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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,...

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Related Experiment Video

Updated: Jun 20, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Iterative two-pass algorithm for missing data imputation in SNP arrays.

Christine Sinoquet¹

¹Computer Science Institute of Nantes-Atlantic (Lina), U.M.R. C.N.R.S. 6241, University of Nantes, 2 rue de la Houssinière, BP 92208, 44322 Nantes Cedex, France. christine.sinoquet@univ-nantes.fr

Journal of Bioinformatics and Computational Biology

|September 29, 2009

Summary

High-throughput genotyping often has missing single nucleotide polymorphism (SNP) data, impacting analyses. The new SNPShuttle algorithm improves SNP inference accuracy by using bi-directional scanning, enhancing reliability for genetic studies.

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Last Updated: Jun 20, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

High-throughput genotyping techniques are crucial for genetic research, but missing data remains a significant challenge.
Even low percentages of missing single nucleotide polymorphism (SNP) data can compromise the reliability of downstream analyses, including SNP-disease association tests.
Existing SNP inference methods, like NPUTE, utilize single-direction scanning, which may not fully capture data dependencies.

Purpose of the Study:

To enhance the accuracy of SNP inference methods for handling missing data in genetic datasets.
To develop an improved algorithm that addresses the limitations of single-directional scanning in SNP imputation.
To evaluate the performance of the proposed SNP inference method on realistic genetic benchmark data.

Main Methods:

A variant algorithm, KNNWinOpti, was developed to exploit both backward and forward dependencies in overlapping windows.
The major contribution, SNPShuttle, iterates bi-directional scanning to predict missing SNP values with increased confidence.
Simulations were conducted using realistic benchmarks derived from the Perlegen Project's high-resolution mouse strain map.

Main Results:

SNPShuttle consistently demonstrated increased accuracy across all 20 mouse chromosomes.
Performance improvements were observed for missing data percentages ranging from 5% to 30%.
The bi-directional scanning approach of SNPShuttle significantly enhanced SNP imputation accuracy compared to previous methods.

Conclusions:

SNPShuttle offers a more accurate and reliable method for inferring missing SNPs, particularly in datasets with substantial missingness.
The algorithm's iterative bi-directional scanning effectively captures complex dependencies within SNP data.
This advancement has the potential to improve the robustness of genetic association studies and other downstream analyses.