Abstract
Omics methods such as transcriptomics allow us to understand the molecular mechanisms and the genetic basis underlying abiotic and biotic stress responses, in addition to target traits, and enable optimization of breeding strategies for the development of new cultivars of agricultural crops. Multiomics integration analyses can reveal the connections between, and provide valuable insights into different biological factors responsible for important traits in crops. To maximize gene discovery in RNA studies, it is necessary to follow three essential steps: experimental design, high-throughput sequencing, and data analysis. In addition to the abundant RNA sequencing (RNA-Seq) data available for main crops, transcript data from orphan crops also have great potential benefits for plant breeding, presenting complex and valuable gene pools for the improvement of future crops. Transcriptomic approaches make valuable contributions to the understanding of the mechanisms involved in stress responses in plants, including biotic stresses, providing important insights into the interactions of hosts with pathogens and pests and into plant responses to abiotic stress, such as cold/heat tolerance. The evolution of methodologies for the development of new plant cultivars with both desirable productivity and high tolerance to biotic and abiotic stresses and future breeding decisions can also benefit greatly from the incorporation of multiomics techniques. In this chapter, we present the main RNA-Seq data approaches that can be exploited for improved breeding of important crops, based primarily on creating machine learning predictive systems, multivariate and Bayesian statistics, and complex network algorithms to model inter- and intraomic interactions.
Published Version
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