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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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MAP: Mutation Arranger for Defining Phenotype-Related Single-Nucleotide Variant.

In-Pyo Baek1, Yong-Bok Jeong2, Seung-Hyun Jung1

  • 1Department of Microbiology, Integrated Research Center for Genome Polymorphism (IRCGP), The Catholic University of Korea College of Medicine, Seoul 137-701, Korea.

Genomics & Informatics
|February 24, 2015
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Summary

A new tool, Mutation Arranger for Defining Phenotype-related SNV (MAP), visualizes recurrent and phenotype-specific mutations from next-generation sequencing data. This user-friendly program aids researchers in identifying clinically meaningful cancer mutations.

Keywords:
cancermutationnext-generation sequencing (NGS)sequence snalysissingle-nucleotide variant (SNV)software

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Next-generation sequencing (NGS) is crucial for identifying disease-causing mutations in human cancers, aiding diagnostic and therapeutic target discovery.
  • Existing single-nucleotide variant (SNV)-calling algorithms lack visualization capabilities for recurrent and phenotype-specific mutations, hindering general researcher accessibility.
  • The need exists for a tool to effectively analyze and visualize mutation patterns within diverse cancer phenotypes from NGS data.

Purpose of the Study:

  • To develop a user-friendly software tool, Mutation Arranger for Defining Phenotype-related SNV (MAP), for visualizing recurrent and phenotype-specific mutations.
  • To enable researchers, particularly those unfamiliar with Linux, to analyze NGS data from cancer cohorts.
  • To facilitate the identification of clinically relevant mutations associated with specific cancer phenotypes.

Main Methods:

  • Development of a user-friendly program named Mutation Arranger for Defining Phenotype-related SNV (MAP).
  • Implementation of multiple functions within MAP to support the determination of recurrent or phenotype-specific mutations.
  • Integration of graphic illustration capabilities for visualizing mutation data.

Main Results:

  • MAP provides a user-friendly interface for analyzing next-generation sequencing data.
  • The tool successfully supports the determination of recurrent and phenotype-specific mutations.
  • MAP generates graphic illustrations, aiding in the interpretation of mutation patterns.

Conclusions:

  • MAP is an effective, user-friendly tool for visualizing recurrent and phenotype-specific mutations from NGS data in cancer research.
  • The software enhances the ability of researchers to identify clinically meaningful mutations, particularly within Windows environments.
  • MAP addresses a critical gap in bioinformatics tools for cancer genomics analysis.