Back

A chromosome-level genome of the King penguin (Aptenodytes patagonicus): an emerging model-in-the-wild for studying evolution

Paris, J. R.; Fernandes, F. A. N.; Santos, C. A.; Pointon, D.-L. B.; Wood, J. M. D.; Obiol, J. F.; Salces-Oritz, J.; Fernandez, R.; Cristofari, R.; Le Bohec, C.; Trucchi, E.

2025-03-15 genomics
10.1101/2025.03.13.642884 bioRxiv
Show abstract

The King penguin (Aptenodytes patagonicus) is an iconic species of the Southern Ocean and is currently being developed as a model-in-the-wild for understanding evolution. We present a high-quality, haplotype-resolved 1.35 Gb chromosome-level genome of an adult female King penguin - Pen/Se-guin - from the Crozet Archipelago, assembled using PacBio HiFi long-read sequencing and Hi-C proximity data. 94.93% of the assembly is assigned to 34 chromosomes (32 autosomes, plus the Z and W chromosomes), with a BUSCO completeness of 97.2%, a k-mer completeness of 99.7%, and a quality value (QV) of 63.8. We also assembled a circularised mitogenome (20,520 bp), which includes the avian tandem duplication (TD). Annotation of repetitive sequences revealed that 16.3% of the genome comprises repetitive elements, with LINEs being the most abundant transposable element class (5.6%). Gene prediction using an extensive multi-tissue RNA-seq dataset resulted in 18,081 predicted protein-coding genes, of which 17,081 were functionally annotated, with a BUSCO completeness of 98.4% and an OMArk completeness of 97.3%. The presented assembly substantially improves the quality of a previous draft genome, showing a 28-fold increase in assembly contiguity and a significantly improved genome annotation, exceeding the standards of the Earth BioGenome Project (EBP) and Vertebrate Genomes Project (VGP). This high-quality genome will enable ongoing and future studies harnessing the King penguin as a model-in-the-wild to test hypotheses on the genotype-to-fitness link, ageing, life-history trait evolution, and adaptation.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.