4.8 Article

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

期刊

NUCLEIC ACIDS RESEARCH
卷 50, 期 20, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkac764

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资金

  1. Australian National Health and Medical Research Council [GNT1129757, GNT1195595, GNT1112681, GNT1185387]
  2. Baker Foundation
  3. Australian Commonwealth Government
  4. University of Melbourne
  5. SVI Foundation Top-Up Scholarship from St Vincent's Institute
  6. St Vincent's Institute Top-Up scholarship
  7. National Health and Medical Research Council [GNT1129757, GNT1195595, GNT1112681, GNT1185387]

向作者/读者索取更多资源

Profiling gametes is important for constructing personalised haplotypes and meiotic crossover landscapes, but existing methods face challenges with low coverage and high processing requirements. In this study, we introduce efficient software tools for generating personalised haplotypes and calling crossovers in gametes, as well as constructing, visualising, and comparing individualised crossover landscapes. These tools achieve highly accurate results with user-friendly installation and efficient computation times.
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.

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