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Comparing Copy Number Variations and SNPs02:26

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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.
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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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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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RareVar: A Framework for Detecting Low-Frequency Single-Nucleotide Variants.

Yangyang Hao1,2, Xiaoling Xuei3,4, Lang Li1,2

  • 11 Department of Medical and Molecular Genetics, Indiana University School of Medicine , Indianapolis, Indiana.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 26, 2017
PubMed
Summary

Detecting low-frequency somatic mutations is crucial for understanding cancer. The RareVar framework accurately identifies these low-frequency single-nucleotide variants using advanced computational modeling and experimental design.

Keywords:
low frequency SNVsmachine learningnext-generation sequencingsequencing error modelingsomatic mutation.

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Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Accurate identification of low-frequency somatic point mutations is clinically significant for understanding cancer etiology and evolution.
  • Current methods struggle with detecting variants at frequencies near the sequencer error rate (typically <5% allele frequency).
  • This limitation hinders comprehensive analysis of tumor heterogeneity and evolutionary dynamics.

Purpose of the Study:

  • To introduce RareVar, an experimental and computational framework for reliable identification of low-frequency single-nucleotide variants.
  • To enable detection of variants at frequencies as low as 0.5%-3% using standard sequencing protocols.
  • To improve the accuracy of variant detection in high-throughput sequencing data.

Main Methods:

  • Developed a benchmark experimental design involving DNA pooling at various concentrations.
  • Implemented a generalized linear model for a position-specific error model.
  • Utilized machine-learning-based variant calibration for enhanced accuracy.
  • The RareVar framework is compatible with most capture and sequencing platforms.

Main Results:

  • RareVar reliably identifies low-frequency single-nucleotide variants (0.5%-3% allele frequency).
  • The computational modeling framework outperforms existing methods in variant detection accuracy.
  • The approach is robust and applicable across various sequencing platforms without protocol modification.

Conclusions:

  • RareVar significantly advances the ability to detect low-frequency somatic mutations.
  • This framework enhances the understanding of cancer etiology and tumor evolution.
  • The method offers a versatile and accurate solution for low-frequency variant identification in genomic studies.