Designing a transcriptome next-generation sequencing project for a nonmodel plant species1

Susan R. Strickler, Aureliano Bombarely, Lukas A. Mueller
2012 American Journal of Botany  
Sample choice and treatment -Sample choice is an important fi rst step in any transcriptomic study. Material must be chosen that will generate relevant data. This requires consideration of background information about the species of interest, determining which tissues and developmental stages, treatments, and controls will be used for RNA extraction, the amount of sequencing necessary, and proper RNA treatment prior to sequencing. Species background information -Both variant alleles of a gene
more » ... d gene duplications can complicate transcript assembly by making it diffi cult to distinguish between sequencing error, heterozygosity, and duplicate genes. Model species are usually selfi ng plants; therefore, highly homozygous lines are available. However, nonmodel species may be outcrossers, which 1 Manuscript The application of next-generation sequencing (NGS) to transcriptomics, commonly called RNA-seq, allows the nearly complete characterization of transcriptomic events occurring in a specifi c tissue. It has proven particularly useful in nonmodel species, which often lack the resources available for sequenced organisms. Mainly, RNA-seq does not require a reference genome to gain useful transcriptomic information. In this review, the application of RNA-seq to nonmodel plant species will be addressed. Important experimental considerations from presequencing issues to postsequencing analysis, including sample and platform selection, and useful bioinformatics tools for assembly and data analysis, are covered. Methods of assembling RNA-seq data and analyses commonly performed with RNA-seq data, including single nucleotide polymorphism detection and analysis of differential expression, are explored. In addition, studies that have used RNA-seq to elucidate nonmodel plant transcriptomics are highlighted.
doi:10.3732/ajb.1100292 pmid:22268224 fatcat:ok6srj7mbjdadkaiw47xp2ypeq