Comparison of Normalization Methods for Defining Copy Number Variation Using Whole-genome SNP Genotyping Data

Kim, Ji Eun; Yim, Seon‐Hee; Jeong, Yong-Bok; Jung, Seong-Hyun; Xu, Haidong; Shin, Sun; Chung, Yeun‐Jun

doi:10.5808/gi.2008.6.4.231

Cited by 3 publications

(2 citation statements)

References 8 publications

(10 reference statements)

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“…To date, approximately 180,000 CNVs have been reported in the Database of Genomic Variants (DGV, see URLs). Arising from the completion of the Human Genome Project and the HapMap Project, a large number of genetic variations associated with human phenotypes or complex diseases have been identified by SNP-based genome-wide association studies (GWAS) [ 5 ]. CNVs might assist us in finding the missing heritability in GWAS.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of copy number variation detection for a SNP array platform

Zhang

et al. 2014

BMC Bioinformatics

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BackgroundCopy Number Variations (CNVs) are usually inferred from Single Nucleotide Polymorphism (SNP) arrays by use of some software packages based on given algorithms. However, there is no clear understanding of the performance of these software packages; it is therefore difficult to select one or several software packages for CNV detection based on the SNP array platform.We selected four publicly available software packages designed for CNV calling from an Affymetrix SNP array, including Birdsuite, dChip, Genotyping Console (GTC) and PennCNV. The publicly available dataset generated by Array-based Comparative Genomic Hybridization (CGH), with a resolution of 24 million probes per sample, was considered to be the “gold standard”. Compared with the CGH-based dataset, the success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the “gold standard”. Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package.ResultsSimply from the quantity of the detected CNVs, Birdsuite detected the most while GTC detected the least. We found that Birdsuite and dChip had obvious detecting bias. And GTC seemed to be inferior because of the least amount of CNVs it detected. Thereafter we investigated the detection consistency produced by one certain software package and the rest three software suits. We found that the consistency of dChip was the lowest while GTC was the highest. Compared with the CNVs detecting result of CGH, in the matching group, GTC called the most matching CNVs, PennCNV-Affy ranked second. In the non-overlapping group, GTC called the least CNVs. With regards to the reproducibility of CNV calling, larger CNVs were usually replicated better. PennCNV-Affy shows the best consistency while Birdsuite shows the poorest.ConclusionWe found that PennCNV outperformed the other three packages in the sensitivity and specificity of CNV calling. Obviously, each calling method had its own limitations and advantages for different data analysis. Therefore, the optimized calling methods might be identified using multiple algorithms to evaluate the concordance and discordance of SNP array-based CNV calling.

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Section: Introductionmentioning

confidence: 99%

Evaluation of copy number variation detection for a SNP array platform

Zhang

et al. 2014

BMC Bioinformatics

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“…Before processing the data, we took logarithm on IC 50 and normalized the cell line gene expression data using the robust multichip average [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression

et al. 2021

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Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC 50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC 50 and 0.81 for AUC. We identify common predictor genes for IC 50 and AUC, with which the performance was similar to those with genes separately found for IC 50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC 50 , 0.7945 for AUC) with 321 predictor genes.

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Identification of 1,531 cSNPs from Full-length Enriched cDNA Libraries of the Korean Native Pig Using in Silico Analysis

Oh¹,

Nguyen²,

Park³

et al. 2009

Genomics & Informatics

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Sequences from the clones of full-length enriched cDNA libraries serve as valuable resources for functional genomics related studies, genome annotation and SNP discovery. We analyzed 7,392 high-quality chromatograms (Phred value ≥30) obtained from sequencing the 5' ends of clones derived from full-length enriched cDNA libraries of Korean native pigs including brainstem, liver, cerebellum, neocortex and spleen libraries. In addition, 50,000 EST sequence trace files obtained from GenBank were combined with our sequences to identify cSNPs in silico. The process generated 11,324 contigs, of which 2,895 contigs contained at least one SNP and among them 610 contigs had a minimum of one sequence from Korean native pigs. Of 610 contigs, we randomly selected 262 contigs and performed in silico analysis for the identification of cSNPs. From the results, we identified 1,531 putative coding single nucleotide polymorphisms (cSNPs) and the SNP detection frequency was one SNP per 465 bp. A large-scale sequencing result of clones from full-length enriched cDNA libraries and identified cSNPs will serve as a useful resource to functional genomics related projects such as a pig HapMap project in the near future.

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Comparison of Normalization Methods for Defining Copy Number Variation Using Whole-genome SNP Genotyping Data

Cited by 3 publications

References 8 publications

Evaluation of copy number variation detection for a SNP array platform

Evaluation of copy number variation detection for a SNP array platform

Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression

Identification of 1,531 cSNPs from Full-length Enriched cDNA Libraries of the Korean Native Pig Using in Silico Analysis

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