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Related Concept Videos

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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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. 
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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.
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Although all next-generation methods use different technologies, they all share a set of standard features.
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Related Experiment Video

Updated: May 18, 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

SNP calling by sequencing pooled samples.

Emanuele Raineri1, Luca Ferretti, Anna Esteve-Codina

  • 1Centro Nacional de Análisis Genómico, Parc Científic de Barcelona, Barcelona, 08028, Spain. emanuele.raineri@gmail.com

BMC Bioinformatics
|September 21, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces snape, a Bayesian software for accurate SNP calling in pooled DNA sequencing. It improves minor allele frequency estimation and reduces false discoveries compared to existing methods.

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Infinium Assay for Large-scale SNP Genotyping Applications
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Last Updated: May 18, 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

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing of pooled DNA samples offers cost-effective genetic variability analysis.
  • SNP calling and minor allele frequency (MAF) estimation in pools present unique challenges due to sequencing errors and allele frequency distributions.
  • Distinguishing true singletons from sequencing artifacts requires sophisticated analytical approaches.

Purpose of the Study:

  • To develop an improved Bayesian approach for SNP calling and MAF computation in pooled sequencing data.
  • To address limitations of existing methods in handling sequencing errors and complex allele frequency distributions.
  • To provide a software tool that enhances accuracy and reduces false discovery rates in pooled sequencing analysis.

Main Methods:

  • Developed a Bayesian statistical framework implemented in the snape software package.
  • Incorporated methods to account for sequencing errors and allowed for flexible prior specifications.
  • Created a simulation pipeline to model SNP generation, pooling, and sequencing processes for performance evaluation.

Main Results:

  • The snape software demonstrates superior performance in SNP calling and MAF estimation compared to established tools like samtools, PoPoolation, and Varscan.
  • Achieved high statistical power (≈35%) with a low false discovery rate (FDR ≈2.5%) for N=50 chromosomes in simulated datasets.
  • The method provides posterior probabilities for SNP segregation and full posterior distributions for allele frequencies.

Conclusions:

  • snape offers a powerful and accurate solution for SNP calling in pooled sequencing data.
  • The software effectively balances statistical power with a low false discovery rate.
  • snape is available as open-source software, facilitating its adoption in genetic research.