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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.
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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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Evolutionary Relationships through Genome Comparisons02:54

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Related Experiment Video

Updated: May 7, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Comparing a few SNP calling algorithms using low-coverage sequencing data.

Xiaoqing Yu1, Shuying Sun

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA. ssun5211@yahoo.com.

BMC Bioinformatics
|September 19, 2013
PubMed
Summary
This summary is machine-generated.

Accurate single nucleotide variation (SNV) identification from low-coverage next-generation sequencing (NGS) data is challenging. Comparing four SNV callers, GATK and Atlas-SNP2 showed higher accuracy, but using multiple algorithms with quality metrics is recommended for reliable results.

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Last Updated: May 7, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) enables identification of single nucleotide variations (SNVs).
  • Low sequencing coverage in single-sample data complicates accurate SNV detection.
  • SNP calling programs generate numerous correlated metrics, hindering validation SNP selection.

Purpose of the Study:

  • To evaluate and compare four SNV calling algorithms: SOAPsnp, Atlas-SNP2, SAMtools, and GATK.
  • To assess algorithm performance in low-coverage, single-sample NGS data.
  • To investigate the utility of quality metrics for post-output filtering of SNVs.

Main Methods:

  • Comparative analysis of SOAPsnp, Atlas-SNP2, SAMtools, and GATK performance.
  • Evaluation using a low-coverage, single-sample sequencing dataset.
  • Application of key SNV quality metrics for post-filtering analysis.
  • Calculation of empirical positive calling rate and sensitivity under varying coverage cutoffs.

Main Results:

  • Overall agreement among the four SNV calling algorithms was low, particularly for non-dbSNPs.
  • GATK and Atlas-SNP2 demonstrated relatively higher positive calling rates and sensitivity.
  • SOAPsnp, SAMtools, and GATK showed higher calling rates for dbSNPs than non-dbSNPs.
  • Increasing coverage slightly improved agreement for non-dbSNPs.

Conclusions:

  • The low agreement between SNV calling algorithms necessitates careful selection of tools and filtering parameters.
  • Employing multiple algorithms and utilizing quality metrics are recommended for reliable SNV identification.
  • Validation study designs should account for the inherent variability in SNV calling results.