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OpenClaw-Medical-Skills 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.

install
source · Clone the upstream repo
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/bulk-stringdb-ppi" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-string-protein-interaction-analysis- && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/bulk-stringdb-ppi" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-string-protein-interaction-analysis- && rm -rf "$T"
manifest: skills/bulk-stringdb-ppi/SKILL.md
tags
#protein-interaction#ppi-network#string-db#network-visualization#gene-list-analysis
source content

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