Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we will assume that you are happy to receive all cookies and you will not see this message again. Click 'Find out more' for information on how to change your cookie settings.

MOTIVATION: The careful normalization of array-based comparative genomic hybridization (aCGH) data is of critical importance for the accurate detection of copy number changes. The difference in labelling affinity between the two fluorophores used in aCGH-usually Cy5 and Cy3-can be observed as a bias within the intensity distributions. If left unchecked, this bias is likely to skew data interpretation during downstream analysis and lead to an increased number of false discoveries. RESULTS: In this study, we have developed aCGH.Spline, a natural cubic spline interpolation method followed by linear interpolation of outlier values, which is able to remove a large portion of the dye bias from large aCGH datasets in a quick and efficient manner. CONCLUSIONS: We have shown that removing this bias and reducing the experimental noise has a strong positive impact on the ability to detect accurately both copy number variation (CNV) and copy number alterations (CNA).

Original publication




Journal article



Publication Date





1195 - 1200


Carbocyanines, Comparative Genomic Hybridization, DNA Copy Number Variations, Fluorescent Dyes, Software