Create a Jupyter Notebook written in Python that aggregates proteomic output from multiple samples and performs some example analysis

The notebook should demonstrate how to use the NMDC API to pull data as well as example analyses and visualizations.

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

Steps:

• 1. Assess background information and collect data for James Stegan's study on riverbed sediment along the Columbia River
• 2. Apply a spectral probability filter across all datasets that optimizes the FDR
• 3. Collapse the remaining peptide identifications and normalize their quantifications
• 4. Generate protein mappings using both "Razor" and "First Hit" strategies
• 5. Perform protein rollup using the "First Hit" results and summarize the sample metadata
• 6. Find additional datasets across omics types in NMDC that share relevant KEGG IDs with this study
• 7. Clean up narration and documentation (proteomic aggregation notebook) #113
• 8. Clean up packages and update requirements.txt (proteomic aggregation notebook) #114
• 9. Rerun notebook after the storage system downtime is over 2/3/25 so gffs can be downloaded (proteomic aggregation notebook) #115
• 10. Rework ending of aggregation notebook so that it instead feeds/links into pmartR rather than doing any analysis. Do incorporate method for obtaining gene annotation and sample information #129