What do we do?
Our group at the Scientific IT Services (SIS) is an interdisciplinary bioinformatics and scientific IT support group which builds up computational tools. These tools range from lab databases to reusable framework components that enable and support both data analysis and data management in life science research and beyond. We collaborate with Swiss and European research groups and industry in the life science sector – such as SystemsX.ch, SyBIT FAIRDOM, HPC-CH and swissuniversities' eSCT / EnhanceR community. We improve and port scientific software, develop data management solutions and provide associated services. We also integrate and operate data analysis pipelines, and provide training and consulting in databases, scientific software development, high-performance and cloud computing.
Highlights 2016This year, we had many discussions with ETH researchers from the Personalized Health domain on their computing and data privacy needs. In response to these discussions, we started to build up the new dedicated data and computing infrastructure “Leonhard Med” at ETH, along with associated data services. While parts of the concept are still in flux as the Swiss Personalized Health Network is shaping up, Leonhard Med is already in use today.
As in the last years, we have been busy porting data analysis platforms to Euler, either from desktops or from older compute clusters. We sparkified the network analysis of antibody repertoires to allow for the first-ever network analysis of a full antibody repertoire with samples containing up to 500k+ CDR3 sequences. We also ported a big software platform this year, the iPortal Proteomics data analysis system, which runs workflows like OpenSWATH. The new EulerPortal performs about twice as fast as the old system.
As the demand for computing power by ETH users has been constantly growing, the HPC cluster Euler has been upgraded with 452 new compute nodes thus increasing the performance of the cluster from 570 TFlops to 1 PFlop. At the same time, Euler’s project storage was extended by 1PB to adapt to ever-growing research data. The next substantial upgrade will be installed early 2017.
We have released a new major version 16.05 of the openBIS data management platform. Among other improvements, the new release features a new and more flexible application programming interface (v3), improved search capabilities and better performance for a set of advanced use cases. The new API incorporates feedback from many users, and in particular from the OpenSEEK software development in the FAIRDOM consortium.
In order to sustain the efforts of FAIRDOM beyond the life-time of the project consortium and support data management that is Findable, Accessible, Interoperable and Reusable, the FAIRDOM association was founded and is now open to new members.
In 2016, the openBIS ELN-LIMS user base increased steadily and now contains more than 15 groups from three different departments. In the CRUS P2 project DLCM (“Data Lifecycle Management”), we worked on improving further the system based on biologists' feedback. Some of the improvements are web-based project summaries, downloadable reports on projects, simplified data upload and direct access to data in the system via a built-in fileserver.
As the complexity of data analysis approaches that are used in the life sciences is growing, ensuring the reproducibility of scientific findings is focusing. We have been working with life science wet labs to integrate the open scientific computing platform Jupyter with the openBIS ELN-LIMS data management system. This is ongoing work, and the first results are very encouraging.
Among the training activities, the 4-day intensive workshop on parallel programming with MPI and OpenMPI in the summer was a highlight. It was conducted by Rolf Rabenseifner from HLRS Stuttgart, a well-known expert in the field.
Main publications 2016
- Wolstencroft K, et al. FAIRDOMHub: a repository and collaboration environment for sharing systems biology research. Nucleic Acids Research 2016; DOI: 10.1093/nar/gkw1032.
- Khan T A, Friedensohn S, Gorter de Vries A R, Straszewski J, Ruscheweyh H J, Reddy S T. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting. Science Advances, 11 Mar 2016, Vol. 2, no. 3; DOI:10.1126/sciadv.1501371.
- Fusco L, Lefort R, Smith K, Benmansour F, Gonzalez G, Barillari C, Rinn B, Fleuret F, Fua P, Pertz O. Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling. J Cell Biol 2016 212:91-111; DOI:10.1083/jcb.201506018.