RESUMO
The nine-spined stickleback (Pungitius pungitius) has been increasingly used as a model system in studies of local adaptation and sex chromosome evolution but its current reference genome assembly is far from perfect, lacking distinct sex chromosomes. We generated an improved assembly of the nine-spined stickleback reference genome (98.3% BUSCO completeness) with the aid of linked-read mapping. While the new assembly (v8) was of similar size as the earlier version (v7), we were able to assign 4.4 times more contigs to the linkage groups and improve the contiguity of the genome. Moreover, the new assembly contains a â¼22.8â Mb Y-linked scaffold (LG22) consisting mainly of previously assigned X-contigs, putative Y-contigs, putative centromere contigs, and highly repetitive elements. The male individual showed an even mapping depth on LG12 (pseudo X chromosome) and LG22 (Y-linked scaffold) in the segregating sites, suggesting near-pure X and Y representation in the v8 assembly. A total of 26,803 genes were annotated, and about 33% of the assembly was found to consist of repetitive elements. The high proportion of repetitive elements in LG22 (53.10%) suggests it can be difficult to assemble the complete sequence of the species' Y chromosome. Nevertheless, the new assembly is a significant improvement over the previous version and should provide a valuable resource for genomic studies of stickleback fishes.
Assuntos
Genoma , Smegmamorpha , Animais , Smegmamorpha/genética , Masculino , Feminino , Genômica/métodos , Mapeamento de Sequências Contíguas/métodos , Cromossomos Sexuais/genética , Mapeamento Cromossômico , Anotação de Sequência Molecular , Ligação Genética , Sequências Repetitivas de Ácido NucleicoRESUMO
Map distance is one of the key measures in genetics and indicates the expected number of crossovers between two loci. Map distance is estimated from the observed recombination frequency using mapping functions, the most widely used of those, Haldane and Kosambi, being developed at the time when the number of markers was low and unobserved crossovers had a substantial effect on the recombination fractions. In contemporary high-density marker data, the probability of multiple crossovers between adjacent loci is negligible and different mapping functions yield the same result, that is, the recombination frequency between adjacent loci is equal to the map distance in Morgans. However, high-density linkage maps contain an interpretation problem: the map distance over a long interval is additive and its association with recombination frequency is not defined. Here, we demonstrate with high-density linkage maps from humans and stickleback fishes that the inverses of Haldane's and Kosambi's mapping functions systematically underpredict recombination frequencies from map distance. To remedy this, we formulate a piecewise function that yields more accurate predictions of recombination frequency from map distance. Our results demonstrate that the association between map distance and recombination frequency is context-dependent and without a universal solution.
Assuntos
Recombinação Genética , Humanos , Mapeamento Cromossômico/métodos , Probabilidade , Ligação GenéticaRESUMO
We describe an integrative approach to improve contiguity and haploidy of a reference genome assembly and demonstrate its impact with practical examples. With two novel features of Lep-Anchor software and a combination of dense linkage maps, overlap detection and bridging long reads, we generated an improved assembly of the nine-spined stickleback (Pungitius pungitius) reference genome. We were able to remove a significant number of haplotypic contigs, detect more genetic variation and improve the contiguity of the genome, especially that of X chromosome. However, improved scaffolding cannot correct for mosaicism of erroneously assembled contigs, demonstrated by a de novo assembly of a 1.6-Mbp inversion. Qualitatively similar gains were obtained with the genome of three-spined stickleback (Gasterosteus aculeatus). Since the utility of genome-wide sequencing data in biological research depends heavily on the quality of the reference genome, the improved and fully automated approach described here should be helpful in refining reference genome assemblies.