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Novel Sequence Discovery by Subtractive Genomics
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SEQ Mapper: A DNA sequence searching tool for massively parallel sequencing data.

James Chun-I Lee1, Bill Tseng1, Liang-Kai Chang2

  • 1Department of Forensic Medicine, College of Medicine, National Taiwan University, No.1 Jen-Ai Road Section 1, Taipei 10051, Taiwan, ROC.

Forensic Science International. Genetics
|October 30, 2016
PubMed
Summary
This summary is machine-generated.

SEQ Mapper efficiently analyzes genetic polymorphisms from massively parallel sequencing (MPS) data. This tool accurately identifies short tandem repeat (STR) and single nucleotide polymorphism (SNP) alleles for forensic and clinical applications.

Keywords:
BioinformaticsForensic scienceMassively parallel sequencingNext generation sequencingSNPSTR

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Massively parallel sequencing (MPS) generates large datasets challenging for genetic polymorphism analysis.
  • Identifying short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) is crucial for genetic studies.

Purpose of the Study:

  • To develop and validate SEQ Mapper, a novel program for analyzing genetic polymorphisms from MPS data.
  • To facilitate the identification of STR and SNP alleles in large sequencing datasets.

Main Methods:

  • SEQ Mapper was designed for sequence mapping between reference data and MPS-generated reads.
  • Proof-of-concept involved using reference sequences for five STR loci and the amelogenin locus.
  • Polymorphism detection utilized four distinct search criteria levels for STR loci analysis.

Main Results:

  • SEQ Mapper correctly identified genotypes for 5 STR loci and the amelogenin gene in 10 test samples.
  • Results were validated against capillary electrophoresis data, demonstrating high accuracy.
  • The program effectively detected STR and SNP alleles generated by MPS.

Conclusions:

  • SEQ Mapper is a valuable tool for detecting STR and SNP alleles from MPS data.
  • Its application spans both clinical medicine and forensic genetics.
  • The program aids in overcoming the analytical challenges posed by large MPS datasets.