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Updated: May 25, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
Published on: February 24, 2015
Genotype calling from next-generation sequencing data using haplotype information of reads
Degui Zhi1, Jihua Wu, Nianjun Liu
1Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. dzhi@ms.soph.uab.edu
A new Hidden Markov Model (HMM) method improves whole genome sequencing accuracy by utilizing jumping reads. The HapSeq program reduces genotyping error rates by up to 30% in simulations and real-world data.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Low coverage sequencing is cost-effective for whole genome sequencing but presents genotype calling challenges.
- Linkage disequilibrium (LD) refinement is crucial for improving genotype accuracy in low coverage data.
- Existing LD methods overlook haplotype information from jumping reads spanning multiple potential polymorphic sites (PPSs).
Purpose of the Study:
- To introduce a novel Hidden Markov Model (HMM)-based method for genotype calling that incorporates jumping reads.
- To implement this method in a software program named HapSeq.
- To evaluate the performance of HapSeq in improving genotype accuracy.
Main Methods:
- Developed an HMM-based method extending existing models (e.g., Thunder).
- Explicitly modeled jumping reads information as emission probabilities conditional on adjacent PPS states.
- Implemented the method in the HapSeq software package.
Main Results:
- HapSeq reduced the genotyping error rate by 30% in simulations (from 0.86% to 0.60%) compared to Thunder.
- In the 1000 Genomes Project, HapSeq decreased error rates by 12% (European ancestry) and 9% (African ancestry).
- Demonstrated significant improvements in genotype accuracy using real-world sequencing data.
Conclusions:
- The HapSeq program effectively leverages jumping reads for more accurate genotype calling.
- This method offers a significant improvement over existing LD-based approaches for low coverage sequencing.
- HapSeq is expected to enhance the quality of large-scale whole genome sequencing projects.
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