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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

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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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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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DNA isolation protocols can be fast and straightforward or complex and time-consuming depending on the type and quality of DNA required for further processing. For example, plasmid DNA extraction is a bit more complicated than genomic DNA extraction because of the need for an appropriate lysis method to separate plasmid DNA from gDNA during isolation. However, for specific applications, such as long-range DNA sequencing that require a good yield of high- quality DNA samples, we need to follow...
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Reference-free detection of isolated SNPs.

Raluca Uricaru1, Guillaume Rizk2, Vincent Lacroix3

  • 1University of Bordeaux, CNRS/LaBRI, F-33405 Talence, France University of Bordeaux, CBiB, F-33000 Bordeaux, France INRA, UMR1349 IGEPP, Le Rheu, France ruricaru@labri.fr.

Nucleic Acids Research
|November 19, 2014
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Summary

discoSnp is a novel reference-free method for detecting single nucleotide polymorphisms (SNPs) in any number of datasets. This efficient tool requires minimal computational resources, making SNP detection accessible for non-model organisms.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing facilitates routine single nucleotide polymorphism (SNP) detection.
  • Reference-based methods are standard, but reference-free approaches are needed for non-model organisms.
  • Existing reference-free methods have limitations in scalability and computational demands.

Purpose of the Study:

  • To develop a reference-free method for detecting both heterozygous and homozygous isolated SNPs.
  • To create a computationally efficient tool applicable to any number of read datasets.
  • To enable SNP detection for non-model organisms lacking a reference genome.

Main Methods:

  • Implemented discoSnp, a novel algorithm for reference-free SNP detection.
  • Designed discoSnp to handle any number of read datasets with low memory and time footprints.
  • Incorporated SNP prediction ranking and allele-specific quality/coverage output for downstream analysis.

Main Results:

  • discoSnp successfully detects isolated SNPs from multiple datasets without a reference genome.
  • The method demonstrates very low computational resource requirements, analyzing billions of reads on a standard computer.
  • Experimental validation on Ixodes ricinus showed 96% of tested predicted SNPs were true polymorphisms.

Conclusions:

  • discoSnp offers an efficient and scalable solution for reference-free SNP detection.
  • The method overcomes limitations of existing tools, particularly for non-model organisms.
  • discoSnp provides reliable SNP predictions suitable for genotyping and population genetics studies.