TAFFYS: An Integrated Tool for Comprehensive Analysis of Genomic Aberrations in Tumor Samples.

Yuanning Liu,Minghui Wang,Huanqing Feng,Ao Li

doi:10.1371/journal.pone.0129835

Yuanning Liu, Minghui Wang + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0129835

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Journal: PloS one	Publication Date: Jun 25, 2015
Citations: 3	License type: CC BY 4.0

Affiliation: University of Science and Technology of China

Abstract

BackgroundTumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification.ResultsWe propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate.ConclusionsTAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data.

Highlights

Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes
The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample than other existed methods
Suppose the genotype of one SNP can be denoted with two alleles ‘A’ and ‘B’, and SNP array signals contain two measurements for each SNP: Log R Ratio (LRR) and B Allele Frequency (BAF), which denote the relative total copy number and the fraction of B allele, respectively [2,6]

Summary

Introduction

Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. In contrast with the popularity of tumor Affymetrix SNP array, the methods that are designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification

Methods

Results

Discussion

Conclusion