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.

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

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

Sort by

Same author

Cuscuta chinensis Lam. extract alleviates ovariectomy-induced osteoporosis by inhibiting VCP-mediated METTL3 protein degradation.

Journal of molecular histology·2026

Same author

A dual-pronged strategy: multi-bioactive nanoplatform synergizes ROS antidote and iron stabilization to protect against liver ischemia-reperfusion injury.

Journal of nanobiotechnology·2026

Same author

A natural remedy: Kidney-targeting nanoplatform for photoacoustic imaging-guided three-in-one theranostics of rhabdomyolysis-induced acute kidney injury.

Materials today. Bio·2026

Same author

Hyaluronic acid engineered melanin-MOF nanoreactor synergistically remodeling redox and immune homeostasis for targeted acute lung injury therapy.

Materials today. Bio·2026

Same author

Urinary microenvironment-degradable nanocapsules for traceable therapy of diabetic nephropathy.

Materials today. Bio·2026

Same author

Early characterization of pregnancy glycemic traits in gestational diabetes mellitus by plasma cell-free mRNA and non-coding RNA.

Epigenetics·2026

Search research articles

Related Experiment Video

Updated: Oct 9, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

CNV-P: a machine-learning framework for predicting high confident copy number variations.

Taifu Wang¹, Jinghua Sun^1,2, Xiuqing Zhang^1,2,3

¹BGI-Shenzhen, Shenzhen, China.

|December 17, 2021

Summary

This study introduces CNV-P, a machine learning tool that significantly reduces false positives in copy-number variant (CNV) detection from genome sequencing. CNV-P enhances the accuracy of identifying genetic disorders for improved research and clinical diagnosis.

Keywords:

Copy number variant Genome sequencing Machine learning

More Related Videos

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

Published on: August 17, 2022

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

Related Experiment Videos

Last Updated: Oct 9, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

Published on: August 17, 2022

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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Copy-number variants (CNVs) are a significant cause of genetic disorders.
Accurate CNV detection from genome sequencing is crucial for disease research.
Existing CNV detection software suffers from high false-positive rates.

Purpose of the Study:

To develop a novel post-processing approach, CNV-P, for improving CNV detection accuracy.
To reduce false-positive fragments identified by existing CNV detection tools.
To enhance the reliability of CNV analysis in genetic disorder research.

Main Methods:

Developed a machine-learning framework named CNV-P.
Utilized features such as read depth (RD), split reads (SR), and read pairs (RP) around putative CNV fragments.
Trained a classifier to distinguish true CNVs from false positives.

Main Results:

Achieved over 90% precision and 85% recall rates in classifying CNVs on real biological datasets.
Demonstrated significant performance improvement compared to state-of-the-art algorithms.
Showed robustness of CNV-P across different CNV sizes and sequencing platforms.

Conclusions:

The CNV-P framework effectively classifies high-confidence CNVs.
This advancement can improve both basic research and clinical diagnosis of genetic diseases.
Enhanced accuracy in CNV detection facilitates a better understanding of genetic disorder etiology.