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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%...
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,...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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.

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Related Experiment Video

Updated: May 31, 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

Sniper: improved SNP discovery by multiply mapping deep sequenced reads.

Daniel F Simola1, Junhyong Kim

  • 1Department of Biology, University of Pennsylvania, 433 S, University Ave., Philadelphia, PA 19104, USA.

Genome Biology
|June 22, 2011
PubMed
Summary
This summary is machine-generated.

Discovering single nucleotide polymorphisms (SNPs) is challenging due to repetitive genomic regions. Sniper, a new Bayesian model, accurately identifies SNPs by accounting for multi-locus reads and sequencing errors.

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Last Updated: May 31, 2026

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

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Published on: June 23, 2012

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphism (SNP) discovery from next-generation sequencing (NGS) data is hindered by genomic complexity.
  • Redundant genomic regions, including repetitive elements and paralogous genes, complicate accurate SNP identification in eukaryotes.

Purpose of the Study:

  • To develop a novel computational method for robust SNP discovery in the presence of genomic redundancy.
  • To improve the sensitivity and specificity of SNP detection using NGS data.

Main Methods:

  • Development of Sniper, a multi-locus Bayesian probabilistic model.
  • Implementation of a computationally efficient algorithm that integrates sequence reads mapping to multiple genomic loci.
  • Accounting for sequencing errors, template bias, and multi-locus SNP combinations within the model.

Main Results:

  • Sniper effectively addresses challenges posed by repetitive genomic regions.
  • The model maintains high sensitivity and specificity across diverse sequencing conditions.
  • Demonstrated computational efficiency in SNP discovery.

Conclusions:

  • Sniper provides a robust solution for SNP discovery in complex eukaryotic genomes.
  • The developed method enhances the accuracy of variant calling from NGS data.
  • Sniper is freely available as a software implementation.