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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%...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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.

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Related Experiment Video

Updated: Apr 18, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

CODEX: a normalization and copy number variation detection method for whole exome sequencing.

Yuchao Jiang¹, Derek A Oldridge², Sharon J Diskin³

¹Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Nucleic Acids Research

|January 26, 2015

Summary

This summary is machine-generated.

We developed CODEX, a new method for accurately detecting copy number variation (CNV) from whole exome sequencing data. CODEX effectively removes biases and noise, improving genomic variation analysis in human diseases.

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

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Published on: October 18, 2013

Area of Science:

Genomics
Bioinformatics
Human Disease Research

Background:

High-throughput sequencing of DNA coding regions is crucial for studying human diseases.
Detecting copy number variation (CNV) from exome sequencing is challenging due to inherent biases and artifacts.

Purpose of the Study:

To introduce CODEX, a novel normalization and CNV calling procedure for whole exome sequencing data.
To improve the accuracy of CNV detection by addressing biases in sequencing data.

Main Methods:

CODEX utilizes a Poisson latent factor model to correct for GC content, exon capture, and amplification biases.
A Poisson likelihood-based recursive segmentation procedure is employed to model count-based exome sequencing data.
The method incorporates cross-sample normalization to reduce noise more effectively than traditional matched-normal normalization.

Main Results:

CODEX demonstrates higher accuracy compared to existing methods on HapMap samples and validation datasets.
The procedure successfully identifies a rare somatic CNV in the ATRX gene in neuroblastoma samples.
CODEX's segmentation procedure effectively detects CNVs with complex nested structures.

Conclusions:

CODEX offers a more accurate and robust approach for CNV detection in whole exome sequencing data.
The method enhances the analysis of genomic variation for human disease studies.
CODEX improves the characterization of both germline and somatic CNVs.