string-protein-interaction-analysis-with-omicverse

Starlitnightly's avatarfrom Starlitnightly

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

⭐783stars🔀95forks📁View on GitHub🕐Updated Nov 20, 2025
[Academic Research]

When & Why to Use This Skill

This Claude skill automates the analysis of protein-protein interactions (PPI) by integrating the STRING database with the omicverse library. It enables researchers to seamlessly query interaction data, construct sophisticated network graphs, and generate publication-quality visualizations for gene lists, significantly accelerating bioinformatics and functional genomics workflows.

Use Cases

  • Biological Network Visualization: Transform bulk gene lists into styled PPI graphs with custom color-coding and metadata to visualize complex cellular relationships.
  • Functional Proteomics Research: Query the STRING database for specific species using NCBI taxonomy IDs to explore protein interaction networks and evidence channels.
  • Gene Candidate Prioritization: Identify key hub proteins and potential regulatory partners by analyzing interaction scores and expanding networks with predicted nodes.
  • Bioinformatics Pipeline Automation: Streamline the transition from raw gene symbols to interactive network models within a Python-based omicverse environment.
namestring-protein-interaction-analysis-with-omicverse
titleSTRING protein interaction analysis with omicverse
descriptionHelp Claude query STRING for protein interactions, build PPI graphs with pyPPI, and render styled network figures for bulk gene lists.

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