Common Copy Number Variation Detection From Multiple Sequenced Samples

Duan, Junbo; Deng, Hong−Wen; Wang, Yuping

doi:10.1109/tbme.2013.2292588

Cited by 15 publications

(3 citation statements)

References 57 publications

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“…A total of 2,079,579 were identified using CNVnator–LUMPY. Utilization of data from multiple samples has been shown to improve CNV detection (Klambauer et al, 2012; Duan et al, 2014). Therefore, as a further error correction step, CNVs were also detected using a multiple sample read depth caller, cn.MOPS (695,741 CNV identified).…”

Section: Resultsmentioning

confidence: 99%

A Survey of Copy Number Variation in the Porcine Genome Detected From Whole-Genome Sequence

et al. 2019

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Copy number variations (CNVs) are gains and losses of large regions of genomic sequence between individuals of a species. Although CNVs have been associated with various phenotypic traits in humans and other species, the extent to which CNVs impact phenotypic variation remains unclear. In swine, as well as many other species, relatively little is understood about the frequency of CNV in the genome, sizes, locations, and other chromosomal properties. In this work, we identified and characterized CNV by utilizing whole-genome sequence from 240 members of an intensely phenotyped experimental swine herd at the U.S. Meat Animal Research Center (USMARC). These animals included all 24 of the purebred founding boars (12 Duroc and 12 Landrace), 48 of the founding Yorkshire-Landrace composite sows, 109 composite animals from generations 4 through 9, 29 composite animals from generation 15, and 30 purebred industry boars (15 Landrace and 15 Yorkshire) used as sires in generations 10 through 15. Using a combination of split reads, paired-end mapping, and read depth approaches, we identified a total of 3,538 copy number variable regions (CNVRs), including 1,820 novel CNVRs not reported in previous studies. The CNVRs covered 0.94% of the porcine genome and overlapped 1,401 genes. Gene ontology analysis identified that CNV-overlapped genes were enriched for functions related to organism development. Additionally, CNVRs overlapped with many known quantitative trait loci (QTL). In particular, analysis of QTL previously identified in the USMARC herd showed that CNVRs were most overlapped with reproductive traits, such as age of puberty and ovulation rate, and CNVRs were significantly enriched for reproductive QTL.

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Section: Resultsmentioning

confidence: 99%

A Survey of Copy Number Variation in the Porcine Genome Detected From Whole-Genome Sequence

et al. 2019

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“…The RD based approach is generally implemented through the following four steps ( Duan et al, 2014 ; Yuan et al, 2019a ): (1) mapping sequencing reads to a reference genome and extracting a read count profile, (2) dividing the genome into non-overlapping bins and calculating a RD value for each bin based on the read count profile, (3) making normalization and correction to the RD values, and (4) analyzing the corrected RD values to declare CNVs. The theoretical assumption underlying the RD based approach is that the RD value of one bin or one region is roughly related to its corresponding copy number, i.e., the larger the RD value, the larger the copy number, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

A Density Peak-Based Method to Detect Copy Number Variations From Next-Generation Sequencing Data

2021

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Copy number variation (CNV) is a common type of structural variations in human genome and confers biological meanings to human complex diseases. Detection of CNVs is an important step for a systematic analysis of CNVs in medical research of complex diseases. The recent development of next-generation sequencing (NGS) platforms provides unprecedented opportunities for the detection of CNVs at a base-level resolution. However, due to the intrinsic characteristics behind NGS data, accurate detection of CNVs is still a challenging task. In this article, we propose a new density peak-based method, called dpCNV, for the detection of CNVs from NGS data. The algorithm of dpCNV is designed based on density peak clustering algorithm. It extracts two features, i.e., local density and minimum distance, from sequencing read depth (RD) profile and generates a two-dimensional data. Based on the generated data, a two-dimensional null distribution is constructed to test the significance of each genome bin and then the significant genome bins are declared as CNVs. We test the performance of the dpCNV method on a number of simulated datasets and make comparison with several existing methods. The experimental results demonstrate that our proposed method outperforms others in terms of sensitivity and F1-score. We further apply it to a set of real sequencing samples and the results demonstrate the validity of dpCNV. Therefore, we expect that dpCNV can be used as a supplementary to existing methods and may become a routine tool in the field of genome mutation analysis.

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“…Currently, there are a few tools which are capable of the simultaneous analysis of several sequenced samples 37 , 42 , 43 . However, a major drawback of these tools is relying only on read depth data which results in suffering from a low power or a high false positive rate, due to the large noise in read depth signals.…”

Section: Introductionmentioning

confidence: 99%

MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples

Malekpour

Pezeshk

Sadeghi

2018

Sci Rep

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Currently a few tools are capable of detecting genome-wide Copy Number Variations (CNVs) based on sequencing of multiple samples. Although aberrations in mate pair insertion sizes provide additional hints for the CNV detection based on multiple samples, the majority of the current tools rely only on the depth of coverage. Here, we propose a new algorithm (MSeq-CNV) which allows detecting common CNVs across multiple samples. MSeq-CNV applies a mixture density for modeling aberrations in depth of coverage and abnormalities in the mate pair insertion sizes. Each component in this mixture density applies a Binomial distribution for modeling the number of mate pairs with aberration in the insertion size and also a Poisson distribution for emitting the read counts, in each genomic position. MSeq-CNV is applied on simulated data and also on real data of six HapMap individuals with high-coverage sequencing, in 1000 Genomes Project. These individuals include a CEU trio of European ancestry and a YRI trio of Nigerian ethnicity. Ancestry of these individuals is studied by clustering the identified CNVs. MSeq-CNV is also applied for detecting CNVs in two samples with low-coverage sequencing in 1000 Genomes Project and six samples form the Simons Genome Diversity Project.

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Common Copy Number Variation Detection From Multiple Sequenced Samples

Cited by 15 publications

References 57 publications

A Survey of Copy Number Variation in the Porcine Genome Detected From Whole-Genome Sequence

A Survey of Copy Number Variation in the Porcine Genome Detected From Whole-Genome Sequence

A Density Peak-Based Method to Detect Copy Number Variations From Next-Generation Sequencing Data

MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples

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