Dataset Information

The comparison of the performance of four whole genome amplification kits on ion proton platform in copy number variation detection.

ABSTRACT: With the development and clinical application of genomics, more and more concern is focused on single-cell sequencing. In the process of single-cell sequencing, whole genome amplification is a key step to enrich sample DNA. Previous studies have compared the performance of different whole genome amplification (WGA) strategies on Illumina sequencing platforms, but there is no related research aimed at Ion Proton platform, which is also a popular next-generation sequencing platform. Here by amplifying cells from six cell lines with different karyotypes, we estimated the data features of four common commercial WGA kits (PicoPLEX WGA Kit, GenomePlex Single Cell Whole Genome Amplification Kit, MALBAC Single Cell Whole Genome Amplification Kit, and REPLI-g Single Cell Kit), including median absolute pairwise difference, uniformity, reproducibility, and fidelity, and examined their performance of copy number variation detection. The results showed that both MALBAC and PicoPLEX could yield high-quality data and had high reproducibility and fidelity; and as for uniformity, PicoPLEX was slightly superior to MALBAC.

SUBMITTER: Zhang X

PROVIDER: S-EPMC6434089 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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The comparison of the performance of four whole genome amplification kits on ion proton platform in copy number variation detection.

Zhang Xinyi X Liang Bo B Xu Xiaoyan X Zhou Feifei F Kong Lingyin L Shen Jingjing J Xia Yingying Y Xuan Liming L Mao Yan Y Xue Yongfeng Y Liu Caixia C Tan Jichun J

Bioscience reports 20170707 4

With the development and clinical application of genomics, more and more concern is focused on single-cell sequencing. In the process of single-cell sequencing, whole genome amplification is a key step to enrich sample DNA. Previous studies have compared the performance of different whole genome amplification (WGA) strategies on Illumina sequencing platforms, but there is no related research aimed at Ion Proton platform, which is also a popular next-generation sequencing platform. Here by amplif ...[more]

PMID: 28572171

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Project description: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.

Project description:BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Dataset Information

The comparison of the performance of four whole genome amplification kits on ion proton platform in copy number variation detection.

Publications

The comparison of the performance of four whole genome amplification kits on ion proton platform in copy number variation detection.

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