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Pairwise comparative analysis of six haplotype assembly methods based on users' experience.

Shuying Sun¹, Flora Cheng², Daphne Han²

¹Department of Mathematics, Texas State University, San Marcos, TX, USA. ssun5211@yahoo.com.

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|June 29, 2023

Summary

This study compared six haplotype assembly (HA) methods, finding HapCUT2 to be the fastest. Performance varied, with SDhaP showing higher disagreement rates across datasets.

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Area of Science:

Genomics
Bioinformatics

Background:

Haplotype information is crucial for understanding genetic variation and disease association.
Haplotype assembly (HA) reconstructs haplotypes from DNA sequencing data.
Numerous HA methods exist, each with distinct advantages and limitations.

Purpose of the Study:

To compare the performance of six leading haplotype assembly algorithms: HapCUT2, MixSIH, PEATH, WhatsHap, SDhaP, and MAtCHap.
To evaluate algorithm efficiency (runtime) and accuracy across different datasets and sequencing depths.
To provide insights into the strengths and weaknesses of current HA methods.

Main Methods:

Six HA algorithms (HapCUT2, MixSIH, PEATH, WhatsHap, SDhaP, MAtCHap) were applied to two NA12878 datasets (hg19 and hg38).
Analyses were conducted on chromosome 10 with three sequencing depth filtering levels (DP1, DP15, DP30).
Efficiency was assessed by CPU runtime; accuracy was evaluated using disagreement rates and switch distance.

Main Results:

HapCUT2 demonstrated the fastest runtime (under 2 minutes) across all datasets.
WhatsHap was also efficient, with runtimes of 21 minutes or less.
SDhaP exhibited significantly higher disagreement rates compared to other algorithms across all tested datasets.
HapCUT2, PEATH, MixSIH, and MAtCHap showed comparable performance in terms of block and SNV counts and accuracy.

Conclusions:

The comparative analysis highlights significant performance differences among HA algorithms.
Findings offer valuable guidance for researchers selecting appropriate HA tools.
This study enhances understanding of current HA method capabilities for genetic variation studies.