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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%...

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Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Modified screening and ranking algorithm for copy number variation detection.

Feifei Xiao¹, Xiaoyi Min¹, Heping Zhang¹

¹Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Bioinformatics (Oxford, England)

|December 28, 2014

Summary

This summary is machine-generated.

A modified screening and ranking algorithm (SaRa) enhances copy number variation (CNV) detection in high-throughput genotyping data. This robust method improves upon existing approaches for identifying genomic CNV segments.

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Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Copy number variation (CNV) involves genomic segments with variable copy numbers compared to a reference genome.
The screening and ranking algorithm (SaRa) is an efficient method for detecting multiple change-points, applicable to CNV detection.
Practical issues limit the direct application of SaRa to single nucleotide polymorphism data.

Purpose of the Study:

To address limitations of the SaRa algorithm in CNV detection using single nucleotide polymorphism data.
To develop a robust and improved SaRa method for accurate CNV identification.
To enhance the performance of CNV detection algorithms in high-throughput genotyping.

Main Methods:

Quantile normalization of original intensities to ensure robustness of the normal mean model-based SaRa.
A novel normal mixture model combined with a modified Bayesian information criterion for change-point selection and CNV segment clustering.
Implementation of the modified SaRa (modSaRa) package in R.

Main Results:

The modified SaRa demonstrated robustness in identifying change-points.
The method achieved superior performance compared to the circular binary segmentation (CBS) method in simulations.
Application to HapMap project data validated the performance of the modified SaRa in detecting CNV segments.

Conclusions:

The modified SaRa method offers theoretical and numerical improvements for CNV identification.
This enhanced algorithm provides a more reliable approach for analyzing high-throughput genotyping data.
The modSaRa package is publicly available for researchers.