Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers

What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible.Author summary: Galaxy users can utilize a large number of tools and workflows. What they could not previously do is run ad hoc scripts and arbitrary tools within their Galaxy instance. This was very limiting, as initial analyses of data often involve interactive exploration with tools like Jupyter or RStudio—powerful platforms that are becoming increasingly popular in life sciences. Here, we showcase Galaxy Interactive Environment framework, designed to combine Galaxy's tools and workflows with environments such as Jupyter.