Marker-assisted selection and protein identification in Eucalyptus

Authors

DOI:

https://doi.org/10.33448/rsd-v11i6.29227

Keywords:

Bibliometrics; Forest improvement; MAS; Teaching.

Abstract

Marker-assisted selection aims to improve quantitative traits in Eucalyptus, and molecular markers allow the identification of (the expression) of challenging alleles. Aiming to overcome this challenge our work presents a systematic review of the use of marker-assisted selection and candidate proteins to identify markers associated with traits of Eucalyptus. The research carried out a prospection of content obtained by keywords in the scientific databases Scopus and Web of Science. The bibliographic data generated was imported using the Bibliometrix package in R software. The nucleic acid sequences were prospected in the Genetic Sequence Database – GenBank. The proteins were prospected in UNIPROT and the 3D structures were obtained at the Swiss Bioinformatics Institute - SIB. The prospection resulted in 63 scientific articles after duplicates were removed, from 41 journals, with an average publication of 12.4 articles per year and an average of 25.2 citations per document. China and Brazil stand out in the number of publications. RAPD technique was the most used in studies to obtain alleles with traits of interest in Eucalyptus. The ISSR and SSR markers were highlighted for studies on genotype resistance. The sequences associated with resistance to galling organisms in Eucalyptus are related to TAC1 transcription regulatory proteins, mediator 2; zinc finger protein 11, associated with the expressed genes TAC1, At5g64680, and ZFP11.

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03/05/2022

How to Cite

CALAZANS, C. C.; NUNES, V. V. .; SOUZA, J. L.; SANTANA, N. A.; ALMEIDA, F. E. de .; SILVA-MANN, R. Marker-assisted selection and protein identification in Eucalyptus. Research, Society and Development, [S. l.], v. 11, n. 6, p. e45311629227, 2022. DOI: 10.33448/rsd-v11i6.29227. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/29227. Acesso em: 23 nov. 2024.

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Agrarian and Biological Sciences