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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Splitpea: a Python package for protein-protein interaction network rewiring analysis due to alternative splicing.

Jeffrey Zhong1, Alyssa Cantu1, Ruth Dannenfelser1

  • 1Department of Computer Science, Rice University, Houston, TX 77005, United States.

Bioinformatics (Oxford, England)
|April 11, 2026
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Summary
This summary is machine-generated.

Splitpea analyzes alternative splicing events to identify changes in protein-protein interaction (PPI) networks. This tool helps researchers visualize how alternative splicing impacts PPIs, revealing potential disruptions or enhancements.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Alternative splicing significantly contributes to proteomic diversity.
  • Understanding alternative splicing's impact on protein-protein interactions (PPIs) is crucial for deciphering cellular functions.

Purpose of the Study:

  • To introduce Splitpea, a novel Python package for analyzing alternative splicing-induced rewiring in PPI networks.
  • To provide a user-friendly tool for mapping splicing events to network changes.

Main Methods:

  • Splitpea integrates skipped exon event data from tools like SUPPA2 and rMATS.
  • It accepts various input formats, including percent spliced in (PSI) values and exon usage statistics.
  • The package generates network graphs and visualization-ready files.

Main Results:

  • Splitpea effectively maps alternative splicing events to PPI network rewiring.
  • It produces detailed summary statistics at both edge and gene levels.
  • The tool generates output files compatible with visualization software such as Cytoscape and Gephi.

Conclusions:

  • Splitpea offers a powerful approach to identify PPIs affected by alternative splicing.
  • The package facilitates the exploration of how alternative splicing influences protein interaction networks.
  • This tool aids in understanding the functional consequences of alternative splicing in biological systems.