Direct inference of haplotypes from sequencing data

Haplotypes are crucial for various genetic analyses, but reconstructing haplotypes from sequencing data remains a significant challenge. Current methods for haplotype reconstruction typically rely on a procedure of two separated stages, variant calling and phasing, but phasing overlooks the errors in variant calling. Additionally, the complexity of haplotype reconstruction increases with the number of homologous chromosomes in the sample, a common scenario in polyploid species or cell mixture sequencing.

To address the challenges above, we propose a unified probabilistic framework that directly utilizes sequencing reads to estimate haplotypes and sequencing error profiles. Rather than focusing solely on variant loci used by traditional phasing methods, our approach models all loci covered by any sequencing read to enhance the estimation of error profiles in sequencing data, thereby increasing the statistical power of haplotype inference, especially for low-coverage datasets. Evaluations on both simulated and real sequencing data demonstrate the superior performance of our method, particularly in scenarios characterized by high sequencing error rates, low coverage, or polyploidy.

Related codes and dataset can be found at: https://github.com/new-zbc/DIHap.