The authors of a recent review1, make some key points about some of the challenges that are general to all RNA-seq experiments:
RNA-Seq rules! – “By comparison to these [other next-gen sequencing] applications, RNA-sequencing (RNA-seq) may be leading the pack in popularity because of its ability to characterize transcriptomes, to assess differential gene expression and to essentially challenge the continued use of microarray technology for studying transcription.”
There is no magic to RNA-Seq. – “We are concerned that next-generation technologies will, most likely, remain expensive for a while, and that there may be an inclination to revert to single sample science that is void of any ability to estimate biological and/or technical variation, or to test scientific hypotheses.”
The same as microarrays…but different – “Because lane-specific coverage, substitution errors and the resulting alignment are all known issues, any of the previous analogies that we have made to microarrays end here. The standard normalizing techniques for microarray data do not apply.”
The authors agree with the findings of our reader poll. – “… as the complexities of experiments continue to grow, there is still no standard practice that allows for design, processing, normalization, efficient dimension reduction and/or statistical analysis.”
When asked: Do we yet have a firm handle on the most appropriate/accurate pipeline for analysis of RNA-Seq datasets?
- Auer PL, Srivastava S, Doerge RW. (2011) Differential expression–the next generation and beyond. Brief Funct Genomics [Epub ahead of print]. [abstract]