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Tools, resources and databases for SNPs and indels in sequences: a review.

Abhik Seal1, Arun Gupta2, M Mahalaxmi3

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|May 6, 2014
PubMed
Summary

Single Nucleotide Polymorphisms (SNPs) are key DNA variations. This study guides researchers on using in silico tools and databases for detecting SNPs in plants and animals.

Keywords:
SNP detectionSNPsbioinformaticsdata miningdatabasesexpressed sequence tagsindelsmolecular markerspoint mutationssequencessingle nucleotide polymorphismssoftware

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Single Nucleotide Polymorphisms (SNPs) represent common point mutations in DNA, serving as crucial markers in genetic analysis.
  • SNPs are valuable for identifying genes linked to diseases and have broad implications in genomic research.
  • The post-genomic era facilitates in silico study of SNPs within known genomes.

Purpose of the Study:

  • To provide a guide for researchers on software tools for SNP detection in plants and animals.
  • To detail the algorithmic background, file requirements, and system specificity of various SNP analysis tools.
  • To compile a list of available databases and resources for SNP information across diverse organisms.

Main Methods:

  • Review and analysis of existing in silico tools for SNP detection.
  • Compilation of software details including algorithmic background and operational requirements.
  • Cataloging of online and standalone SNP analysis resources and databases.

Main Results:

  • Identification of various online and standalone software tools for SNP analysis.
  • Detailed descriptions of tool specifications (algorithmic, file, OS, and species specificity).
  • A comprehensive list of databases and resources for SNP data in a wide range of organisms.

Conclusions:

  • In silico methods offer an accessible approach to studying SNPs in post-genomic research.
  • Effective utilization of available tools and databases can significantly aid SNP detection in plants and animals.
  • This guide aims to streamline the process of SNP analysis for researchers in relevant fields.