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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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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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SNP-PHAGE--High throughput SNP discovery pipeline.

Lakshmi K Matukumalli1, John J Grefenstette, David L Hyten

  • 1US Department of Agriculture, ARS, Beltsville Agricultural Research Center, Bovine Functional Genomics Laboratory, Beltsville, MD 20705, USA. lmatukum@gmu.edu

BMC Bioinformatics
|October 25, 2006
PubMed
Summary

We developed SNP-PHAGE, an open-source bioinformatics pipeline for efficient single nucleotide polymorphism (SNP) discovery and haplotype analysis. This tool aids in identifying genetic variations and facilitates submissions to GenBank, accelerating marker development.

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Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are crucial genetic markers for fine mapping and association studies.
  • Existing SNP discovery methods require combining multiple bioinformatics programs and lack integrated solutions.
  • High-throughput analysis technologies are increasingly utilizing SNPs over traditional markers like RFLPs and AFLPs.

Purpose of the Study:

  • To develop an integrated, open-source bioinformatics pipeline for SNP discovery and haplotype analysis.
  • To provide a user-friendly tool for researchers to develop SNP markers efficiently.
  • To facilitate data management and analysis for genetic studies.

Main Methods:

  • Developed SNP-PHAGE, a pipeline written in Perl on a UNIX/Linux platform using a MySQL database.
  • Integrated a machine learning tool for enhanced SNP discovery efficiency.
  • Included scripts for a web interface with common data analysis queries.

Main Results:

  • Successfully discovered over 10,000 SNPs from soybean sequence traces using SNP-PHAGE.
  • The pipeline facilitates identification of common haplotypes within sequence tagged sites.
  • SNP-PHAGE supports GenBank (dbSNP) submissions and provides preliminary data analysis tools.

Conclusions:

  • SNP-PHAGE offers a comprehensive bioinformatics solution for high-throughput SNP discovery and haplotype analysis.
  • The user-friendly web interface simplifies SNP selection and visualization.
  • This open-source software serves as a valuable starting point for SNP marker development.