Let’s start with what we know. We know about RNA-seq and we’re familiar with the power to discover differentially expressed transcripts between different experimental conditions. We know that about 65% of the projects we’ve finished at Cofactor over the last year or so use RNA-seq, with the majority of those projects looking to address differential transcript expression. We also know that the ability to monitor the transcriptome has moved at an incredible rate over the last decade or so with RNA-seq and Microarrays revealing many important biological insights.
In light of the enormous success and insight RNA-seq experiments have yielded, some researchers have started to explore aspects of the proteome at a higher resolution. However, determining the information encoded in the genome at the protein-coding level remains a challenge. Techniques such as polysome profiling using microarray, or whole proteome analysis by mass spec have their drawbacks, due in part to technical challenges, inconsistency and sensitivity. Some of the disadvantages of those approaches may be overcome by deep-sequencing techniques such as Ribosomal-profiling, also known as “ribo-seq”.
What is ribo-seq? Simply put, ribo-seq is a way of addressing what portion of the transcriptome is actively translated into proteins. By enriching RNA fragments or ‘footprints’ of actively translating messages, we’re able to get a high resolution view of this data. Most of the work describing the technique and application has been pioneered by the Weissman lab, with commercial kits for preparing ribosome protected RNAs into next-gen libraries for sequencing now available.
RNA-seq provides researchers with an illuminating snapshot of the transcriptome while ribo-seq zooms in on positional and quantitative aspects of the proteins being produced in the cell. The results of early studies have revealed novel insights into open reading frames (ORFs), translation rate(s) and even identified a novel class of lincRNAs associated with ribosomes. All of these aspects will lead to a deeper understanding of cell types and yield new protein information at a higher resolution than had been previously achievable.
