Genome Expedition Series

I’m utilizing advantages and pitfalls of C. elegans genome data to demonstrate how to approach biological data using Python and Observable.

Part I — introduction

Genome expedition #1 — sneak peek at C. elegans genome

I give a brief introduction on where to find C. elegans genome annotations and structure of annotation data file. I demonstrate basic features using Python and Observable.

Genome expedition #2 — getting prepared for the genome

I implement Python functions to parse and shape existing genome annotation file into a more useful and refined format. This post creates the basis for annotation-dependent data analysis and visualization.

Genome expedition #3 — visualizing genomic elements

In this episode I benefit from the auto() plot functionality of Observable to look at different aspects of gene distribution among the genome. This is also a showcase of Observable’s grammer and capabilities. An interactive notebook version of the post can be also found here.

Genome expedition #4 — advanced visualizations

I use explicit Observable plotting functions to customize the look-and-feel of graphs on annotation data and provide deeper explanation of how different visual elements can be utilized . An interactive notebook version of the post can be also found here.