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What is your quest?

A recent report on programming language popularity shows Python is positioned to outshine Java as the most popular language choice.  In an effort to showcase the value of Python in the life sciences and medicine, today we are pleased to launch the Python collection with guest advisor by David Pérez-Suárez (University College London).  The aim of the collection is to expose new tools to a wide audience, while building a critical mass and encouraging collaboration.

How does it… um… how does it work?

This collection follows closely on the heels of our recent ‘Show me the Code’ campaign, where we published software tools spanning the life sciences and medicine; including R packages, Bioconductor vignettes, and Docker containers.  Much like this campaign, the new Python collection benefits from our recent collaborations with Plotly and Code Ocean– meaning authors and readers get more than just a PDF. For instance, authors can embed Jupyter notebooks into their articles as Code Ocean widgets.

The most recent paper in the collection describes segment_liftover, a Python tool developed by Bo Gao and colleagues at the University of Zurich.  The tool, designed to improve the liftover between genomic builds, shows improvement over other existing tools according to peer reviewer Oscar Krijgsman (Netherlends Cancer Institute).

Matthew Frampton, first author of Seqfam: A python package for analysis of Next Generation Sequencing DNA data in families, stresses the importance of publishing Python packages in the quote below.

“It is important to publish Python packages in order to share code that is potentially useful to others, and to promote reproducibility of results. Under the F1000Research model, author(s) of a python package can rapidly publish a descriptive software article which increases the package’s visibility in the relevant community. In addition, the transparent review process provides potential users with expert perspectives on the package’s utility.”

You have proved yourself worthy. Will you join me?

We have already set up similar gateways (large collections) such as Cytoscape Apps and RPackages. In our experience, publishing a citable, peer-reviewed article outlining a new software tool boosts its visibility and gives its creators proper credit for their contribution. The F1000Research publishing platform also preserves the original syntax highlighting and supports article versioning so that the paper can be iterated in line with the code.

If you have a Python tool you feel would benefit from being peer reviewed, please do submit to the Python collection here.