bioRxivGenomicsPreprint
De novo assembly and annotation of the Patagonian toothfish (Dissostichus eleginoides) genome
David RyderiD, David Stone, Diana MinardiiD, Ainsley Riley, Justin Avant, Lisa Jane CrossiD, Marta Söffker, Deborah Davidson, Andrew Newman, Peter Campbell ThomsoniD, Chris Darby, Ronny van AerleiD
Abstract
Patagonian toothfish (Dissostichus eleginoides) is an economically and ecologically important fish species in the family Nototheniidae, found at depths between 70 and 2,500 meters on the southern shelves and slopes around the sub-Antarctic islands of the Southern Ocean. Genomic sequence data for this species is limited. Here, we report a high-quality assembly and annotation of the D. eleginoides genome, generated using a combination of Illumina, PacBio and Omni-C sequencing technologies. To aid the genome annotation, the transcriptome derived from a variety of toothfish tissues was also generated using both short and long read sequencing methods. The final genome assembly was 797.8 Mb with a N50 scaffold length of 3.5 Mb. Approximately 31.7% of the genome consisted of repetitive elements. A total of 35,543 putative protein-coding regions were identified, of which 50% have been functionally annotated. Transcriptomics analysis showed that approximately 64% of the predicted genes (22,617 genes) were found to be expressed in the tissues sampled. Comparative genomics analysis revealed that the anti-freeze glycoprotein (AFGP) locus of D. eleginoides does not contain any AFGP proteins compared to the same locus in the Antarctic toothfish (Dissostichus mawsoni). This is in agreement with previously published results looking at hybridization signals and confirms that Patagonian toothfish do not possess AFGP coding sequences in their genome. The high-quality genome assembly of the Patagonian toothfish will provide a valuable genetic resource for ecological and evolutionary studies on this and other closely related species.
- organism: Drosophila
- assay: whole-genome sequencing
- research focus: genome assembly
- approach: statistical model
- Citations
- 0
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