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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Related Experiment Video

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data.

Masaaki Kobayashi1, Hajime Ohyanagi1,2, Hideki Takanashi3

  • 1Bioinformatics Laboratory, Department of Life Sciences, School of Agriculture, Meiji University, Kanagawa 214-8571, Japan.

DNA Research : an International Journal for Rapid Publication of Reports on Genes and Genomes
|May 13, 2017
PubMed
Summary

Heap is a new tool for accurate single nucleotide polymorphism (SNP) calling from low-coverage next-generation sequencing (NGS) data. This facilitates cost-effective genome-wide association studies (GWAS) and genomic prediction (GP).

Keywords:
genome-wide association studies (GWAS)genomic prediction (GP)next-generation sequencing (NGS)restriction-site associated DNA sequencing (RAD-seq)single nucleotide polymorphism (SNP)

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Large-scale genomic resources and next-generation sequencing (NGS) data enable genome-wide association studies (GWAS) and genomic prediction (GP).
  • The accuracy of GWAS and GP relies heavily on the quality and quantity of genetic variants used.
  • Conventional single nucleotide polymorphism (SNP) calling tools often require high sequencing depth for optimal sensitivity and accuracy.

Purpose of the Study:

  • To develop a novel bioinformatics tool named Heap.
  • Heap is designed for robustly sensitive and accurate SNP calling, especially from low-coverage NGS data.
  • The tool aims to improve the efficiency and cost-effectiveness of genomic studies.

Main Methods:

  • Heap processes NGS data aligned to reference genomes.
  • It determines genotypes and calls SNPs by excluding sites at read ends or those with minor alleles supported by a single read to minimize false positives.
  • Performance was evaluated using low-coverage (7X) restriction-site associated DNA sequencing data.

Main Results:

  • Heap demonstrated superior performance compared to existing tools.
  • The tool achieved the highest F-scores when analyzing low-coverage sequencing data from sorghum and rice.
  • Heap provides accurate SNP calling even with limited sequencing depth.

Conclusions:

  • Heap enables highly sensitive and accurate SNP calling from low-coverage NGS data.
  • This facilitates more cost-effective genome-wide association studies (GWAS) and genomic prediction (GP).
  • The developed tool supports advancements in genomic research by improving variant detection efficiency.