Location: Plant, Soil and Nutrition ResearchTitle: High-throughput illumina strand-specific RNA sequencing library preparation Author
Submitted to: Cold Spring Harbor Protocols
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/2/2011
Publication Date: 8/1/2011
Citation: Zhong, S., Joung, J., Zheng, Y., Liu, B., Shao, Y., Xiang, J., Zhangjun, F., Giovannoni, J.J. 2011. High-throughput illumina strand-specific RNA sequencing library preparation. Cold Spring Harbor Protocols. 8:940-949. DOI: 10.1101/pdb.prot5652. Interpretive Summary: Recent advances in DNA sequencing technologies are dramatically changing the way biological scientists approach their trade. Simple, efficient and cost effective DNA sequencing is driving analysis of large scale gene expression activity from more traditional methods to DNA sequence based approaches. Such approaches, however, normally do not distinguish between RNA molecules derived from complimentary strands of the DNA and thus are sometime inaccurate or misleading. Here we describe a novel method for large scale strand-specific DNA sequencing that provides much more accurate gene expression data when applied to cDNA libraries. This protocol will have wide impact in both plant and animal gene expression studies.
Technical Abstract: Conventional Illumina RNA-Seq does not have the resolution to decode the complex eukaryote transcriptome due to the lack of RNA polarity information. Strand-specific RNA sequencing (ssRNA-Seq) can overcome these limitations and as such is better suited for genome annotation, de novo transcriptome assembly and accurate digital gene expression analysis. This protocol describes a simple and robust method to generate ssRNA-Seq libraries for the Illumina sequencing platform. It has significantly increased the throughput to 96 libraries in a two-day preparation while simultaneously lowering the reagent costs to below ten dollars per library. It is compatible with both single-read and paired-end multiplex sequencing and, most importantly, its data can also be used with existing conventional RNA-Seq data. This is a significant advantage, since it enables researchers to switch to ssRNA-Seq even if a large amount of data has already been generated by the non-strand specific methods.