Germany's Approach for the Secondary Use of Federated Real-World Data in the German Portal for Medical Research Data.

In the light of big data driven clinical research, fair access to real world clinical health data enables evidence to improve patient care. Germany's healthcare system provides an abundant data resource but unique challenges due to its federated nature, heterogeneity and high data-protection standards. The Medical Informatics Initiative (MII) developed concepts that are being implemented in the German Portal for Medical Research Data (FDPG) to grant access to distributed data-sources across state borders.

[Foundations for the scientific use of extensive health care data in Germany-results of the Data Sharing working group of the Medical Informatics Initiative].

Healthcare data are an important resource in applied medical research. They are available multicentrically. However, it remains a challenge to enable standardized data exchange processes between federal states and their individual laws and regulations.

Towards a National Portal for Medical Research Data (FDPG): Vision, Status, and Lessons Learned.

Harmonizing medical data sharing frameworks is challenging. Data collection and formats follow local solutions in individual hospitals; thus, interoperability is not guaranteed. The German Medical Informatics Initiative (MII) aims to provide a Germany-wide, federated, large-scale data sharing network.

A framework to score the effects of structural variants in health and disease.

Although technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing.

HemoMIPs-Automated analysis and result reporting pipeline for targeted sequencing data.

Targeted sequencing of genomic regions is a cost- and time-efficient approach for screening patient cohorts. We present a fast and efficient workflow to analyze highly imbalanced, targeted next-generation sequencing data generated using molecular inversion probe (MIP) capture. Our Snakemake pipeline performs sample demultiplexing, overlap paired-end merging, alignment, MIP-arm trimming, variant calling, coverage analysis and report generation.