WaveDec: A Wavelet Approach to Identify Both Shared and Individual Patterns of Copy-Number Variations

Cai, Hongmin; Chen, Pei-Hua; Chen, Jiazhou; Cai, Jiulun; Song, Yan; Han, Guoqiang

doi:10.1109/tbme.2017.2769677

Cited by 13 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, various computational methods have already been developed for analyzing each type of the data sets. For example, aiming at aCGH data, classic methods include fastRPCA (Nowak et al, 2011), PLA (Zhou et al, 2014), WaveDec (Cai et al, 2018), and graCNV (Auer et al, 2007). Meanwhile, aiming at SNP array data, famous methods include GISTIC (Beroukhim et al, 2007), STAC (Diskin et al, 2006), SAIC (Yuan et al, 2012b), and AISAIC (Zhang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

show abstract

“…Popular methods of such category include CODEX [11], panelcn.MOPS [12], DCC [7], WaveDec [13], and HetRCNA [14]. The other two categories of modes are primarily used for the detection of CNVs with the purpose of analyzing genetic diversity and seeking out mutated genes in individuals.…”

Section: Introductionmentioning

confidence: 99%

CIRCNV: Detection of CNVs Based on a Circular Profile of Read Depth from Sequencing Data

Zhao

Tian

et al. 2021

Biology

View full text Add to dashboard Cite

Copy number variation (CNV) is a common type of structural variation in the human genome. Accurate detection of CNVs from tumor genomes can provide crucial information for the study of tumor genesis and cancer precision diagnosis. However, the contamination of normal genomes in tumor genomes and the crude profiles of the read depth make such a task difficult. In this paper, we propose an alternative approach, called CIRCNV, for the detection of CNVs from sequencing data. CIRCNV is an extension of our previously developed method CNV-LOF, which uses local outlier factors to predict CNVs. Comparatively, CIRCNV can be performed on individual tumor samples and has the following two new features: (1) it transfers the read depth profile from a line shape to a circular shape via a polar coordinate transformation, in order to improve the efficiency of the read depth (RD) profile for the detection of CNVs; and (2) it performs a second round of CNV declaration based on the truth circular RD profile, which is recovered by estimating tumor purity. We test and validate the performance of CIRCNV based on simulation and real sequencing data and perform comparisons with several peer methods. The results demonstrate that CIRCNV can obtain superior performance in terms of sensitivity and precision. We expect that our proposed method will be a supplement to existing methods and become a routine tool in the field of variation analysis of tumor genomes.

show abstract

“…However, when facing with relatively low-coverage-depth data, the false-positive rate of CNVnator is not easy to control due to the influence from artifacts such as GC-content bias and uneven distribution of reads, although the CNVnator method has dealt with the GC bias in a reasonable way. Other popular RD-based methods include ReadDepth (Miller et al, 2011), XCAVATOR (Magi et al, 2017), Wavedec (Cai et al, 2018), seqCNV (Chen et al, 2017), iCopyDAV (Dharanipragada et al, 2018), GROM-RD (Smith et al, 2015), CONDEL (Yuan et al, 2018a), CLImAT (Yu et al, 2014), CNV_IFTV (Yuan et al, 2019b), m-HMM (Wang et al, 2014), DCC (Yuan et al, 2018c), CNV-seq (Xie and Tammi, 2009), and FREEC (Boeva et al, 2012). The characteristics of the existing methods are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Zhao

Huang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Copy number variation (CNV) is a very important phenomenon in tumor genomes and plays a significant role in tumor genesis. Accurate detection of CNVs has become a routine and necessary procedure for a deep investigation of tumor cells and diagnosis of tumor patients. Next-generation sequencing (NGS) technique has provided a wealth of data for the detection of CNVs at base-pair resolution. However, such task is usually influenced by a number of factors, including GC-content bias, sequencing errors, and correlations among adjacent positions within CNVs. Although many existing methods have dealt with some of these artifacts by designing their own strategies, there is still a lack of comprehensive consideration of all the factors. In this paper, we propose a new method, MFCNV, for an accurate detection of CNVs from NGS data. Compared with existing methods, the characteristics of the proposed method include the following: (1) it makes a full consideration of the intrinsic correlations among adjacent positions in the genome to be analyzed, (2) it calculates read depth, GC-content bias, base quality, and correlation value for each genome bin and combines them as multiple features for the evaluation of genome bins, and (3) it addresses the joint effect among the factors via training a neural network algorithm for the prediction of CNVs. We test the performance of the MFCNV method by using simulation and real sequencing data and make comparisons with several peer methods. The results demonstrate that our method is superior to other methods in terms of sensitivity, precision, and F1-score and can detect many CNVs that other methods have not discovered. MFCNV is expected to be a complementary tool in the analysis of mutations in tumor genomes and can be extended to be applied to the analysis of single-cell sequencing data.

show abstract

WaveDec: A Wavelet Approach to Identify Both Shared and Individual Patterns of Copy-Number Variations

Abstract: Both the shared and individual patterns can be uniquely characterized as well as effectively decomposed within the wavelet space.

Cited by 13 publications

References 35 publications

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

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

CIRCNV: Detection of CNVs Based on a Circular Profile of Read Depth from Sequencing Data

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Contact Info

Product

Resources

About