Hooded crow genome assembly (PacBio long reads + BioNano optical maps + DoveTail HiC maps)
Weissensteiner, Matthias (2020), Hooded crow genome assembly (PacBio long reads + BioNano optical maps + DoveTail HiC maps) , Dryad, Dataset, https://doi.org/10.5061/dryad.ns1rn8ppj
Structural variation (SV) is an important component of mutations providing the raw material for evolution. Here, we uncover the genome-wide spectrum of intra- and interspecific SV segregating in natural populations of seven songbird species in the genus Corvus. Combining short-read (N = 127) and long-read re-sequencing (N = 31), as well as optical mapping (N = 16), we apply both assembly- and read mapping approaches to detect SV and characterize a total of 220,452 insertions, deletions and inversions. We exploit sampling across wide phylogenetic timescales to validate SV genotypes and assess the contribution of SV to evolutionary processes in an avian model of incipient speciation. We reveal an evolutionary young (~530,000 years) cis-acting 2.25-kb retrotransposon insertion reducing expression of the NDP gene with consequences for premating isolation. Our results attest to the wealth and evolutionary significance of SV segregating in natural populations and highlight the need for reliable SV genotyping.
This dataset represents the reference assembly for the hooded crow (Corvus (corone) cornix) in this study. We de-novo assembled PacBio long-read data generated in Weissensteiner et al. 2017, performed hybrid-scaffolding with BioNano optical mapping data from Weissensteiner et al. 2017 and scaffolded with newly generated HiC chromatin interaction mapping data. Corvus_cornix__S_Up_H32__genome__FALCON_UNZIP_arrow_polished_primary_contigs_Irys.v4.2_hybrid_scaffolds_HiRise_HiC_scaffolds_Irys.v4.2_hybrid_scaffolds_comparative_scaffolding__v5.5.fasta contains the primary contigs of the PacBio de novo assembly, scaffolded with BioNano optical maps and Dovetail HiC chromatin interaction maps.
Vetenskapsrådet, Award: 621-2010-5553
European Research Council, Award: ERCStG-336536