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Updated: Feb 24, 2026

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

An improved analysis methodology for translational profiling by microarray.

Thomas Sbarrato^1,2,3,4, Ruth V Spriggs⁵, Lindsay Wilson⁵

¹Medical Research Council Toxicology Unit, Leicester LE1 9HN, United Kingdom thomas.sbarrato@inserm.fr rvs3@leicester.ac.uk.

RNA (New York, N.Y.)

|August 27, 2017

Summary

This summary is machine-generated.

New analysis methods are needed for translatome array data, as traditional methods fail. We developed INCATome, a novel approach that accurately normalizes data and identifies deregulated genes (DEGs) for improved biological discovery.

Keywords:

microarray analysis polysome profiling translational regulation translatome

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Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Translational regulation is crucial for cellular gene expression.
Existing genome-wide translatome studies often use microarray data.
Current analysis methods are not optimized for the unique characteristics of translatome array data.

Purpose of the Study:

To evaluate the suitability of conventional transcriptome analysis methods for translatome array data.
To develop and validate a novel analysis methodology for translatome data.
To improve the identification of deregulated genes (DEGs) and biological pathways from translatome studies.

Main Methods:

Simulated translatome array data with varying deregulation levels.
Comparison of conventional normalization methods (Quantile, LOESS) and statistical tests.
Development and implementation of the Internal Control Analysis of Translatome (INCATome) methodology.
Application of INCATome to a biological dataset of cells silenced for splicing factor PSF.

Main Results:

Conventional normalization methods and statistical tests failed to accurately process translatome array data.
INCATome demonstrated superior normalization performance compared to existing methods.
INCATome enabled stringent identification of DEGs with improved validation concordance.
Application of INCATome to biological data yielded enhanced identification of true positive DEGs.

Conclusions:

Standard transcriptome analysis methods are inadequate for translatome array data.
INCATome provides a robust solution for normalizing translatome data and identifying DEGs.
INCATome facilitates the discovery of novel biological pathways, offering significant benefits for researchers.
The INCATome package is recommended for both new and existing translatome studies to enhance biological insights.