sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data
Year: 2022
Type: article
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.
Source: Nucleic Acids Research
DOI: 10.1093/nar/gkac764
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,876
Awards: A simple method to determine individual genome structures and assay fertility, using single haploid cell sequencing, Combining human genetics with single-cell genomics to unravel the differentiation dynamics of human induced pluripotent stem cells, Coordination of the Fanconi anemia pathway to maintain genome stability, Developing interpretable machine learning models for clinical imaging and single-cell genomics
OpenAlex ID: W4295899404