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

Liu Y, Li A, Feng H, Wang M - PLoS ONE (2015)

Bottom Line: 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.Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China.

ABSTRACT

Background: 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. 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.

Results: We 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.

Conclusions: TAFFYS 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.

No MeSH data available.


Related in: MedlinePlus

Comparison of results from TAFFYS and GPHMM.Comparison of genome-wide copy number profiles obtained from TAFFYS using Affymetrix GenomeWide5.0 SNP array and GPHMM copy number profile from Illumina HumanCNV370v1 SNP array analysis.
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pone.0129835.g006: Comparison of results from TAFFYS and GPHMM.Comparison of genome-wide copy number profiles obtained from TAFFYS using Affymetrix GenomeWide5.0 SNP array and GPHMM copy number profile from Illumina HumanCNV370v1 SNP array analysis.

Mentions: Affymetrix has released a series of SNP array platforms for human genome genotyping. Despite the differences in chip design, resolution and signal pre-process suites, they have been successfully applied into tumor samples analysis. Here, we employ real tumor samples to assess the performance on different Affymetrix SNP arrays. Firstly, we focus on the breast cancer sample 7204, which is both analyzed by Affymetrix GenomeWide5.0 (GW5) and Illumina HumanCNV370k [5]. This dataset is available on GEO website with the accession number [GEO: GSE16400]. The Affymetrix data is applied into TAFFYS to generate the aberration profile, compared with the result of Illumina SNP array data, which is processed by tQN [22] and GPHMM [10] for signal pre-processing and detection of genomic aberration. The genome-wide copy number aberrations are shown in Fig 6. The genomic aberration results of TAFFYS show excellent concordance with those of GPHMM. Furthermore, we compare the results of lung cancer cell-line sample H1395 on another two Affymetrix platforms: Affymetrix Mapping 500K (available on GEO website with accession number [GEO: GSE17247]) and Affymetrix GW6.0. The results in S5 Fig demonstrate that TAFFYS still yields very agreeable results on both Affymetrix platforms. Taken together, these results suggest TAFFYS provides reliable detection of genomic aberration on different Affymetrix SNP arrays.


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

Liu Y, Li A, Feng H, Wang M - PLoS ONE (2015)

Comparison of results from TAFFYS and GPHMM.Comparison of genome-wide copy number profiles obtained from TAFFYS using Affymetrix GenomeWide5.0 SNP array and GPHMM copy number profile from Illumina HumanCNV370v1 SNP array analysis.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482394&req=5

pone.0129835.g006: Comparison of results from TAFFYS and GPHMM.Comparison of genome-wide copy number profiles obtained from TAFFYS using Affymetrix GenomeWide5.0 SNP array and GPHMM copy number profile from Illumina HumanCNV370v1 SNP array analysis.
Mentions: Affymetrix has released a series of SNP array platforms for human genome genotyping. Despite the differences in chip design, resolution and signal pre-process suites, they have been successfully applied into tumor samples analysis. Here, we employ real tumor samples to assess the performance on different Affymetrix SNP arrays. Firstly, we focus on the breast cancer sample 7204, which is both analyzed by Affymetrix GenomeWide5.0 (GW5) and Illumina HumanCNV370k [5]. This dataset is available on GEO website with the accession number [GEO: GSE16400]. The Affymetrix data is applied into TAFFYS to generate the aberration profile, compared with the result of Illumina SNP array data, which is processed by tQN [22] and GPHMM [10] for signal pre-processing and detection of genomic aberration. The genome-wide copy number aberrations are shown in Fig 6. The genomic aberration results of TAFFYS show excellent concordance with those of GPHMM. Furthermore, we compare the results of lung cancer cell-line sample H1395 on another two Affymetrix platforms: Affymetrix Mapping 500K (available on GEO website with accession number [GEO: GSE17247]) and Affymetrix GW6.0. The results in S5 Fig demonstrate that TAFFYS still yields very agreeable results on both Affymetrix platforms. Taken together, these results suggest TAFFYS provides reliable detection of genomic aberration on different Affymetrix SNP arrays.

Bottom Line: 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.Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China.

ABSTRACT

Background: 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. 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.

Results: We 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.

Conclusions: TAFFYS 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.

No MeSH data available.


Related in: MedlinePlus