygous loss-of-function alterations of SMARCB1 in almost all cases with an otherwise low mutational load. RT arise at different intracranial (ATRT) as well as extracranial (MRT) anatomical sites. Three main molecular subgroups (ATRT-SHH, ATRT-TYR, ATRT-MYC) have been characterized for ATRT which are epigenetically and clinically diverse, while MRT show remarkable similarities with ATRT-MYC distinct from ATRT-SHH and ATRT-TYR. Even though there are hypotheses about various cells of origin among RT subgroups, precursor cells of RT have not yet been identified. Previous studies on the temporal control of SMARCB1 knockout in genetically engineered mouse models have unveiled a tight vulnerable time frame during embryogenesis with regard to the susceptibility of precursor cells to result in RT. In this study, we employed single-cell RNA sequencing to describe the intra- and intertumoral heterogeneity of murine ATRT-SHH and -MYC as well as extracranial MYC tumor cells. We defined subgroup-specific tumor markers for all RT classes but also observed a notable overlap of gene expression patterns in all MYC subgroups. By comparing these single-cell transcriptomes with available single-cell maps of early embryogenesis, we gained first insights into the cellular origin of RT. Finally, unsupervised clustering of published human RT methylation data and healthy control tissues confirmed the existence of different cells of origin for intracranial SHH tumors and MYC tumors independent of their anatomical localizations.

Objective Electronic medical record (EMR) systems have the potential of supporting clinical work by providing the right information at the right time to the right people and thus make efficient use of resources. This is especially important in low-resource settings where reliable data are also needed to support public health and local supporting organizations. In this systematic literature review, our objectives are to identify and collect literature about success criteria of EMR implementations in low-resource settings and to summarize them into recommendations. Materials and Methods Our search strategy relied on PubMed queries and manual bibliography reviews. Studies were included if EMR implementations in low-resource settings were described. The extracted success criteria and measurements were summarized into 7 categories: ethical, financial, functionality, organizational, political, technical, and training. Results We collected 381 success criteria with 229 measurements from 47 articles out of 223 articles. Most papers were evaluations or lessons learned from African countries, published from 1999 to 2013. Almost half of the EMR systems served a specific disease area like human immunodeficiency virus (HIV). The majority of criteria that were reported dealt with the functionality, followed by organizational issues, and technical infrastructures. Sufficient training and skilled personnel were mentioned in roughly 10%. Political, ethical, and financial considerations did not play a predominant role. More evaluations based on reliable frameworks are needed. Conclusions Highly reliable data handling methods, human resources and effective project management, as well as technical architecture and infrastructure are all key factors for successful EMR implementation.

The mechanism for generating double minutes chromosomes (dmin) and homogeneously staining regions (hsr) in cancer is still poorly understood. Through an integrated approach combining nextgeneration sequencing, single nucleotide polymorphism array, fluorescent in situ hybridization and polymerase chain reaction-based techniques, we inferred the fine structure ofMYC-containing dmin/hsr amplicons harboring sequences from several different chromosomes in seven tumor cell lines, and characterized an unprecedented number of hsr insertion sites. Local chromosome shattering involving a single-step catastrophic event (chromothripsis) was recently proposed to explain clustered chromosomal rearrangements and genomic amplifications in cancer. Our bioinformatics analyses based on the listed criteria to define chromothripsis led us to exclude it as the driving force underlying amplicon genesis in our samples. Instead, the finding of coexisting heterogeneous amplicons, differing in their complexity and chromosome content, in cell lines derived from the same tumor indicated the occurrence of a multistep evolutionary process in the genesis of dmin/hsr. Our integrated approach allowed us to gather a complete view of the complex chromosome rearrangements occurring within MYC amplicons, suggesting that more than one model may be invoked to explain the origin of dmin/hsr in cancer. Finally, we identified PVT1 as a target of fusion events, confirming its role as breakpoint hotspot in MYC amplification. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Motivation: Bisulfite sequencing is currently the gold standard to obtain genome-wide DNA methylation profiles in eukaryotes. In contrast to the rapid development of appropriate pre-processing and alignment software, methods for analyzing the resulting methylation profiles are relatively limited so far. For instance, an appropriate pipeline to detect DNA methylation differences between cancer and control samples is still required. Results: We propose an algorithm that detects significantly differentially methylated regions in data obtained by targeted bisulfite sequencing approaches, such as reduced representation bisulfite sequencing. In a first step, this approach tests all target regions for methylation differences by taking spatial dependence into account. A false discovery rate procedure controls the expected proportion of incorrectly rejected regions. In a second step, the significant target regions are trimmed to the actually differentially methylated regions. This hierarchical procedure detects differentially methylated regions with increased power compared with existing methods. Availability: R/Bioconductor package BiSeq. Contact: katja.hebestreit@uni-muenster.de Supplementary information: Supplementary Data are available at Bioinformatics online.

A variety of genome-wide profiling techniques are available to probe complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher-level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we provide a comparison among various modeling procedures for integrating genome-wide profiling data of gene copy number and transcriptional alterations and highlight common approaches to genomic data integration. A transparent benchmarking procedure is introduced to quantitatively compare the cancer gene prioritization performance of the alternative methods. The benchmarking algorithms and data sets are available at http://intcomp.r-forge.r-project.org, PDF file including supplementary material. 9 pages. Preprint

Hospital Information Systems (HIS) handle a large number of different types of documents. Exchange and analysis of data from different HIS is facilitated by the use of standardized codes to identify document types. HL7's Clinical Document Architecture (CDA) uses LOINC (logical observation identifiers names and Codes) codes for clinical documents. The authors assessed the coverage of LOINC codes for document types in a German HIS. The authors analyzed document types that occurred more than 10 times in approximately 1.3 million documents in a commercial HIS at a major German University Hospital. Document types were mapped manually to LOINC using the Regenstrief LOINC Mapping Assistant (RELMA). Each document type was coded by two physicians. In case of discrepancies a third expert was consulted to reach consensus. For 76 of 86 document categories a LOINC code was identified, but for 38 of these categories, the LOINC code was not specific as deemed necessary. More than 93% of our local HIS documents had local document types that could be assigned a LOINC code.

Motivation: Unsupervised clustering of microarray data may detect potentially important, but not obvious characteristics of samples, for instance subgroups of diagnoses with distinct gene profiles or systematic errors in experimentation. Results: Multidimensional clustering (mdclust) is a method, which identifies sets of sample clusters and associated genes. It applies iteratively two-means clustering and score-based gene selection. For any phenotype variable best matching sets of clusters can be selected. This provides a method to identify gene–phenotype associations, suited even for settings with a large number of phenotype variables. An optional model based discriminant step may reduce further the number of selected genes. Availability: R-code and supplemental information available from http://martin-dugas.de/mdclust/ Supplementary information: http://martin-dugas.de/mdclust/

AIMS Catheter ablation of the inferior vena cava-tricuspid annulus isthmus and continuation of antiarrhythmic drug therapy have been shown to be an effective hybrid therapy for atrial flutter which results from antiarrhythmic drug treatment of atrial fibrillation. The aim of this study was to determine the risk factors for recurrence of atrial fibrillation in patients undergoing hybrid therapy for antiarrhythmic drug-induced atrial flutter. METHODS AND RESULTS 90 patients with paroxysmal (n=46) or persistent atrial fibrillation (n=44) developed atrial flutter due to the administration of amiodarone (n=48), flecainide (n=22), propafenone (n=14) or sotalol (n=6). Recurrence of atrial fibrillation after ablation was assessed during follow-up on continued antiarrhythmic drug therapy and during long-term follow-up, irrespective of the initial antiarrhythmic medication. During the follow-up on continued antiarrhythmic drug therapy (16+/-13 months), recurrence of atrial fibrillation was documented in 24 of 90 patients (27%). The presence of accompanying pre-ablation episodes of atrial fibrillation on antiarrhythmic treatment (Odds ratio 7.1, 95% confidence interval 2.3 to 25, p=0.001) and decreased left ventricular ejection fraction (Odds ratio 3.7, 95% confidence interval 1.01 to 12.5, p=0.048) were significant and independent predictors of post-ablation atrial fibrillation. Antiarrhythmic medication was discontinued during long-term follow-up due to adverse drug effects (amiodarone, n=12; flecainide, n=1) in 13 patients (14%). During the long-term follow-up, irrespective of the initial antiarrhythmic medication (21+/-15 months), stable sinus rhythm was maintained in 60 of 90 patients (67%). CONCLUSION Hybrid therapy can be considered as the first line therapy for patients with antiarrhythmic drug-induced atrial flutter but patients should be carefully evaluated for accompanying pre-ablation episodes of atrial fibrillation and possible adverse drug effects before initiation of hybrid therapy.

Introduction: Information systems are a key success factor for medical research and healthcare. Currently, most of these systems apply heterogeneous and proprietary data models, which impede data exchange and integrated data analysis for scientific purposes. Due to the complexity of medical terminology, the overall number of medical data models is very high. At present, the vast majority of these models are not available to the scientific community. The objective of the Portal of Medical Data Models (MDM, https://medical-data-models.org) is to foster sharing of medical data models. Methods: MDM is a registered European information infrastructure. It provides a multilingual platform for exchange and discussion of data models in medicine, both for medical research and healthcare. The system is developed in collaboration with the University Library of Munster to ensure sustainability. A web front-end enables users to search, view, download and discuss data models. Eleven different export formats are available (ODM, PDF, CDA, CSV, MACRO-XML, REDCap, SQL, SPSS, ADL, R, XLSX). MDM contents were analysed with descriptive statistics. Results: MDM contains 4387 current versions of data models (in total 10 963 versions). 2475 of these models belong to oncology trials. The most common keyword (n = 3826) is ‘Clinical Trial’; most frequent diseases are breast cancer, leukemia, lung and colorectal neoplasms. Most common languages of data elements are English (n = 328 557) and German (n = 68 738). Semantic annotations (UMLS codes) are available for 108 412 data items, 2453 item groups and 35 361 code list items. Overall 335 087 UMLS codes are assigned with 21 847 unique codes. Few UMLS codes are used several thousand times, but there is a long tail of rarely used codes in the frequency distribution. Discussion: Expected benefits of the MDM portal are improved and accelerated design of medical data models by sharing best practice, more standardised data models with semantic annotation and better information exchange between information systems, in particular Electronic Data Capture (EDC) and Electronic Health Records (EHR) systems. Contents of the MDM portal need to be further expanded to reach broad coverage of all relevant medical domains. Database URL: https://medical-data-models.org