期刊
MOLECULAR ECOLOGY RESOURCES
卷 22, 期 2, 页码 458-467出版社
WILEY
DOI: 10.1111/1755-0998.13491
关键词
autosomes; bioinformatics; resequencing; scaffold-level assembly
资金
- Det Frie Forskningsrad [DFF8049-00098B]
- European Research Council [853442]
- Carlsbergfondet [CF19-0427]
- Novo Nordisk Fonden [NNF20OC0061343]
- Lundbeckfonden [R215-2015-4174]
- European Research Council (ERC) [853442] Funding Source: European Research Council (ERC)
The SATC method is proposed to assign sex to samples and identify sex-linked scaffolds from NGS data, working for species with a homogametic/heterogametic sex determination system. It uses scaffold depth distribution and PCA to achieve sex assignment and sex-linked scaffold identification, without prior knowledge of sample sex, using WGS data.
Being able to assign sex to individuals and identify autosomal and sex-linked scaffolds are essential in most population genomic analyses. Non-model organisms often have genome assemblies at scaffold-level and lack characterization of sex-linked scaffolds. Previous methods to identify sex and sex-linked scaffolds have relied on synteny between the non-model organism and a closely related species or prior knowledge about the sex of the samples to identify sex-linked scaffolds. In the latter case, the difference in depth of coverage between the autosomes and the sex chromosomes are used. Here, we present sex assignment through coverage (SATC), a method to assign sex to samples and identify sex-linked scaffolds from next generation sequencing (NGS) data. The method works for species with a homogametic/heterogametic sex determination system and only requires a scaffold-level reference assembly and sampling of both sexes with whole genome sequencing (WGS) data. We use the sequencing depth distribution across scaffolds to jointly identify: (i) male and female individuals, and (ii) sex-linked scaffolds. This is achieved through projecting the scaffold depths into a low-dimensional space using principal component analysis (PCA) and subsequent Gaussian mixture clustering. We demonstrate the applicability of our method using data from five mammal species and a bird species complex. The method is freely available at as R code and a graphical user interface (GUI).
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