Create a notebook exploring functional annotations in a single proteomics sample (Python)

[bmeluch]

Create a Jupyter Notebook that pulls in proteomics data from the NMDC database, selects an example dataset, and demonstrates how to assess the statistical significance of protein functional annotations.

Milestone 2.26: Sample Jupyter and RStudio notebooks available that highlight NMDC data and metadata

[CamiloPosso]

Just about finished with the draft in python. Currently, given a single bio-sample, we can fetch the protein reports and gff annotations, do the over-representation analysis for any annotation category present (cog, pfam, etc), and plot the most significant over-represented annotations. In these example plots we're using biosample nmdc:bsm-13-bgefg837.

This is an example of the table produced by the analysis, shown in the data wrangle view of vscode:

And this is the plot we can make from the results:

I think we should make the annotations human readable in this analysis, so I'm working on translating those. Ie, the pfam annotation PF13620 should read 'Carboxypeptidase regulatory-like domain', and so on.