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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

aCGH.Spline--an R package for aCGH dye bias normalization.

Tomas W Fitzgerald¹, Lee D Larcombe, Solena Le Scouarnec

¹Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. tf2@sanger.ac.uk

Bioinformatics (Oxford, England)

|March 2, 2011

Summary

Accurate copy number analysis requires careful normalization of array-based comparative genomic hybridization (aCGH) data. Our new method, aCGH.Spline, effectively removes dye bias, improving the detection of copy number variations (CNVs) and alterations (CNAs).

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Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Accurate detection of copy number changes in array-based comparative genomic hybridization (aCGH) data is crucial.
Dye bias, arising from differential fluorophore labeling (Cy5/Cy3), can skew aCGH data interpretation and increase false discoveries.

Purpose of the Study:

To develop an efficient method for removing dye bias from large aCGH datasets.
To improve the accuracy of copy number variation (CNV) and copy number alteration (CNA) detection.

Main Methods:

Developed aCGH.Spline, a novel method combining natural cubic spline interpolation with linear interpolation for outlier values.
Applied the method to large aCGH datasets to address dye bias.

Main Results:

aCGH.Spline effectively removes a significant portion of dye bias from aCGH data.
The method is quick and efficient for large datasets.

Conclusions:

Bias removal and noise reduction positively impact the accurate detection of CNVs and CNAs.
This method enhances the reliability of aCGH data analysis.