Starlitnightly

string-protein-interaction-analysis-with-omicverse

Help Claude query STRING for protein interactions, build PPI graphs with pyPPI, and render styled network figures for bulk gene lists.

Starlitnightly 1,190 146 Updated 10mo ago

Resources

1
GitHub

Install

npx skillscat add starlitnightly/omicverse/string-protein-interaction-analysis-with-omicverse

Install via the SkillsCat registry.

About this skill

This skill enables Claude to query STRING for protein-protein interactions using omicverse, construct PPI networks with pyPPI, and generate styled network visualizations for bulk gene lists. It streamlines the process of analyzing gene interaction data by automating species selection, API queries, and network rendering. Developers should use it when working with gene lists that require protein interaction analysis and visualization within the omicverse framework.

SKILL.md

STRING protein interaction analysis with omicverse

Overview

Invoke this skill when the user has a list of genes and wants to explore STRING protein–protein interactions via omicverse. The
workflow mirrors `t_network.ipynb`, covering species selection, S
TRING API queries, and quick visualisation of the resulting network.

Instructions

  1. Set up libraries
    • Import omicverse as ov and call ov.utils.ov_plot_set() (or ov.plot_set()) to match omicverse aesthetics.
  2. Collect gene inputs
    • Accept a curated list of gene symbols (gene_list = [...]).
    • Encourage the user to flag priority genes or categories so you can colour-code groups in the plot.
  3. Assign metadata for plotting
    • Build dictionaries mapping genes to types and colours, e.g. gene_type_dict = dict(zip(gene_list, ['Type1']*5 + ['Type2']*6 )) and gene_color_dict = {...}.
    • Remind users that consistent group labels improve legend readability.
  4. Query STRING interactions
    • Call ov.bulk.string_interaction(gene_list, species_id) where species_id is the NCBI taxonomy ID (e.g. 4932 for yeast).
    • Inspect the resulting DataFrame for combined scores and evidence channels to verify coverage.
  5. Construct the network object
    • Initialise ppi = ov.bulk.pyPPI(gene=gene_list, gene_type_dict=..., gene_color_dict=..., species=species_id).
    • Run ppi.interaction_analysis() to fetch and cache STRING edges.
  6. Visualise the network
    • Generate a default plot with ppi.plot_network() to reproduce the notebook figure.
    • Mention that advanced styling (layout, node size, legends) can be tuned through ov.utils.plot_network keyword arguments if
      the user requests adjustments.
  7. Troubleshooting
    • Ensure gene symbols match the species—STRING expects case-sensitive identifiers; suggest mapping Ensembl IDs to symbols when
      queries fail.
    • If the API rate-limits, instruct the user to wait or provide a cached interaction table.
    • For missing interactions, recommend enabling STRING's "add_nodes" option via ppi.interaction_analysis(add_nodes=...) to exp
      and the network.

Examples

  • "Retrieve STRING interactions for FAA4 and plot the network highlighting two gene classes."
  • "Download the STRING edge table for my Saccharomyces cerevisiae gene panel and colour nodes by module."
  • "Extend the network by adding the top five predicted partners before plotting."

References