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Updated: May 14, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Published on: June 23, 2012

Hap-seq: an optimal algorithm for haplotype phasing with imputation using sequencing data.

Dan He1, Buhm Han, Eleazar Eskin

  • 1IBM T.J. Watson Research, Yorktown Heights, NY 10598, USA. dhe@us.ibm.com

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|February 7, 2013
PubMed
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Hap-seq accurately infers haplotypes from low-coverage sequencing data by combining reference panel imputation with read-based assembly. This novel method improves haplotype reconstruction for both common and rare alleles.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Haplotype inference is crucial for genetic analyses like admixture mapping and imputation.
  • Traditional methods rely on microarray data and population frequencies or reference panels.
  • Sequencing data analysis often involves inferring genotypes then imputing, ignoring read origin information.

Purpose of the Study:

  • To develop a novel method, Hap-seq, for accurate haplotype inference from sequencing data.
  • To combine imputation and assembly approaches within a unified likelihood framework.
  • To overcome limitations of existing methods, particularly at low sequencing coverage.

Main Methods:

  • Hap-seq integrates reference panel information with sequencing read data.

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Published on: June 23, 2012

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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  • A dynamic programming algorithm maximizes the joint likelihood of haplotypes.
  • The method simultaneously performs imputation and assembly.

    • Main Results:

    • Hap-seq achieves higher accuracy in haplotype reconstruction compared to state-of-the-art imputation methods.
    • The method effectively reconstructs haplotypes containing both common and rare alleles.
    • Hap-seq requires only low sequencing coverage.

    Conclusions:

    • Hap-seq offers a more accurate and efficient approach to haplotype inference from sequencing data.
    • The method's ability to work with low coverage makes it broadly applicable.
    • Hap-seq advances genetic analysis by improving haplotype phasing accuracy.