An improved protocol for the production of AFLPTM markers in complex genomes by means of capillary electrophoresis

Journal of Animal Breeding and Genetics - Tập 122 Số 1 - Trang 62-68 - 2005
Riccardo Papa1, Michela Troggio2, Paolo Ajmone‐Marsan3, Francesco Nonnis Marzano1
1Dipartimento di Biologia Evolutiva e Funzionale, University of Parma, Parma, Italy
2Dipartimento di Scienze Ambientali, University of Parma, Parma, Italy
3Istituto di Zootecnica, Universitá Cattolica del Sacro Cuore, Piacenza, Italy

Tóm tắt

SummaryThe amplified fragment‐length polymorphism (AFLP) technology is a recently introduced method to investigate genomes of different complexity, from microbial to higher organisms. It is applied to purposes as diverse as identification of species, strain and varieties, investigation of genetic diversity within and between populations, simple and complex trait mapping, and construction of linkage and physical maps. This technology has been designed on the use of primers labelled with radioactivity and on AFLP fragment separation on sequencing gel. We show that the original EcoRI/TaqI AFLP protocol does not perform appropriately when transferred to fluorescent labelling and capillary electrophoresis (CE), and propose an improved protocol for the production of high‐quality AFLP markers in fish, rodents and artiodactyles by means of the Beckman‐Coulter CEQ2000 automatic DNA sequencer. In addition, we describe the procedure routinely used in our laboratory to obtain binary matrices from AFLP profiles with the aid of Genographer free‐share software (vers. 1.6.0, J.J. Benham, Montana State University), able to elaborate original fragment data and convert them to standard graphical formats for phylogenetic analyses. Comparison with radioactive AFLPs in goats confirmed the reliability of the protocol developed for CE. In fact, 107 fragments generated by two primer combinations and identified by both techniques were attributed the same scoring. Compared with traditional methods, the use of capillary systems and automated analysis increases data throughput and scoring reliability, decreasing the overall experimental error.

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