Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

Genome Copying Errors

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their survival. Therefore, the copying errors are checked and repaired at three levels.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026

Same author

Transport discovery of emerging robust helical surface States in Z2 = 0 systems.

Physical review letters·2014

Same author

Aromatic triazole foldamers induced by C-H···X (X = F, Cl) intramolecular hydrogen bonding.

The Journal of organic chemistry·2014

Same author

Human heat shock protein-specific cytotoxic T lymphocytes display potent antitumour immunity in multiple myeloma.

British journal of haematology·2014

Same author

ATG-Fresenius S combined with cyclosporine a: an effective immunosuppressive therapy for children with aplastic anemia.

Journal of pediatric hematology/oncology·2014

Same author

Priming of Toll-like receptor 4 pathway in mesenchymal stem cells increases expression of B cell activating factor.

Biochemical and biophysical research communications·2014

Same journal

Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.

Interdisciplinary sciences, computational life sciences·2026

Same journal

DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction.

Interdisciplinary sciences, computational life sciences·2026

Same journal

STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.

Interdisciplinary sciences, computational life sciences·2026

Same journal

Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.

Interdisciplinary sciences, computational life sciences·2026

Same journal

Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.

Interdisciplinary sciences, computational life sciences·2026

Same journal

ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.

Interdisciplinary sciences, computational life sciences·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 6, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

SSLCNV: A Semi-supervised Learning Framework for Accurate Copy Number Variation Detection.

Ruchao Du¹, Jinxin Dong², Hua Jiang¹

¹School of Computer Science and Technology, Liaocheng University, Liaocheng, 252000, China.

Interdisciplinary Sciences, Computational Life Sciences

|November 27, 2025

Summary

This summary is machine-generated.

SSLCNV improves copy number variation (CNV) detection by combining multiple tools and semi-supervised learning. This novel method enhances accuracy and robustness, especially in complex genomic data.

Keywords:

Copy number variation DBSCAN Next-generation sequencing Semi-supervised learning

More Related Videos

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Related Experiment Videos

Last Updated: Jan 6, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Copy number variation (CNV) is a key structural variation impacting genetic diversity and disease.
Existing CNV detection tools have limitations in sensitivity, breakpoint resolution, and robustness in complex sequencing data.
There is a need for improved CNV detection methods that leverage existing tool strengths.

Purpose of the Study:

To develop a novel semi-supervised learning framework (SSLCNV) for accurate CNV detection.
To enhance CNV detection accuracy and robustness, particularly in complex sequencing environments.
To improve the utilization of existing CNV detection tools.

Main Methods:

SSLCNV employs consensus-based pseudo-labeling by intersecting predictions from four representative CNV tools.
Density-based clustering with a novel constraint z-score is used to enhance accuracy.
The framework effectively detects CNVs from partially labeled and unlabeled data.

Main Results:

SSLCNV consistently achieves superior F1-scores compared to existing tools across various sequencing depths and tumor purities.
The method demonstrates robust performance under low-coverage conditions, improving recall without significant precision loss.
Comprehensive evaluations on simulated and real datasets validate SSLCNV's effectiveness.

Conclusions:

SSLCNV provides a scalable and accurate solution for CNV detection.
The framework is particularly advantageous for complex genomic backgrounds and low-coverage data.
SSLCNV represents a significant advancement in leveraging multiple tools for improved CNV identification.