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Establishing Chromosome Genomics in Forage and Turf Grasses

  • D. Kopecký
  • J. Číhalíková
  • J. Kopecká
  • J. Vrána
  • M. Havránková
  • Š. Stočes
  • J. Bartoš
  • H. Šimková
  • J. Šafář
  • M. Kubaláková
  • P. Navrátil
  • J. Doležel
Conference paper

Abstract

Analyses of large genomes are hampered by high proportions of repetitive DNA, that make the assembly of short sequence reads difficult. This is also the case for meadow fescue (Festuca pratensis Huds.), one of predominant grass species in temperate and Northern regions with the genome size estimated at 1C = 3,175 Mbp. This species is known for its ability to survive under freezing conditions and it has been used widely in intergeneric hybridization with various ryegrass species to produce superior Festulolium cultivars. Here we describe attempts to dissect the meadow fescue’s genome into smaller fractions—individual chromosomes and groups of chromosomes. Following the methods of flow cytogenetics developed for legumes and cereals, we have developed a chromosome sorting protocol for grasses and currently we are able to sort F. pratensis chromosome 4 (the largest in the genome) and two groups of three chromosomes each: 2, 3, 7 and 1, 5, 6. As the first step we sequenced chromosome 4 by Illumina with 50x coverage and assembled low copy and genic regions. This facilitated detailed comparative analysis with sequenced genomes of rice, Brachypodium and sorghum and provided the first insight into the genome composition of this species. The possibility to purify chromosome 4 opens the way for a more efficient analysis of genetic loci on this chromosome that control important agronomic traits, such as freezing tolerance. Moreover, purified chromosomes are excellent templates for PCR screening as well as cytogenetic and physical mapping.

Keywords

Bacterial Artificial Chromosome Bacterial Artificial Chromosome Clone Bacterial Artificial Chromosome Library DArT Marker Metacentric Chromosome 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Authors would like to thank Prof. Adam J. Lukaszewski for critical reading and valuable comments.This work has been supported by the Czech Science Foundation (grant award P501/11/0504) and by the Ministry of Education, Youth and Sports of the Czech Republic and the European Regional Development Fund (Operational Programme Research and Development for Innovations No. CZ.1.05/2.1.00/01.0007).

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • D. Kopecký
    • 1
  • J. Číhalíková
    • 1
  • J. Kopecká
    • 1
  • J. Vrána
    • 1
  • M. Havránková
    • 1
  • Š. Stočes
    • 1
  • J. Bartoš
    • 1
  • H. Šimková
    • 1
  • J. Šafář
    • 1
  • M. Kubaláková
    • 1
  • P. Navrátil
    • 1
  • J. Doležel
    • 1
  1. 1.Institute of Experimental BotanyCentre of the Region Haná for Biotechnological and Agricultural ResearchOlomoucCzech Republic

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