sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data

Abstract

Profiling gametes of an individual enables the construction of personalised haplotypes and meiotic crossover landscapes, now achievable at larger scale than ever through the availability of high-throughput single-cell sequencing technologies. However, high-throughput single-gamete data commonly have low depth of coverage per gamete, which challenges existing gamete-based haplotype phasing methods. In addition, haplotyping a large number of single gametes from high-throughput single-cell DNA sequencing data and constructing meiotic crossover profiles using existing methods requires intensive processing. Here, we introduce efficient software tools for the essential tasks of generating personalised haplotypes and calling crossovers in gametes from single-gamete DNA sequencing data (sgcocaller), and constructing, visualising, and comparing individualised crossover landscapes from single gametes (comapr). With additional data pre-possessing, the tools can also be applied to bulk-sequenced samples. We demonstrate that sgcocaller is able to generate impeccable phasing results for high-coverage datasets, on which it is more accurate and stable than existing methods, and also performs well on low-coverage single-gamete sequencing datasets for which current methods fail. Our tools achieve highly accurate results with user-friendly installation, comprehensive documentation, efficient computation times and minimal memory usage.

Mapping crossover events of mouse meiotic recombination by restriction fragment ligation-based Refresh-seq

“…Hidden Markov Model (HMM) was used to phase the hetSNPs and 99.97% of the hetSNPs were precisely phased to the correct haplotype in the genome (Supplementary Table S1 ), which was comparable with previous studies. The SNP linkage in the gametes offers information for constructing chromosome-level phased haplotypes of the individual 25 . Then, we inferred crossovers on each chromosome of every single sperm with the phased haplotypes.…”

Haplotype-resolved assembly of a pig genome using single-sperm sequencing

“…Li et al developed Hapi, which utilizes sperm data to obtain haplotypes by employing the PHMM (pairwise Hidden Markov Model) method 23 . Lyu et al developed sgcocaller software, which outperforms the Hapi algorithm in accuracy and performance, providing great efficiency for sperm research 24 . In this study, we sequenced 102 sperm cells with an average depth of 10.05X and sequenced the blood samples from the donor boar with 95.12X, which allowed us to directly infer sperm haplotypes, as shown in Fig.…”

IIIandMe: An Algorithm for Chromosome-scale Haplotype Determination Using Genome-wide Variants of Three Haploid Reproductive Cells

“…The inference accuracies for 4-gamete, 5-gamete, and 6-gamete analyses were also 100%, as displayed in We demonstrated that IIIandMe, which applies to genomic data of high quality, can accurately infer chromosomal haplotypes using three or a few more single gametes and is computationally much more efficient than Hapi. Given that crossovers are very rare (Beye et al 2006;Lyu et al 2022), three gametes are theoretically sufficient for phasing the entire genome, likely pushing the boundary to its possible limit. We foresee that IIIandMe will find its way to become impactful in many genetic research areas and applications.…”