SPINNAKER

SPINNAKER predicts competing endogenous RNA (ceRNA) interactions within biological networks to identify regulatory crosstalk among mRNAs, long non-coding RNAs (lncRNAs), and microRNAs.

Key Features:

• R implementation: Implemented as a collection of R functions that reproduce the underlying mathematical model.
• Mathematical model: Employs a quantitative model to predict ceRNA interactions and crosstalk.
• MATLAB-to-R port: The model and algorithms were adapted from an original MATLAB implementation into R.
• Computational optimization: Incorporates optimizations aimed at reducing computational execution time for analyses.
• Empirical application: Applied to identify PVT1 long non-coding RNA acting as a ceRNA for the miR-200 family in breast invasive carcinoma.

Scientific Applications:

• CeRNA network prediction: Identification of competing endogenous RNA interactions and crosstalk within transcriptomic networks.
• Cancer research: Analysis of RNA-mediated regulatory mechanisms in cancer, including breast invasive carcinoma.
• Gene regulation studies: Investigation of post-transcriptional regulation involving mRNAs, lncRNAs, and microRNAs.
• Target prioritization: Highlighting candidate RNA interactions for downstream experimental validation and therapeutic investigation.

Methodology:

The methodology adapts a mathematical model originally implemented in MATLAB into an R implementation composed of R functions.