Most research organizations are comprised of individuals with a wide range of technical skill, from experienced data scientists and software developers to complete nonprogrammers. Making Jupyter accessible to all members of a research organization, regardless of their programming ability, empowers it to best utilize the latest analysis methods while avoiding bottlenecks due to a lack of coders.
Thorin Tabor offers an overview of the GenePattern Notebook, which offers a wide suite of enhancements to the Jupyter environment to help bridge the gap between programmers and nonprogrammers. These include the capability to render any Python function as an interactive widget, a rich-text editor for markdown cells, a graphical interface for adding new analytic tools to a notebook, and the GenePattern Notebook Repository, a free JupyterHub instance where researchers can develop and publish their own bioinformatic notebooks. The GenePattern Notebook also allows Jupyter to communicate with the open source GenePattern API. This service wraps hundreds of different software tools for general machine learning methods—including clustering, classification, and dimensionality reduction—as well those in the bioinformatics domain, including tools for analyzing gene expression data, sequence variation, proteomics, and genomic networks. It makes all of these methods available through a user-friendly interface that is accessible to both programming and nonprogramming researchers.
Thorin Tabor is a software engineer at UCSD and a contributing scientist at the Broad Institute. He is the lead developer of the GenePattern Notebook and an open source developer on the integration of bioinformatic tools with Jupyter.
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