Clinical Bioinformatics
Advances in high-throughput technologies — including next-generation sequencing, mass spectrometry, and single-cell omics — have unlocked unprecedented insights into disease mechanisms, tissue composition, and biological systems. Translating these data into actionable knowledge requires tailored, scalable, and secure bioinformatics solutions. Our clinical bioinformatics services specialize in integrative multi-omics analysis, combining custom workflows, reproducible pipelines, and high-performance computing infrastructure designed for handling sensitive clinical data. Where appropriate, we also incorporate machine learning and AI-driven methods to enhance discovery and support predictive modeling in translational research.
Types of services
We leverage ETH Zurich’s state-of-the-art secure computing environment to ensure safe, reliable, and compliant handling of sensitive biomedical and clinical data. Our infrastructure enables scalable computational analysis close to the data, supports high-throughput workloads, and meets the highest standards for data protection. We assist with operational procedures, resource management, and offer interfaces to integrate with complementary services and platforms.
We provide end-to-end workflows for the analysis of single-cell genomics data that enable detailed insights into cellular composition, tumor heterogeneity, immune microenvironments, and gene or pathway-level activity at single-cell resolution.
Our robust, workflow-managed pipelines support both germline and somatic variant calling, including SNVs, InDels, and CNVs. We process data across various sequencing scales — from targeted panels to whole exomes and genomes — and scale analyses from single samples to large patient cohorts.
We enrich genomic variant data with treatment-relevant annotations using curated knowledge bases (e.g., CIViC, OncoKB), drug-gene interaction databases (e.g., DGIdb), and information from clinical trial registries (e.g., clinicaltrials.gov). Literature mining and in-silico prediction tools complement these resources to support translational interpretation and clinical decision-making.
We offer scalable, reproducible pipelines for bulk RNA-seq analysis, including gene expression quantification, as well as the detection of gene fusions, isoform switching, and alternative splicing events that may underlie disease phenotypes.
We perform integrative analyses across multiple omics layers — such as genomics, transcriptomics, epigenomics, and proteomics — to uncover regulatory mechanisms and disease-driving processes. Our approaches include data harmonization, proteogenomic linking, and pathway-level interpretation to provide a systems-level view of biological function and clinical relevance.
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