A flurry of papers in May of 2008 introduced the world to RNA-Seq. This new technology provided a higher resolution picture of transcription than was possible ever before. Nearly five years on, we look back at the publications that upset the microarray industry and sent tweed-clad professors scrambling for the nearest R tutorial. Here’s a rough guide to the five papers that announced the birth of this technology.
Nagalakshmi and Wang et al., Science | Online: 5/1/2008 In print:6/6/2008
The Transcriptional Landscape of the Yeast Genome Defined
This paper won the race to publication, online, by a day. Nagalakshmi and colleagues at the Snyder Lab combined the new method with 5’-RACE to show that nearly three quarters of the non-repetitive S. cerevisiae genome is transcribed.
Lister, O’Malley, Tonti-Filippini et al., Cell | Online: 5/2/2008 In print: 5/2/2008
Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis
Epigeneticists might want to delve into this paper as their first RNA-Seq read. This team from Southern California and Australia examined genome wide cytosine methylation using methylC-Seq (bisulfite sequencing) and related it to small RNA expression in Arabadopsis. They also did what they called mRNA-Seq, seemingly as an afterthought. But they ended up producing a rather comprehensive view of global transcriptional activity and regulation. While they were beat to the press by a day and none of their method naming stuck, they probably don’t care. They have way better weather than you do.
Wilhelm and Marguerat et al., Nature | Online: 5/18/2008 In print:6/26/2008
Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution
If you favor fission yeast to the budding variety, you will like work from the venerable Wellcome Trust Sanger Institute. This group paired RNA-Seq with tilling arrays to paint a picture of eukaryotic transcription that focused on variable splicing.
Cloonan, Forrest, Kolle, et al., Nature Methods | Online: 5/30/2008 In print: 7/2008
Stem cell transcriptome profiling via massive-scale mRNA sequencing
This paper takes the prize for least effective method branding. SQRL, their acronym for short quantitative random RNA libraries, simply didn’t stick. But the Aussies and their collaborators at Applied Biosystems get extra points for being the only group not to use an Illumina sequencer, reading their nucleotides instead with SOLiD technology.
Mortazavi and Williams et al., Nature Methods | Online: 5/30/2008 In print: 7/2008
Mapping and quantifying mammalian transcriptomes by RNA-Seq
This work maximized bang-for-buck from the new method by using RNA-Seq to characterize and quantify transcription. This CalTech team probed the transcriptional repertoire of mouse tissues with spike-in controls from Arabadopsis and phage lambda to show that gene expression can be quantified using the new method. They introduced us to the variable RPKM (reads per kilobase of exon model per million mapped reads) and paved the way for differential expression analyses using RNA-Seq.