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A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism

Yasuhito Nannya¹, Masashi Sanada, Kumi Nakazaki

¹Department of Hematology/Oncology, Graduate School of Medicine, University of Tokyo, Hongo, Tokyo, Japan.

Cancer Research

|July 19, 2005

Summary

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We developed Copy Number Analyzer for Affymetrix GeneChip Mapping 100K arrays (CNAG), a robust algorithm for human genome copy number analysis. CNAG improves signal-to-noise ratios for accurate detection of copy number alterations in various diseases.

Area of Science:

Genomics
Bioinformatics

Background:

Copy number analysis is crucial for understanding genomic variations and diseases.
Existing methods may have limitations in accuracy and reliability.

Purpose of the Study:

To develop a robust algorithm for high-resolution copy number analysis of the human genome.
To improve signal-to-noise ratios and enable allele-based copy number detection.

Main Methods:

Utilized high-density oligonucleotide microarrays with 116,204 single-nucleotide polymorphisms.
Implemented quadratic regressions to account for PCR product length and GC content.
Optimized reference selection, including constitutional DNA when available.

Main Results:

Achieved significant improvement in signal-to-noise ratios.

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Reduced standard deviation (SD) values from 0.67 +/- 0.12 to 0.18 +/- 0.03.

Enabled allele-based copy number detection in cancer genomes.

Conclusions:

The developed algorithm, implemented as CNAG software, provides highly reliable copy number alteration detection.
CNAG is a valuable tool for studying cancer pathogenesis and human genome complexity.
Enhancements allow for accurate estimation of copy number aberration magnitude and range.