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Related Experiment Video

Updated: Jun 28, 2026

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Published on: April 6, 2012

Detecting microRNA binding and siRNA off-target effects from expression data.

Stijn van Dongen¹, Cei Abreu-Goodger, Anton J Enright

¹Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.

|November 4, 2008

Summary

Sylamer identifies microRNA and small interfering RNA off-target effects in gene 3' untranslated regions. This method uses a ranked gene list to detect occurrence biases, offering a fast and accurate approach for RNA interference studies.

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Last Updated: Jun 28, 2026

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

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Published on: April 6, 2012

Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs

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Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Published on: May 25, 2015

Area of Science:

Bioinformatics
Molecular Biology
Genomics

Background:

MicroRNA (miRNA) and small interfering RNA (siRNA) are crucial for gene regulation.
Identifying off-target effects is essential for understanding RNA interference (RNAi) experiments.
Current methods may lack efficiency or accuracy in detecting these off-target signals.

Purpose of the Study:

To introduce Sylamer, a novel computational method for detecting miRNA and siRNA off-target signals.
To analyze occurrence biases of sequence elements within 3' untranslated regions (UTRs) of ranked gene lists.
To provide a tool that enhances the speed and accuracy of off-target effect analysis.

Main Methods:

Sylamer processes ranked gene lists derived from miRNA perturbation or RNA interference experiments.
It analyzes the 3' UTRs for sequence element enrichment using hypergeometric P-values.
The method generates a landscape plot visualizing occurrence biases across the gene ranking.

Main Results:

Sylamer effectively detects sequence element biases indicative of off-target effects.
The landscape plot provides a clear visualization of these biases.
The method demonstrated utility, speed, and accuracy across multiple test datasets.

Conclusions:

Sylamer is a valuable tool for identifying miRNA and siRNA off-target effects.
The method offers an efficient and accurate approach for analyzing RNAi experiments.
Sylamer aids in the interpretation of gene expression data following miRNA or siRNA perturbations.