title: Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling.
abstract: BACKGROUND
  RNA sequencing (RNA-Seq) is often used for transcriptome profiling as well as the identification of novel transcripts and alternative splicing events. Typically, RNA-Seq libraries are prepared from total RNA using poly(A) enrichment of the mRNA (mRNA-Seq) to remove ribosomal RNA (rRNA), however, this method fails to capture non-poly(A) transcripts or partially degraded mRNAs. Hence, a mRNA-Seq protocol will not be compatible for use with RNAs coming from Formalin-Fixed and Paraffin-Embedded (FFPE) samples.
  
  
  RESULTS
  To address the desire to perform RNA-Seq on FFPE materials, we evaluated two different library preparation protocols that could be compatible for use with small RNA fragments. We obtained paired Fresh Frozen (FF) and FFPE RNAs from multiple tumors and subjected these to different gene expression profiling methods. We tested 11 human breast tumor samples using: (a) FF RNAs by microarray, mRNA-Seq, Ribo-Zero-Seq and DSN-Seq (Duplex-Specific Nuclease) and (b) FFPE RNAs by Ribo-Zero-Seq and DSN-Seq. We also performed these different RNA-Seq protocols using 10 TCGA tumors as a validation set.The data from paired RNA samples showed high concordance in transcript quantification across all protocols and between FF and FFPE RNAs. In both FF and FFPE, Ribo-Zero-Seq removed rRNA with comparable efficiency as mRNA-Seq, and it provided an equivalent or less biased coverage on gene 3' ends. Compared to mRNA-Seq where 69% of bases were mapped to the transcriptome, DSN-Seq and Ribo-Zero-Seq contained significantly fewer reads mapping to the transcriptome (20-30%); in these RNA-Seq protocols, many if not most reads mapped to intronic regions. Approximately 14 million reads in mRNA-Seq and 45-65 million reads in Ribo-Zero-Seq or DSN-Seq were required to achieve the same gene detection levels as a standard Agilent DNA microarray.
  
  
  CONCLUSIONS
  Our results demonstrate that compared to mRNA-Seq and microarrays, Ribo-Zero-Seq provides equivalent rRNA removal efficiency, coverage uniformity, genome-based mapped reads, and consistently high quality quantification of transcripts. Moreover, Ribo-Zero-Seq and DSN-Seq have consistent transcript quantification using FFPE RNAs, suggesting that RNA-Seq can be used with FFPE-derived RNAs for gene expression profiling.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhao
authors.forename: Xiaping
authors.initials: X
authors.lastname: He
authors.forename: Katherine A
authors.initials: KA
authors.lastname: Hoadley
authors.forename: Joel S
authors.initials: JS
authors.lastname: Parker
authors.forename: David Neil
authors.initials: DN
authors.lastname: Hayes
authors.affiliation: Curriculum in Bioinformatics and Computational Biology, The University of North Carolina at Chapel Hill, Chapel Hill NC 27599, USA. cperou@med.unc.edu.
authors.forename: Charles M
authors.initials: CM
authors.lastname: Perou
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis; D016612:Paraffin Embedding / Q000379:methods; D011061:Poly A / Q000235:genetics*; D012313:RNA / Q000145:classification / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D012335:RNA, Ribosomal / Q000235:genetics*; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA; D016707:Tissue Fixation
chemical_list: D012333:RNA, Messenger; D012335:RNA, Ribosomal; D011061:Poly A; D012313:RNA