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Related Concept Videos

MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...
RNA Interference01:23

RNA Interference

RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
Experimental RNAi02:15

Experimental RNAi

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 24, 2026

Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs
11:00

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

Published on: June 12, 2018

Quantification of miRNA-mRNA interactions.

Ander Muniategui1, Rubén Nogales-Cadenas, Miguél Vázquez

  • 1Group of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastian, Spain.

Plos One
|February 21, 2012
PubMed
Summary
This summary is machine-generated.

We developed TaLasso, a new method to accurately identify microRNA (miRNA) targets by combining expression data and sequence information. TaLasso improves upon existing methods by significantly increasing the recovery of experimentally validated miRNA-mRNA interactions.

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Last Updated: May 24, 2026

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Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) are small RNA molecules regulating gene expression by targeting messenger RNAs (mRNAs).
  • Existing miRNA-mRNA interaction databases, primarily based on sequence complementarity, suffer from high false positive rates and limited overlap.
  • Integrating expression data with sequence-based predictions offers a promising approach for more accurate miRNA target identification.

Purpose of the Study:

  • To develop and validate a novel computational method for identifying miRNA-mRNA interactions.
  • To improve the accuracy of miRNA target prediction by integrating expression profiles and sequence information.
  • To enhance the discovery of biologically relevant miRNA-mRNA regulatory relationships.

Main Methods:

  • Utilized LASSO regression with non-positive constraints to integrate miRNA and mRNA expression data with sequence-based predictions.
  • Developed a method named TaLasso (miRNA-Target LASSO) to enforce solution sparseness and identify miRNAs with down-regulatory effects on target mRNA expression.
  • Applied TaLasso to two public datasets containing paired human miRNA and mRNA expression levels.

Main Results:

  • TaLasso recovered miRNA-target interactions significantly enriched in experimentally validated targets compared to other algorithms.
  • The top-ranked interactions identified by TaLasso were associated with biologically meaningful gene functions.
  • The method demonstrated superior performance in identifying relevant miRNA-mRNA relationships.

Conclusions:

  • TaLasso provides a robust and accurate method for identifying miRNA-mRNA targets by integrating multiple data sources.
  • The approach enhances the reliability of miRNA target prediction, leading to more meaningful biological insights.
  • TaLasso is available as open-source code (Matlab/R) and a web tool for human miRNA analysis.