Mainzelliste SecureEpiLinker (MainSEL): privacy-preserving record linkage using secure multi-party computation.

MOTIVATION: Record Linkage has versatile applications in real-world data analysis contexts, where several datasets need to be linked on the record level in the absence of any exact identifier connecting related records. An example are medical databases of patients, spread across institutions, that have to be linked on personally identifiable entries like name, date of birth or ZIP code. At the same time, privacy laws may prohibit the exchange of this personally identifiable information (PII) across institutional boundaries, ruling out the outsourcing of the record linkage task to a trusted third party. We propose to employ privacy-preserving record linkage (PPRL) techniques that prevent, to various degrees, the leakage of PII while still allowing for the linkage of related records. RESULTS: We develop a framework for fault-tolerant PPRL using secure multi-party computation with the medical record keeping software Mainzelliste as the data source. Our solution does not rely on any trusted third party and all PII is guaranteed to not leak under common cryptographic security assumptions. Benchmarks show the feasibility of our approach in realistic networking settings: linkage of a patient record against a database of 10 000 records can be done in 48 s over a heavily delayed (100 ms) network connection, or 3.9 s with a low-latency connection. AVAILABILITY AND IMPLEMENTATION: The source code of the sMPC node is freely available on Github at https://github.com/medicalinformatics/SecureEpilinker subject to the AGPLv3 license. The source code of the modified Mainzelliste is available at https://github.com/medicalinformatics/MainzellisteSEL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

StreaMD: Advanced analysis of molecular dynamics using R.

Gromacs is one of the most popular molecular simulation suites currently available. In this contribution we present streaMD, the first interface between Gromacs trajectory files and the statistical language R. The amount of data created due to ever increasing computational power renders fast and efficient analysis of trajectories into a challenge. Especially as standard approaches such as root-mean square fluctuations and the like provide only limited physical insight. In our streaMD package integration of the Gromacs I/O libraries with advanced, graph-based analysis methods as the java library Stream leads to both: improved speed and analysis depth. We benchmark our results and highlight the applicability of the package by an interesting problem in RNA design, namely the interaction of tetracycline with an aptamer. © 2018 Wiley Periodicals, Inc.