Evotec has developed a web-based platform, PanHunter^® which combines commonly used statistical analysis algorithms with the potential of machine learning and artificial intelligence capabilities to enable user-friendly analysis of complex data sets.

PanHunter has functionalities that cover every aspect of data handling; users just need to log in, open the GUI, launch the necessary applications and start analyzing their data. The great advantage of PanHunter is its versatility: it contains everything that is necessary to conduct investigations of even complex and large datasets, facilitates collaboration, eliminates the need to wait for feedback from a bioinformatics core facility, and can be easily updated. Moreover, it follows a clear no-code-principle so that all scientists involved in a project can interact with the data.

As a result, PanHunter is the perfect tool to function as a cental hub for all omics-related research in your organization. Your bioinformatics will love it for its data management and processing capabilities, while all other scientists will be empowered to facilitate major parts of the data analysis autonomously. It’s simply an all-in-one solution for omics-data-driven research.
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Highly interactive: No more waiting for bottlenecks to clear. Results appear right away and can be prepared for presentation and discussion.  

True multi-omics support: Analyze and interpret data from many omics platforms and protocols across species. Interconnect the datasets to reveal a whole spectrum of insights into disease processes and their treatment. 

Modular and extendable: Opening the PanHunter start-page reveals the spectrum of apps that aid the user during the in-depth analysis of multi-omics data. The ability to combine self-generated data with data in the public domain increases the validity of the final analysis. 

Clicks instead of code: By interacting with their data via the graphical user interface and the various apps, there is no need for the user to write their own scripts: a few clicks bring the results that matter. 

Fully collaborative: The design of PanHunter supports a high level of collaboration so that data can be shared, results compared, and reports discussed. 

Reliable and reproducible: The PanHunter user can rely on the peer-reviewed algorithms that lead to fully transparent and reproducible workflows.  

New Start Page

The new start page offers a more modern and cleaner overview for the available projects and apps. It also allows users to easily reach our Knowledge Center, the Administration app, and other resources.

scExplorer Study Management

Within the new Single-Cell Explorer app, data managers can now manage new studies. This means that they can evaluate the quality of a newly added study before “registering” it with the rest of PanHunter, making it available for all other users within the project as well. This helps to avoid premature data analysis of not-yet fully QC’d data.

New Comparisons App Adapts to Omics Context

The calculation of new comparisons (differential analysis results) now automatically adopts to the omics context you are working in. Selecting a transcriptomics dataset will use different default parameters (e.g., using “DESeq2” instead of “limma”) compared to when selecting a proteomics dataset.

Updated Ensemble Version

PanHunter is now using the latest Ensemble database version 111.