Present and Future Salmonid Cytogenetics

• Muhammet, Gaffaroglu

• Zuzana, Majtánová

• Radka, Symonová

• Šárka, Pelikánová

• Sevgi, Unal

• Zdeněk, Lajbner

• Petr, Ráb

抄録

Salmonids are extremely important economically and scientifically; therefore, dynamic developments in their research have occurred and will continue occurring in the future. At the same time, their complex phylogeny and taxonomy are challenging for traditional approaches in research. Here, we first provide discoveries regarding the hitherto completely unknown cytogenetic characteristics of the Anatolian endemic flathead trout, Salmo platycephalus, and summarize the presently known, albeit highly complicated, situation in the genus Salmo. Secondly, by outlining future directions of salmonid cytogenomics, we have produced a prototypical virtual karyotype of Salmo trutta, the closest relative of S. platycephalus. This production is now possible thanks to the high-quality genome assembled to the chromosome level in S. trutta via soft-masking, including a direct labelling of repetitive sequences along the chromosome sequence. Repetitive sequences were crucial for traditional fish cytogenetics and hence should also be utilized in fish cytogenomics. As such virtual karyotypes become increasingly available in the very near future, it is necessary to integrate both present and future approaches to maximize their respective benefits. Finally, we show how the presumably repetitive sequences in salmonids can change the understanding of the overall relationship between genome size and G+C content, creating another outstanding question in salmonid cytogenomics waiting to be resolved.

source:https://www.mdpi.com/2073-4425/11/12/1462

収録刊行物

• Genes

  Genes 11 (12), 1462-, 2020-12-06

  MDPI

キーワード

• chromosome banding
• cytotaxonomy of trout
• FISH
• NOR phenotype
• rDNA
• Salmo platycephalus

詳細情報

• CRID: 1051694358376639360
• NII論文ID: 120006954116
• ISSN: 20734425
• Web Site: http://id.nii.ac.jp/1394/00001698/
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles