Leading in straightforward, interactive, collaborative, and easy-to-use analysis software for biological data.

Data is the basis for all insights: We provide the necessary tools for fast and comprehensive interpretations.

Highly interactive

No more waiting for pipelines to finish. See your results right away and fully interactive!

True multi-omics support

Analyse and interpret data from many omics platforms and protocols across species borders together or next to each other.

Modular and extendable

Battle-proven features, developed by researchers for researchers.

Always the right interface

You decide whether you interact with the data via the graphical user interface or use scripts and the API.

Fully collaborative

Share data, results, and more with your colleagues or even work in parallel.

Reliable and reproducible

Rely on peer reviewed algorithms and a fully transparent and reproducible workflow.


Dr John Szilagyi

Dr. John Szilagyi

Bristol Meyers Squibb

John is a research scientist in the non-clinical investigative toxicology department at Bristol Meyers Squibb (BMS). His area of focus is placental toxicology.

John has a broad expertise in drug transport, enzyme and cell-based assays. He obtained his PhD in toxicology from Rutgers University in New Brunswick and joined BMS in June 2020 after a postdoctoral research stay in the toxicology and environmental medicine department of the University of North Carolina at Chapel Hill.

John`s project with Evotec:

My active collaboration with Evotec involves high-content screening and bioinformatics investigations of differentiating pluripotent stem cells treated with protein-degrading teratogens, such as thalidomide. The overall goal of this work is to better understand the mechanisms behind stem cell differentiation in embryonic limb development and identify the sensitive pathways that can disrupt that process. In doing so, we hope to better inform drug discovery and development to improve the development strategy for safer and more effective medicines.

Dr. Britta Seip

Dr. Britta Seip

Research Scientist Metabolic Disease

For me as a molecular biologist running high-throughput transcriptome analyses, it is very easy to get started with data analysis without any prior coding knowledge.

EVOpanHunter supports the data analysis and visualisation of large datasets with just a few mouse-clicks. To quality control my data after sequencing, I regularly use the “Sample QC” and “scRNA-Seq Browser” apps and to explore my data later on I use the “New Comparison”, “Top Tables” and “Gene Comparisons” apps.

The standard statistical parameters are pre-set, but are adjustable for more experienced users, giving me at the same time confidence as well as the ability to tailor my analyses to my research question. I especially appreciate that in EVOpanHunter all analyses, starting from Sample QC to the in-depths analysis, are offered in one tool. Together with colleagues from different scientific backgrounds we can thus simultaneously and collaboratively investigate the dataset to answer our research questions and drive the projects to the next level.

Ramon Oliveira Vidal

Dr. Ramon Oliveira Vidal

Research Scientist

Starting from a clean visualisation of dimension reductions, one can select individual sets of cells or entire cell clusters and annotate them manually.

EVOpanHunter has great support to single-cell sequencing and its related technologies such as spatial transcriptomics. It is also possible to automatically annotate cell-types using our single-cell classifier, which is based on machine learning technology. It can be of great help when analysing complex or unknown tissue types.

A typical data analysis workflow starts with a quality check to spot technical biases or outliers that would interfere with biological interpretation. Subsequent dimension reduction gives a first idea of sample clustering and structure as well as provides the starting point for downstream analyses. Typically, we look into differential gene expression, pathway and GO term enrichment, network analysis, interpretation of patient data, and signature matching. EVOpanHunter apps intuitively guide through this workflow, making omics data analysis also accessible to wet lab scientist after a short introduction.

An important role of a Computational Biologist is to present insights to projects and clients. With EVOpanHunter we are able to produce high quality, interactive visualisations that are easy to share, understand, and can be integrated into our analysis reports. These visualisations include many different formats ranging from dimension reduction plots, heatmaps, networks and pathway visualisations, and each has multiple options to customise.

Together with our colleagues, we are constantly working on the implementation of new tools and the improvement of existing apps, ensuring state-of-the-art analysis of datasets. Thereby, EVOpanHunter is organically growing with every new project, addresses real life questions, and incorporates the expertise of many, highly skilled scientists.
Our vision for EVOpanHunter is to be a digital working space that enables a joint omics data analysis by wet and dry lab teams and allows focusing on data mining and biological insights. Therefore, it is great to see that EVOpanHunter development builds on user perspectives and needs.

Winfried Wunderlich

Dr. Winfried Wunderlich

Group and Project Leader Metabolic Disease

EVOpanHunter has supported my team and me in several areas of Drug Discovery: Target ID – Generating EVOpanHunter’s Top Tables makes it just a mouse click away to compare disease vs normal transcriptomes in order to come up with disease-related pathways

Target validation – Using EVOpanHunter’s ‘Drill down’ apps it is just a few clicks to generate a customised display of expression levels for individual genes as well as groups of related genes in order to direct follow up assays towards specific cell lines or tissues in which the target gene is expressed.

Mechanism-of-action – EVOpanHunter’s New comparison’ app and its linkage to Pathway mapping and Network visualisation features made it easy (even for non-bioinformaticians) to perform the automated comparison of transcriptomic data sets which enabled the identification of pathways that were affected e.g. by compound treatments or genetic manipulations.

Translatability – Using the ‘Compare Top Tables’-functionality it was effortless to compare disease-associated signatures between a specific human disease and the respective animal model(s) in order to identify the suitability of a model to study a particular pathway in the context of a disease.

The EVOpanHunter Team was always fast and helpful in many ways, in particular not just by explaining the optimal use of individual apps but also by expanding functionalities on demand. In case they hit a glass ceiling, the EVOpanHunter team effectively interacted with a team of Computational Biologists to go even deeper into data analysis.


with test data set

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Frequently Asked Questions

Our goal is to empower any scientist to explore, analyze, and interpret deep biological data in the fastest, easiest, and most visual and collaborative way. Furthermore, we want to democratize access to life science information, analysis results, and insights to help researchers find the shortest paths to new treatments.

Many bioinformatic tools available today rely on robust pipelines, which come with the advantage of high reproducibility. However, they also come with the disadvantage of rigidity and long waiting times between starting and interpreting the analysis. In our understanding, a key  factor to success in research are fast, interactive, and straightforward tools that stimulate creative and immersive interaction with the data and providing results right away. That’s why EVOpanHunter democratizes data access by providing the ability to analyze data interactively. It provides all the necessary analysis options to dig deeper into the data and helps finding all the interesting piece to the research puzzle at hand. Most importantly, this interactivity does not come with a loss of reproducibility, which is still achieved through strong version control and audit trails at all levels. However, we think this should not come into the way of a researcher who is hunting down the next breakthrough!

Compared to simpler and widely used assays, omics technologies offer the opportunity to take an unbiased snapshot of a biological system at the molecular level. It also goes along with as drastic change in the general approach – moving from hypothesis driven research to an a priori discovery driven approach, which allows to gain understanding of all underlying processes. Due to the high dimensionality of the data generated by omics technologies, it becomes possible to “zoom in” on anything you might be interested in. EVOEVOpanHunter is the platform that enables you to facilitate all this data analysis and interpretation.

As the number of omics datasets being published increases exponentially, it is very likely that you already came across a publication, which used or was based on omics data, even though you haven’t generated them for your own research. In a situation like that, EVOpanHunter can help you out perfectly. Our platform allows you to easily integrate the data you would like to have a look at, even when it comes from the public domain. Furthermore, EVOpanHunter provides guidance for you how to analyze and estimate the quality of the data your are looking at. And if that is not enough, if you have more questions or need further support interpreting the data, you can always reach out to us and get help from our teams of Application Specialists and Computational Biologists.