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Experimental RNAi02:15

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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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Related Experiment Video

Updated: May 9, 2025

Electrophoretic Mobility Shift Assay EMSA for the Study of RNA-Protein Interactions: The IRE/IRP Example
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SIREs 3.0, an improved RNA prediction tool for iron-responsive elements.

Clara Suárez-Quintana1,2, Mar Navarro-Padilla3, Uciel Chorostecki2

  • 1Department of Biomedicine, Iron metabolism: Regulation and Diseases Group, Universitat Internacional de Catalunya (UIC), Sant Cugat del Vallès 08195, Spain.

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|May 5, 2025
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Summary

SIREs 3.0 is an improved web server for identifying iron-responsive elements (IREs) in RNA sequences. This tool aids in understanding iron metabolism regulation and associated human diseases.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genetics

Background:

  • Iron-responsive elements (IREs) are crucial RNA motifs regulating iron metabolism genes via iron regulatory proteins (IRPs).
  • Dysregulation of the IRP/IRE system is implicated in various human diseases.
  • The SIREs web server facilitates the identification of IREs in nucleotide sequences.

Purpose of the Study:

  • To present SIREs 3.0, an upgraded web server for predicting IREs.
  • To enhance the prediction accuracy and user experience for IRE analysis.
  • To provide researchers with advanced tools for studying iron metabolism regulation.

Main Methods:

  • Developed a Flask-based framework for improved interconnectivity.
  • Integrated NCBI and Ensembl APIs for fetching genomic data.
  • Introduced novel input modes (batch, transcript, gene) and updated IRE motifs based on in vivo data.

Main Results:

  • SIREs 3.0 offers improved IRE prediction by incorporating transcript location and novel motifs.
  • Enhanced scoring system and graphical representations improve data interpretation.
  • The web server now features a more responsive and accessible interface across devices.

Conclusions:

  • SIREs 3.0 provides a powerful and versatile platform for IRE prediction.
  • The enhanced features facilitate large-scale and single-gene analyses of iron metabolism.
  • This tool supports research into diseases linked to IRP/IRE system disruptions.