Special Issue "Plant Genetics and Molecular Breeding"
Deadline for manuscript submissions: closed (20 February 2019)
Dr. Pedro Martínez-Gómez
CEBAS- CSIC, Centro de Edafología y Biología Aplicada del Segura, Department of Plant Breeding, Murcia, Spain
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Interests: plant breeding approaches; marker assisted selection; evaluation of agronomical traits; integrating genetic, genomic, transcriptomic and proteomic approaches
The development of new plant varieties is a long and tedious process involving the generation of large seedling populations for the selection of the best individuals. While the ability of breeders to generate large populations is almost unlimited, the management, phenotyping (genetic studies) and selection of these seedlings are the main factors limiting the generation of new cultivars. Genomic (DNA) studies for the development of marker-assisted selection (MAS) strategies are particularly useful when the evaluation of the character is expensive, time-consuming or with long juvenile periods. More recently, proteomic (proteins and enzymes) and transcriptomic (RNA) studies have been used for the clarification of the mentioned genomic studies.
Papers submitted to this Special Issue must report high novelty results and/or plausible and testable new models for the integrative analysis of the different approaches applied to plant breeding including genetic (phenotyping and transmission of agronomic characters), genomic (DNA regions responsible for the different agronomic characters), proteomic (proteins and enzymes involved in the expression of the characters) and transcriptomic (gene expression analysis of the characters) approaches for the development of new MAS strategies. In addition, the application of massive sequencing methodologies ("deep-sequencing") of the genome (DNA-Seq) and transcriptome (RNA-Seq), based on lowering the costs of DNA sequencing, could be an additional interesting approach in this Special Issue.
Dr. Pedro Martínez-Gómez
Manuscript Submission Information
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- throughput analysis
- assisted selection