Robust smooth segmentation approach for array CGH data analysis

Huang, Jian; Gusnanto, Arief; O’Sullivan, Kathleen; Staaf, Johan; Borg, Åke; Pawitan, Yudi

doi:10.1093/bioinformatics/btm359

Cited by 35 publications

(33 citation statements)

References 23 publications

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“…Sample-specific logR data were exported from BeadStudio 2.0 for the 23 EAC biopsies with >50% tumor cells, as described above. R scripts were used to apply a smoothed binary segregation algorithm (19) to these data for each sample across each chromosome. These data were then compiled to generate median (smoothed) logR values across each data point.…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Copy Number Analysis in Esophageal Adenocarcinoma Using High-Density Single-Nucleotide Polymorphism Arrays

Nancarrow

Handoko²,

Smithers³

et al. 2008

Cancer Research

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We applied whole-genome single-nucleotide polymorphism arrays to define a comprehensive genetic profile of 23 esophageal adenocarcinoma (EAC) primary tumor biopsies based on loss of heterozygosity (LOH) and DNA copy number changes. Alterations were common, averaging 97 (range, 23-208) per tumor. LOH and gains averaged 33 (range, 3-83) and 31 (range, 11-73) per tumor, respectively. Copy neutral LOH events averaged 27 (range, 7-57) per EAC. We noted 126 homozygous deletions (HD) across the EAC panel (range, 0-11 in individual tumors). Frequent HDs within FHIT (17 of 23), WWOX (8 of 23), and DMD (6 of 23) suggest a role for common fragile sites or genomic instability in EAC etiology. HDs were also noted for known tumor suppressor genes (TSG), including CDKN2A, CDKN2B, SMAD4, and GALR1, and identified PDE4D and MGC48628 as potentially novel TSGs. All tumors showed LOH for most of chromosome 17p, suggesting that TSGs other than TP53 may be targeted. Frequent gains were noted around MYC (13 of 23), BCL9 (12 of 23), CTAGE1 (14 of 23), and ZNF217 (12 of 23). Thus, we have confirmed previous reports indicating frequent changes to FHIT, CDKN2A, TP53, and MYC in EAC and identified additional genes of interest. Meta-analysis of previous genome-wide EAC studies together with the data presented here highlighted consistent regions of gain on 8q, 18q, and 20q and multiple LOH regions on 4q, 5q, 17p, and 18q, suggesting that more than one gene may be targeted on each of these chromosome arms. The focal gains and deletions documented here are a step toward identifying the key genes involved in EAC development.

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Section: Methodsmentioning

confidence: 99%

Genome-Wide Copy Number Analysis in Esophageal Adenocarcinoma Using High-Density Single-Nucleotide Polymorphism Arrays

Nancarrow

Handoko²,

Smithers³

et al. 2008

Cancer Research

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“…Log2 ratios were normalised for spatial and intensitydependent biases using a two-dimensional LOESS regression followed by a BAC-dependent bias correction as previously described [14,20]. Polymorphic BACs identified in an analysis of 50 male:female and female:female hybridisations were filtered out.…”

Section: Microarray-based Comparative Genomic Hybridisationmentioning

confidence: 99%

An integrative genomic and transcriptomic analysis reveals molecular pathways and networks regulated by copy number aberrations in basal-like, HER2 and luminal cancers

Natrajan

Weigelt²,

Mackay³

et al. 2009

Breast Cancer Res Treat

144

146

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“…And most of the approaches to detect CNVs with short-read sequence use an alignment method, which requires long computational time in building index and matching to reference. Alternatively, a few fast and efficient methods for aCGH have been published [17], [18], [19]. A more practical solution to detect small-sized CNVs is to shorten the length of each probe, in other words, to increase the resolution of the aCGH.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Sample Based Method for Identifying Common CNVs in Normal Human Genomic Structure Using High-Resolution aCGH Data

et al. 2011

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BackgroundIt is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample.Methodology and Principal FindingsWe developed a multi-sample-based genomic variations detector (MGVD) that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs); CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR).Conclusions and SignificanceWe designed a specialized algorithm to detect common CNVs from extremely high-resolution multi-sample aCGH data. MGVD showed high sensitivity and a low false discovery rate for a simulated data set, and outperformed most current methods when real, high-resolution HapMap datasets were analyzed. MGVD also had the fastest runtime compared to the other algorithms evaluated when actual, high-resolution aCGH data were analyzed. The CNVZs identified by MGVD can be used in association studies for revealing relationships between phenotypes and genomic aberrations. Our algorithm was developed with standard C++ and is available in Linux and MS Windows format in the STL library. It is freely available at: http://embio.yonsei.ac.kr/~Park/mgvd.php.

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Robust smooth segmentation approach for array CGH data analysis

Abstract: The R package smoothseg to perform smooth segmentation is available from http://www.meb.ki.se/~yudpaw.

Cited by 35 publications

References 23 publications

Genome-Wide Copy Number Analysis in Esophageal Adenocarcinoma Using High-Density Single-Nucleotide Polymorphism Arrays

Genome-Wide Copy Number Analysis in Esophageal Adenocarcinoma Using High-Density Single-Nucleotide Polymorphism Arrays

An integrative genomic and transcriptomic analysis reveals molecular pathways and networks regulated by copy number aberrations in basal-like, HER2 and luminal cancers

A Multi-Sample Based Method for Identifying Common CNVs in Normal Human Genomic Structure Using High-Resolution aCGH Data

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