Gene Expression Analysis Using RNA-Seq from Organisms Lacking Substantial Genomic Resources

Abstract

Development of massively parallel “next generation” sequencing technology (NGS) has dramatically revolutionized biological studies. Among the many applications of NGS, RNASeq is one of the most important uses of this technology. RNA-Seq enables investigators to accurately probe the current state of a transcriptome and assess many biologically important issues, such as; gene expression levels, differential splicing events, and allele-specific gene expression. Compared with previous technologies (e.g., microarrays, etc.) NGS has the clear advantage of not being limited to experimental systems having well characterized genomes or transcript sequence libraries. This positions RNA-seq approaches as important and versatile techniques for experimental systems and species where specific genetic information may be limited or altogether lacking. A major goal of most transcriptomic studies is the identification and characterization of all transcripts within a developmental stage or specific tissue. NGS techniques have made the massive amount of data required to carry out such studies both inexpensive and available to an unprecedented extent. Clever computer algorithms have made the assembly of these massive data sets the work of one or two people with reasonably powerful workstations or a moderate analytical server. Once a reference transcriptome has been assembled, analyses can be carried out that involve several steps, such as; mapping short sequence reads to transcriptome, quantifying the abundance of genes or gene sets, and comparing differential expression patterns among all samples. Herein we outline the processes from obtaining raw short read data to advanced comparative gene expression analysis and we review bioinformatic programs currently available, such as Tophat, Cufflinks, DESeq, that are specifically designed to address each of the above steps. We will discuss both accuracy and ease of use of these tools by biologists beginning to pursue these types of analyses. In addition to individual programs, we will also discuss integration of multiple programs into pipelines for more rapid and complete expression analyses. Overall, the future applications of RNA-Seq will open new avenues for transcriptome analyses of less well-studied and/or wild caught species that could not have previously been approached. This will yield a wealth of new comparative data highlighting the many ways plants and animals have developed to survive in this rapidly changing environment.