Enfoques genómicos y transcriptómicos hacia la selección de plantas

Autores/as

  • Seyed Mehdi Jazayeri Universidad Nacional de Colombia
  • Ronald Villamar Torres Université de Montpellier

DOI:

https://doi.org/10.26910/issn.2528-8083vol2iss8.2017pp54-64

Palabras clave:

genomics, omics, plant selection, plant improvement

Resumen

La era Omica ha abierto una nueva ventana a la biología. La genómica y la transcriptómica son dos campos conocidos, con los cuales, la selección y el mejoramiento de plantas se estudian con mayor facilidad y precisión. Proporcionan información útil sobre los genes, las transcripciones, sus funciones y sirven como datos primordiales para otros enfoques posteriores. Los genomas de referencia de varias plantas han sido secuenciados, y están disponibles, facilitando así el acceso a información ómica indispensable para llevar a cabo estudios basados ​​en estos mismos genomas. El total de datos genómicos, transcriptómicos y los hallazgos de métodos variantes que van desde QTL (rasgo cuantitativo), PSN (polimorfismo de un solo nucleótido), NCV (número de copias variante), GBS (genoma por secuencia) son extremadamente importantes para la selección y el mejoramiento de plantas en términos de precio y tiempo. Los nuevos flujos de trabajo utilizan diferentes enfoques basados ​​en la información genómica / transcriptómica en pasos posteriores mezclándolos y se validan durante todo el proceso para seleccionar genotipos que posean un rasgo deseado agronómicamente importante. SNP-Seq, que se presenta en este artículo, es un nuevo enfoque para analizar las plantas hacia la selección y la detección mediante secuenciación de SNP en varios genotipos simultáneamente. Este proceso puede acelerar el ciclo de selección de plantas desde los genotipos a los fenotipos en una forma de ingeniería inversa.

 

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2017-12-29
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Jazayeri, S. M., & Torres, R. V. (2017). Enfoques genómicos y transcriptómicos hacia la selección de plantas. Journal of Science and Research, 2(8), 54–64. https://doi.org/10.26910/issn.2528-8083vol2iss8.2017pp54-64

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