SIS provides a diverse range of data analysis services to research groups. These include writing custom software for data analysis, development and maintenance of data analysis workflows, performing data analysis on demand.

Custom Scientific Software for Data Analysis

SIS implements software together with research groups, according to the provided requirements, to help them achieve results more effectively, in a faster and more reliable way. The types of software vary greatly. Below are some case studies that can give you an idea of how this service can be useful for you.

Single Cell Force Spectroscopy Analyzer

Atomic Force Microscopy (AFM) allows imaging, measuring, and manipulating matter at the nanoscale level. SIS developed the Single Cell Force Spectroscopy Analyzer to support researchers in using AFM for measuring the force between two living cells.

envipy Software for EAWAG

EAWAG researchers use LCMS to track water quality, for example in swiss lakes and rivers. We took over existing algorithms developed in R and integrated them into a versatile application with a graphical user interface for end users. See envipy Software for EAWAG.


Big data analysis often exceeds the capabilities of researchers own desktop computers or institutes computing servers. Using the ETH HPC infrastructure we implemented algorithms for finding patterns in large text corpuses: Sparkgram.

Data analysis on demand

Genomics Analytics Service

Using the powerful ETH HPC infrastructure, SIS offers fast and convenient analysis of next generation sequencing data. At present the pipeline has support for drawing in measurement data from FGCZ and performing analysis of RNA-Seq data sets, including quality control (fastQC), genome alignment (STAR), gene/transcript/exon level counting (subread-featureCount), common statistics to find differential expression and splicing (from Bioconductor: DESeq, DEXSeq, edgeR,…) and it supports reporting and managing several versions of analysis and report. The RNA-Seq pipeline was initially developed in the group of Constance Ciaudo (Link).

This automated pipeline can either be used by the research group itself after getting a short introductory training, or in a co-analysis approach where a bioinformatician from SIS sits together with a postdoc from the lab to perform the analysis. Customizations of the pipeline (and the service) are possible.

Data Analysis Workflows

In close collaboration with researchers, SIS builds and maintains research data workflows to help increase reproducibility of science and scaling up of complex data analysis tasks. Some case studies are presented here.

OpenSWATH Proteomics Workflow

The Aebersold Lab at the Institute of Molecular Systems Biology is developing and using the new SWATH technology to analyze protein compositions of diverse biological samples. We established a data analysis workflow in close collaboration with members of the Aebersold Lab: OpenSWATH Proteomics Workflow.

Multi-view Reconstruction Workflow for Light-sheet Microscopy

We implemented an existing workflow for automated reconstruction of multi-view image data acquired with a light-sheet microscope. The workflow enables researchers to offload this time consuming analysis from their local desktop machines to the ETH cluster computing infrastructure. It was implemented in close collaboration with members of the group of Dagmar Iber. Read more

Image Analysis: Background Correction Workflow

We implemented a workflow for background correction of microscopy images for the Cell Systems Dynamics group of Timm Schröder to analyze their images on a compute cluster and to track this process for enabling reproducibility of their results. Read more about the Image Background Correction Workflow.