RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods
AuthorHolik, AZ; Law, CW; Liu, R; Wang, Z; Wang, W; Ahn, J; Asselin-Labat, M-L; Smyth, GK; Ritchie, ME
Source TitleNucleic Acids Research
PublisherOXFORD UNIV PRESS
University of Melbourne Author/sAsselin-Labat, Marie-Liesse; Law, Charity; Smyth, Gordon; Ritchie, Matthew; HOLIK, ALIAKSEI; LIU, RUIJIE
AffiliationMedical Biology (W.E.H.I.)
School of Mathematics and Statistics
Document TypeJournal Article
CitationsHolik, A. Z., Law, C. W., Liu, R., Wang, Z., Wang, W., Ahn, J., Asselin-Labat, M. -L., Smyth, G. K. & Ritchie, M. E. (2017). RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods. NUCLEIC ACIDS RESEARCH, 45 (5), https://doi.org/10.1093/nar/gkw1063.
Access StatusOpen Access
Carefully designed control experiments provide a gold standard for benchmarking different genomics research tools. A shortcoming of many gene expression control studies is that replication involves profiling the same reference RNA sample multiple times. This leads to low, pure technical noise that is atypical of regular studies. To achieve a more realistic noise structure, we generated a RNA-sequencing mixture experiment using two cell lines of the same cancer type. Variability was added by extracting RNA from independent cell cultures and degrading particular samples. The systematic gene expression changes induced by this design allowed benchmarking of different library preparation kits (standard poly-A versus total RNA with Ribozero depletion) and analysis pipelines. Data generated using the total RNA kit had more signal for introns and various RNA classes (ncRNA, snRNA, snoRNA) and less variability after degradation. For differential expression analysis, voom with quality weights marginally outperformed other popular methods, while for differential splicing, DEXSeq was simultaneously the most sensitive and the most inconsistent method. For sample deconvolution analysis, DeMix outperformed IsoPure convincingly. Our RNA-sequencing data set provides a valuable resource for benchmarking different protocols and data pre-processing workflows. The extra noise mimics routine lab experiments more closely, ensuring any conclusions are widely applicable.
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