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.

PanHunter 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 PanHunter 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.

PanHunter 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. PanHunter 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 PanHunter 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, PanHunter is organically growing with every new project, addresses real life questions, and incorporates the expertise of many, highly skilled scientists.
Our vision for PanHunter 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 PanHunter development builds on user perspectives and needs.

Target validation – Using PanHunter‘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 – PanHunter‘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 PanHunter 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 PanHunter team effectively interacted with a team of Computational Biologists to go even deeper into data analysis.