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

Xie, Kai; Tian, Yuan; Yuan, Xiguo

doi:10.3389/fgene.2020.632311

Cited by 5 publications

(3 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The somatic mutation data included single nucleotide variations (SNVs) and copy number variations (CNVs). The SNV data were processed by mutect, and CNV data were processed by GISTIC algorithm as previously described [ 29 ]. Methylation data were downloaded from the LinkedOmics database ( , accessed on 24 February 2021).…”

Section: Methodsmentioning

confidence: 99%

Bioinformatics and Experimental Analyses Reveal MAP4K4 as a Potential Marker for Gastric Cancer

Zhang

Cai

Wei-feng

et al. 2022

Genes

View full text Add to dashboard Cite

Background: Gastric cancer remains the most prevalent and highly lethal disease worldwide. MAP4K4, a member of Ste20, plays an important role in various pathologies, including cancer. However, its role in gastric cancer is not yet fully elucidated. Therefore, this study aims to determine the tumor-promoting role of MAP4K4 in gastric cancer and whether it can be used as a new and reliable biomarker to predict the prognosis of gastric cancer. For this purpose, we divide the samples into high- and low-expression groups according to the expression level of MAP4K4. The association of MAP4K4 expression with prognosis is assessed using the Kaplan–Meier survival analysis. Furthermore, immune infiltration analysis using ESTIMATE is conducted to evaluate the tumor immune scores of the samples. Results: The findings reveal a significantly higher expression of MAP4K4 in tumor samples than in adjacent samples. The high-expression group was significantly enriched in tumor-related pathways, such as the PI3K-Akt signaling pathway. In addition, immune infiltration analysis revealed a positive correlation between immune scores and MAP4K4 expression. We also observed that miRNAs, such as miR-192-3p (R = −0.317, p-value 3.111 × 10−9), miR-33b-5p (R= −0.238, p-value 1.166 × 10−5), and miR-582-3p (R = −0.214, p-value 8.430 × 10−5), had potential negative regulatory effects on MAP4K4. Moreover, we identified several transcription factors, ubiquitinated proteins, and interacting proteins that might regulate MAP4K4. The relationship between MAP4K4 and DNA methylation was also identified. Finally, we verified the high expression of MAP4K4 and its effect on promoting cancer. Conclusion: MAP4K4 might be closely related to gastric cancer’s progression, invasion, and metastasis. Its high expression negatively impacts the prognosis of gastric cancer patients. This suggests MAP4K4 as an important prognostic factor for gastric cancer and could be regarded as a new potential prognostic detection and therapeutic target.

show abstract

Section: Methodsmentioning

confidence: 99%

Bioinformatics and Experimental Analyses Reveal MAP4K4 as a Potential Marker for Gastric Cancer

Zhang

Cai

Wei-feng

et al. 2022

Genes

View full text Add to dashboard Cite

show abstract

“…For example, CNV-LOF [22], CNV-KOF [23], and IhybCNV [24] adopt different outlier factors to calculate anomaly scores and determine CNVs by applying a boxplot or binary clustering model. Along with the development of machine learning, models based on classification, regression, neural networks, or clustering are adopted by CNV detection methods, including AluScanCNV2 [25], CNV_IFTV [26], CNV-RF [27], and dpCNV [28], and so on. However, most such types of methods are not able to locate the precise breakpoints of CNVs unless specific treatment is taken.…”

Section: Introductionmentioning

confidence: 99%

CNVbd: A Method for Copy Number Variation Detection and Boundary Search

Lan,

Liao,

Haque

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

Copy number variation (CNV) has been increasingly recognized as a type of genomic/genetic variation that plays a critical role in driving human diseases and genomic diversity. CNV detection and analysis from cancer genomes could provide crucial information for cancer diagnosis and treatment. There still remain considerable challenges in the control-free calling of CNVs accurately in cancer analysis, although advances in next-generation sequencing (NGS) technology have been inspiring the development of various computational methods. Herein, we propose a new read-depth (RD)-based approach, called CNVbd, to explore CNVs from single tumor samples of NGS data. CNVbd assembles three statistics drawn from the density peak clustering algorithm and isolation forest algorithm based on the denoised RD profile and establishes a back propagation neural network model to predict CNV bins. In addition, we designed a revision process and a boundary search algorithm to correct the false-negative predictions and refine the CNV boundaries. The performance of the proposed method is assessed on both simulation data and real sequencing datasets. The analysis shows that CNVbd is a very competitive method and can become a robust and reliable tool for analyzing CNVs in the tumor genome.

show abstract

“…The advantage of this strategy is that it can in principle predict copy number gain and loss of any size, but its drawback is the low resolution of breakpoint detection. A large number of CNV detection methods have been developed based on RD strategy, including CNV-LOF ( Yuan et al, 2021 ), SeqCNV ( Chen et al, 2017 ), BIC-seq2 ( Xi et al, 2016 ), dpCNV ( Xie et al, 2021 ), iCopyDav ( Dharanipragada et al, 2018 ), CNVnator ( Abyzov et al, 2011 ), SPCNV ( Liu et al, 2023 ), CNVkit ( Talevich et al, 2016 ), among others. CNV-LOF performs successive and non-overlapping divisions of RD profiles to form a set of RD segments, and performs the cyclic binary segmentation (CBS) algorithm ( Venkatraman and Olshen, 2007 ) on each segment.…”

Section: Introductionmentioning

confidence: 99%

Detection of copy number variations based on a local distance using next-generation sequencing data

Liu,

Yang,

2023

Front. Genet.

View full text Add to dashboard Cite

As one of the main types of structural variation in the human genome, copy number variation (CNV) plays an important role in the occurrence and development of human cancers. Next-generation sequencing (NGS) technology can provide base-level resolution, which provides favorable conditions for the accurate detection of CNVs. However, it is still a very challenging task to accurately detect CNVs from cancer samples with different purity and low sequencing coverage. Local distance-based CNV detection (LDCNV), an innovative computational approach to predict CNVs using NGS data, is proposed in this work. LDCNV calculates the average distance between each read depth (RD) and its k nearest neighbors (KNNs) to define the distance of KNNs of each RD, and the average distance between the KNNs for each RD to define their internal distance. Based on the above definitions, a local distance score is constructed using the ratio between the distance of KNNs and the internal distance of KNNs for each RD. The local distance scores are used to fit a normal distribution to evaluate the significance level of each RDS, and then use the hypothesis test method to predict the CNVs. The performance of the proposed method is verified with simulated and real data and compared with several popular methods. The experimental results show that the proposed method is superior to various other techniques. Therefore, the proposed method can be helpful for cancer diagnosis and targeted drug development.

show abstract

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

Cited by 5 publications

References 48 publications

Bioinformatics and Experimental Analyses Reveal MAP4K4 as a Potential Marker for Gastric Cancer

Bioinformatics and Experimental Analyses Reveal MAP4K4 as a Potential Marker for Gastric Cancer

CNVbd: A Method for Copy Number Variation Detection and Boundary Search

Detection of copy number variations based on a local distance using next-generation sequencing data

Contact Info

Product

Resources

About