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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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A hybrid correcting method considering heterozygous variations by a comprehensive probabilistic model.

Jiaqi Liu1,2, Jiayin Wang3,4, Xiao Xiao1,5

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710048, China.

BMC Genomics
|November 19, 2020
PubMed
Summary

QIHC is a novel hybrid error correction method that improves accuracy for third-generation sequencing data, especially at heterozygous sites and low coverages. This method enhances biological research by reducing data waste and improving downstream analysis.

Keywords:
Error correction methodHeterozygous variantHybrid correction methodPacBio sequencingProbabilistic modelSequencing analysisSequencing error

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Third-generation sequencing (TGS) offers longer reads but suffers from high error rates, impacting downstream analyses.
  • Existing error correction methods struggle with heterozygous sites, common in diploid/polyploid organisms, especially at low sequencing coverage.
  • Discarding high-error TGS data leads to significant waste, necessitating effective error correction strategies.

Purpose of the Study:

  • To develop an accurate hybrid error correction method for TGS data.
  • To specifically address the challenge of correcting errors at heterozygous loci.
  • To improve the efficiency and accuracy of TGS data analysis, particularly under low-coverage conditions.

Main Methods:

  • Proposed QIHC, a hybrid correction method utilizing both next-generation sequencing (NGS) and TGS data.
  • Developed probabilistic models based on Bayesian classification to estimate site heterozygosity and posterior probabilities.
  • Implemented a three-module system: pseudo-reference sequence generation, read alignment, and heterozygosity estimation/correction.

Main Results:

  • QIHC demonstrated superior accuracy in error correction compared to Jabba and Canu across various NGS and TGS data coverages.
  • The method excelled in identifying heterozygous sites and correcting errors, particularly under low-coverage scenarios.
  • QIHC maintained performance advantages even with varying TGS error rates, outperforming Canu.

Conclusions:

  • QIHC significantly outperforms existing methods like Canu and Jabba for TGS data error correction, especially in the presence of heterozygous sites.
  • The method's hybrid approach and Bayesian modeling enhance accuracy and sensitivity, making it valuable for genomic research.
  • QIHC offers a robust solution for handling high-error TGS data, reducing waste and enabling more reliable downstream analyses.