Mortonburks1191

RNA interference (RNAi) is a posttranscriptional gene silencing phenomenon induced by double-stranded RNA. It has been widely used as a knockdown technology to analyze gene function in many organisms. In tomato, RNAi technology has widely been used as a reverse genetic tool for functional genomics study. Generally, RNAi is often achieved through transgenes producing hairpin RNA molecules. RNAi lines have the advantage with respect to more modern CRISPR/Cas9 mutants of different levels of downregulation of target gene, and allow the characterization of life-essential genes that cannot be knocked out without killing the organism. Also, RNAi allows to suppress gene expression in multigene families in a regulated manner. In this chapter, an efficient approach to create RNAi stable knockdown-transformed tomato lines is reported. In order, it describes the choice of the target silencing fragment, a highly efficient cloning strategy for the hairpin RNA construct production, a relatively easy procedure to transform and regenerate tomato plants using Agrobacterium tumefaciens and a methodology to test the goodness of the transformation procedure.RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analysis of transcriptomes. It uses high-throughput next-generation sequencing technologies and has revolutionized our understanding of the complexity and dynamics of whole transcriptomes.In this chapter, we recall the key developments in transcriptome analysis and dissect the different steps of the general workflow that can be run by users to design and perform a mRNA-seq experiment as well as to process mRNA-seq data obtained by the Illumina technology. The chapter proposes guidelines for completing a mRNA-seq study properly and makes available recommendations for best practices based on recent literature and on the latest developments in technology and algorithms. We also remark the large number of choices available (especially for bioinformatic data analysis) in front of which the scientist may be in trouble.In the last part of the chapter we discuss the new frontiers of single-cell RNA-seq and isoform sequencing by long read technology.The global climate is changing, resulting in significant economic losses worldwide. selleck inhibitor It is thus necessary to speed up the plant selection process, especially for complex traits such as biotic and abiotic stresses. Nowadays, genomic selection (GS) is paving new ways to boost plant breeding, facilitating the rapid selection of superior genotypes based on the genomic estimated breeding value (GEBV). GEBVs consider all markers positioned throughout the genome, including those with minor effects. Indeed, although the effect of each marker may be very small, a large number of genome-wide markers retrieved by high-throughput genotyping (HTG) systems (mainly genotyping-by-sequencing, GBS) have the potential to explain all the genetic variance for a particular trait under selection. Although several workflows for GBS and GS data have been described, it is still hard for researchers without a bioinformatics background to carry out these analyses. This chapter has outlined some of the recently available bioinformatics resources that enable researchers to establish GBS applications for GS analysis in laboratories. Moreover, we provide useful scripts that could be used for this purpose and a description of key factors that need to be considered in these approaches.Quantitative trait loci mapping has become a common practice in crop plants and can be accomplished using either biparental populations following interval mapping or natural populations following the approach of association mapping. Because of its ability to use the natural diversity and to search for functional variants in a broader germplasm, association mapping is becoming popular among researchers. An overview of the different steps involved in association mapping in plants is provided in this chapter.Forward genetic analysis remains as one of the most powerful tools for assessing gene functions, although the identification of the causal mutation responsible for a given phenotype has been a tedious and time-consuming task until recently. Advances in deep sequencing technologies have provided new approaches for the exploitation of natural and artificially induced genetic diversity, thus accelerating the discovery of novel allelic variants. In this chapter, a mapping-by-sequencing forward genetics approach is described to identify causal mutations in tomato (Solanum lycopersicum L.), a major crop species that is also a model species for plant biology and breeding.Most plant agronomic traits are quantitatively inherited. Identification of quantitative trait loci (QTL) is a challenging target for most scientists and crop breeders as large-scale genotyping is difficult. Molecular marker technology has continuously evolved from hybridization-based technology to PCR-based technology, and finally, sequencing-based high-throughput single-nucleotide polymorphisms (SNPs). High-throughput sequencing technologies can provide strategies for sequence-based SNP genotyping. Here we describe the SLAF-seq that can be applied as the SNP genotyping approach. The high-throughput SNP genotyping methods will prove useful for the construction of high-density genetic maps and identification of QTLs for their deployment in plant breeding and facilitate genome-wide selection (GWS) and genome-wide association studies (GWAS).High-resolution melting (HRM) analysis is a cost-effective, specific, and rapid tool that allows distinguishing genetically related plants and other organisms based on the detection of small nucleotide variations, which are recognized from melting properties of the double-stranded DNA. It has been widely applied in several areas of research and diagnostics, including botanical authentication of several food commodities and herbal products. Generally, it consists of the main steps (1) in silico sequence analysis and primer design; (2) DNA extraction from plant material; (3) amplification by real-time PCR with an enhanced fluorescent dye targeting a specific DNA barcode or other regions of taxonomic interest (100-200 bp); (4) melting curve analysis; and (5) statistical data analysis using a specific HRM software. This chapter presents an overview of HRM analysis and application, followed by the detailed description of all the required reagents, instruments, and protocols for the successful and easy implementation of a HRM method to differentiate closely related plant species.