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Direct inference of haplotypes from sequencing data.

Zhen Zhang1,2,3, Bencong Zhu3, Yongyi Luo3

  • 1The First Institute, Kunming Institute of Physics, Kunming, Yunnan Province, 650223, China.

Bioinformatics Advances
|September 22, 2025
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Summary
This summary is machine-generated.

This study introduces a unified probabilistic framework for haplotype reconstruction directly from sequencing reads. The method improves accuracy in complex scenarios like polyploidy and low-coverage data by modeling sequencing errors.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Haplotype reconstruction is vital for genetic analyses but challenging with current two-stage methods (variant calling and phasing) that ignore variant calling errors.
  • Haplotype reconstruction complexity increases with homologous chromosome number, common in polyploid species or cell mixtures.

Purpose of the Study:

  • To develop a unified probabilistic framework for direct haplotype reconstruction from sequencing data.
  • To improve haplotype inference accuracy, especially for low-coverage and high-error datasets, and in polyploid samples.

Main Methods:

  • A unified probabilistic framework directly utilizing sequencing reads to estimate haplotypes and sequencing error profiles.
  • Modeling all loci covered by sequencing reads, not just variant loci, to enhance error profile estimation.

Main Results:

  • The proposed method demonstrates superior performance on simulated and real sequencing data compared to traditional phasing methods.
  • Significant improvements were observed in scenarios with high sequencing error rates, low coverage, and polyploidy.

Conclusions:

  • The unified framework offers a more robust and accurate approach to haplotype reconstruction.
  • This method enhances statistical power for haplotype inference, particularly in challenging genomic contexts.