A haplotype-resolved, de novo genome assembly for the wood tiger moth (Arctia plantaginis) through trio binning.
摘要:
Diploid genome assembly is typically impeded by heterozygosity because it introduces errors when haplotypes are collapsed into a consensus sequence. Trio binning offers an innovative solution that exploits heterozygosity for assembly. Short, parental reads are used to assign parental origin to long reads from their F1 offspring before assembly, enabling complete haplotype resolution. Trio binning could therefore provide an effective strategy for assembling highly heterozygous genomes, which are traditionally problematic, such as insect genomes. This includes the wood tiger moth (Arctia plantaginis), which is an evolutionary study system for warning colour polymorphism. We produced a high-quality, haplotype-resolved assembly for Arctia plantaginis through trio binning. We sequenced a same-species family (F1 heterozygosity ∼1.9%) and used parental Illumina reads to bin 99.98% of offspring Pacific Biosciences reads by parental origin, before assembling each haplotype separately and scaffolding with 10X linked reads. Both assemblies are contiguous (mean scaffold N50: 8.2 Mb) and complete (mean BUSCO completeness: 97.3%), with annotations and 31 chromosomes identified through karyotyping. We used the assembly to analyse genome-wide population structure and relationships between 40 wild resequenced individuals from 5 populations across Europe, revealing the Georgian population as the most genetically differentiated with the lowest genetic diversity. We present the first invertebrate genome to be assembled via trio binning. This assembly is one of the highest quality genomes available for Lepidoptera, supporting trio binning as a potent strategy for assembling heterozygous genomes. Using our assembly, we provide genomic insights into the geographic population structure of A. plantaginis.
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关键词:
Lepidoptera , annotation , genome assembly , population genomics , trio binning , wood tiger moth; Arctia plantaginis
DOI:
10.1093/gigascience/giaa088
被引量:
年份:
2020


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