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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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Comparing Copy Number Variations and SNPs02:26

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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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Cancers Originate from Somatic Mutations in a Single Cell02:21

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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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Sanger Sequencing01:57

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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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Related Experiment Video

Updated: Apr 20, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Using SomaticSniper to Detect Somatic Single Nucleotide Variants.

David E Larson1, Travis E Abbott1, Richard K Wilson1

  • 1The Genome Institute at Washington University, St. Louis, Missouri.

Current Protocols in Bioinformatics
|November 29, 2014
PubMed
Summary
This summary is machine-generated.

This protocol details running SomaticSniper for detecting somatic single nucleotide variants (SNVs) in cancer research using next-generation sequencing data. It includes filtering steps to reduce false positives and software compilation instructions.

Keywords:
next-generation sequencingsomatic variant callingsomaticsniper

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Somatic single nucleotide variants (SNVs) are crucial in cancer research.
  • Next-generation sequencing (NGS) generates vast amounts of data for variant detection.

Purpose of the Study:

  • To provide a protocol for utilizing the SomaticSniper tool.
  • To describe methods for filtering SomaticSniper output to minimize false positives.

Main Methods:

  • Running the SomaticSniper somatic SNV detector.
  • Implementing filtering strategies on SomaticSniper output.
  • Compiling the SomaticSniper software.

Main Results:

  • Successful detection of somatic SNVs.
  • Reduction of false positive calls through filtering.
  • Availability of a compiled software package.

Conclusions:

  • The SomaticSniper protocol enables effective somatic SNV detection in cancer genomics.
  • Filtering is essential for accurate variant calling from NGS data.
  • The provided support protocols facilitate software implementation.