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Related Experiment Video

Updated: Feb 17, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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PlanNET: homology-based predicted interactome for multiple planarian transcriptomes.

S Castillo-Lara1, J F Abril1

  • 1Computational Genomics Laboratory, Genetics, Microbiology and Statistics Department, Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Catalonia, Spain.

Bioinformatics (Oxford, England)
|November 30, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed PlanNET, a web tool predicting protein-protein interactions in planarians. This aids understanding of regeneration mechanisms in this model organism.

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Area of Science:

  • * Molecular Biology and Genomics
  • * Bioinformatics and Computational Biology

Background:

  • * Planarians are key model organisms for studying animal regeneration.
  • * Limited protein-protein interaction data hinders understanding of planarian regeneration mechanisms.

Purpose of the Study:

  • * To develop a computational method for predicting protein-protein interactions in planarians.
  • * To create a user-friendly web application for exploring these interactions and associated data.

Main Methods:

  • * Prediction of protein-protein interactions using sequence homology and a human interactome reference.
  • * Application of the methodology to 11 Schmidtea mediterranea transcriptomic datasets.
  • * Development of the PlanNET web application utilizing Neo4j for data management and exploration.

Main Results:

  • * Successful prediction and storage of protein-protein interaction networks for Schmidtea mediterranea.
  • * PlanNET enables visualization of networks, sequence annotations, and mapping of RNA-seq expression data.
  • * The tool facilitates transcript-centric exploration of the planarian interactome.

Conclusions:

  • * PlanNET provides a valuable resource for researchers studying planarian regeneration.
  • * The developed methodology offers a reproducible approach for predicting and exploring protein interaction networks in non-model organisms.