Jul 9, 2016

RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods

BioRxiv : the Preprint Server for Biology
Aliaksei Z HolikMatthew E. Ritchie

Abstract

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-sequen...Continue Reading

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Mentioned in this Paper

Study
Small Nuclear RNA
RNA, Untranslated
Sequence Determinations, RNA
Virus Replication
Cell Culture Techniques
Profile (Lab Procedure)
Gene Expression
Poly A
Small Nucleolar RNA Location

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