Dataset Information

PSCC: sensitive and reliable population-scale copy number variation detection method based on low coverage sequencing.

ABSTRACT:

Background

Copy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method.

Methodology/principal findings

In our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (?2×) and ultra LCS (?0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS.

Conclusions/significance

Our study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.

SUBMITTER: Li X

PROVIDER: S-EPMC3897425 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

PSCC: sensitive and reliable population-scale copy number variation detection method based on low coverage sequencing.

Li Xuchao X Chen Shengpei S Xie Weiwei W Vogel Ida I Choy Kwong Wai KW Chen Fang F Christensen Rikke R Zhang Chunlei C Ge Huijuan H Jiang Haojun H Yu Chang C Huang Fang F Wang Wei W Jiang Hui H Zhang Xiuqing X

PloS one 20140121 1

<h4>Background</h4>Copy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing ...[more]

PMID: 24465483

Similar Datasets

Project description:BACKGROUND:Detection of DNA copy number alterations (CNAs) is critical to understand genetic diversity, genome evolution and pathological conditions such as cancer. Cancer genomes are plagued with widespread multi-level structural aberrations of chromosomes that pose challenges to discover CNAs of different length scales, and distinct biological origins and functions. Although several computational tools are available to identify CNAs using read depth (RD) signal, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. Additionally, RD signal is affected by overdispersion-driven biases at low coverage, which significantly inflate false detection of CNA regions. RESULTS:We have developed CNAtra framework to hierarchically discover and classify 'large-scale' and 'focal' copy number gain/loss from a single whole-genome sequencing (WGS) sample. CNAtra first utilizes a multimodal-based distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We implemented Savitzky-Golay smoothing filter and Modified Varri segmentation to capture the change points of the RD signal. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy numbers. Next, we identified focal alterations in each large segment using coverage-based thresholding to mitigate the adverse effects of signal variations. Using cancer cell lines and patient datasets, we confirmed CNAtra's ability to detect and distinguish the segmental aneuploidies and focal alterations. We used realistic simulated data for benchmarking the performance of CNAtra against other single-sample detection tools, where we artificially introduced CNAs in the original cancer profiles. We found that CNAtra is superior in terms of precision, recall and f-measure. CNAtra shows the highest sensitivity of 93 and 97% for detecting large-scale and focal alterations respectively. Visual inspection of CNAs revealed that CNAtra is the most robust detection tool for low-coverage cancer data. CONCLUSIONS:CNAtra is a single-sample CNA detection tool that provides an analytical and visualization framework for CNA profiling without relying on any reference control. It can detect chromosome-level segmental aneuploidies and high-confidence focal alterations, even from low-coverage data. CNAtra is an open-source software implemented in MATLAB®. It is freely available at https://github.com/AISKhalil/CNAtra.

Dataset Information

PSCC: sensitive and reliable population-scale copy number variation detection method based on low coverage sequencing.

Background

Methodology/principal findings

Conclusions/significance

Publications

PSCC: sensitive and reliable population-scale copy number variation detection method based on low coverage sequencing.

Similar Datasets

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