Abstract
The quality of RNA sequencing data relies on specific priming by the primer used for reverse transcription (RT-primer). Nonspecific annealing of the RT-primer to the RNA template can generate reads with incorrect cDNA ends and can cause misinterpretation of data (RT mispriming). This kind of artifact in RNA-seq based technologies is underappreciated and currently no adequate tools exist to computationally remove them from published data sets. We show that mispriming can occur with as little as two bases of complementarity at the 3' end of the primer followed by intermittent regions of complementarity. We also provide a computational pipeline that identifies cDNA reads produced from RT mispriming, allowing users to filter them out from any aligned data set. Using this analysis pipeline, we identify thousands of mispriming events in a dozen published data sets from diverse technologies including short RNA-seq, total/mRNA-seq, HITS-CLIP, and GRO-seq. We further show how RT mispriming can lead to misinterpretation of data. In addition to providing a solution to computationally remove RT-misprimed reads, we also propose an experimental solution to completely avoid RT-mispriming by performing RNA-seq using thermostable group II intron derived reverse transcriptase (TGIRT-seq).