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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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%...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Genome Copying Errors02:46

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.
Single Nucleotide Polymorphisms-SNPs01:05

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,...

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Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Statistical challenges associated with detecting copy number variations with next-generation sequencing.

Shu Mei Teo1, Yudi Pawitan, Chee Seng Ku

  • 1Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597.

Bioinformatics (Oxford, England)
|September 4, 2012
PubMed
Summary
This summary is machine-generated.

Detecting copy number variations (CNVs) using next-generation sequencing (NGS) is challenging due to genomic complexity and short reads. This review highlights biases in CNV detection methods, including mappability and GC-content, and discusses quality control impacts.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) data analysis for copy number variations (CNVs) detection is an emerging field with no standardized protocols.
  • Existing CNV detection methods (depth of coverage, read-pair, split-read, assembly-based) face challenges due to genomic complexity and short NGS read lengths.

Purpose of the Study:

  • To review and discuss potential biases in CNV detection across four primary NGS-based methods.
  • To investigate specific challenges including read mappability, GC-content bias, read quality control, and the identification of duplications.

Main Methods:

  • Review of existing literature and algorithms for CNV detection using NGS.
  • Analysis of real data from the 1000 Genomes Project, focusing on depth of coverage (DOC) data.
  • Demonstration of GC-correction algorithms and assessment of DOC algorithm sensitivity before and after read quality control.

Main Results:

  • Identified and discussed key areas of bias in CNV detection methods: mappability, GC-content, read quality, and duplication identification.
  • Illustrated the impact of reads in repetitive regions on CNV detection accuracy.
  • Demonstrated the effectiveness of GC-correction algorithms and the improved sensitivity of DOC algorithms post-quality control.

Conclusions:

  • CNV detection from NGS data remains challenging, with significant biases affecting accuracy across all major methods.
  • Quality control of reads and appropriate bias correction methods (e.g., GC-content correction) are crucial for reliable CNV detection.
  • Duplications present a greater detection challenge compared to deletions, necessitating further methodological development.