alexa Estimation and assessment of raw copy numbers at the single locus level.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Data Mining in Genomics & Proteomics

Author(s): Bengtsson H, Irizarry R, Carvalho B, Speed TP

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Abstract MOTIVATION: Although copy-number aberrations are known to contribute to the diversity of the human DNA and cause various diseases, many aberrations and their phenotypes are still to be explored. The recent development of single-nucleotide polymorphism (SNP) arrays provides researchers with tools for calling genotypes and identifying chromosomal aberrations at an order-of-magnitude greater resolution than possible a few years ago. The fundamental problem in array-based copy-number (CN) analysis is to obtain CN estimates at a single-locus resolution with high accuracy and precision such that downstream segmentation methods are more likely to succeed. RESULTS: We propose a preprocessing method for estimating raw CNs from Affymetrix SNP arrays. Its core utilizes a multichip probe-level model analogous to that for high-density oligonucleotide expression arrays. We extend this model by adding an adjustment for sequence-specific allelic imbalances such as cross-hybridization between allele A and allele B probes. We focus on total CN estimates, which allows us to further constrain the probe-level model to increase the signal-to-noise ratio of CN estimates. Further improvement is obtained by controlling for PCR effects. Each part of the model is fitted robustly. The performance is assessed by quantifying how well raw CNs alone differentiate between one and two copies on Chromosome X (ChrX) at a single-locus resolution (27kb) up to a 200kb resolution. The evaluation is done with publicly available HapMap data. AVAILABILITY: The proposed method is available as part of an open-source R package named aroma.affymetrix. Because it is a bounded-memory algorithm, any number of arrays can be analyzed. This article was published in Bioinformatics and referenced in Journal of Data Mining in Genomics & Proteomics

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