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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...
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...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: Jun 21, 2026

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice
07:07

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

A modified LOESS normalization applied to microRNA arrays: a comparative evaluation.

Davide Risso1, Maria Sofia Massa, Monica Chiogna

  • 1Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy.

Bioinformatics (Oxford, England)
|July 25, 2009
PubMed
Summary

A new normalization method, loessM, is effective for microRNA (miRNA) arrays, outperforming standard techniques. Combining loessM with eCADS experimental design improves identification of differentially expressed genes by reducing false positives and negatives.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Microarray normalization is crucial for accurate gene expression analysis, removing technical biases.
  • Existing normalization methods, effective for large-scale arrays, may be unsuitable for microRNA (miRNA) platforms.
  • This study addresses the impact of normalization on miRNA differential expression analysis.

Purpose of the Study:

  • To propose and evaluate a novel normalization method, loessM, for two-colour miRNA arrays.
  • To investigate the influence of different normalization strategies on identifying differentially expressed genes in miRNA data.
  • To compare loessM performance against existing methods using simulated and real datasets.

Main Methods:

  • Development of the loessM normalization algorithm.
  • Application of various normalization techniques to simulated and real two-colour miRNA datasets.
  • Evaluation of normalization impact on differential expression analysis, focusing on false positive and negative rates.
  • Comparison of loessM with other methods and assessment of combined approaches with experimental designs like eCADS.

Main Results:

  • Standard normalizations can distort miRNA data, leading to significant false positives and negatives.
  • The novel loessM normalization method demonstrates superior performance across various experimental scenarios.
  • Channel effects negatively impact small arrays when differential expression assumptions are unmet, necessitating appropriate experimental design.
  • Combining loessM with eCADS experimental design enhances specificity and sensitivity for differential gene expression detection.

Conclusions:

  • The loessM normalization method offers improved accuracy for miRNA expression analysis.
  • Appropriate experimental design, such as eCADS, is critical for mitigating bias in miRNA studies.
  • The combination of loessM and eCADS provides robust results for identifying differentially expressed genes in miRNA expression data.