bioRxivMolecular BiologyPreprint
Comparative Evaluation of Targeted RNA Sequencing Protocols for Gene Expression Quantification With and Without Unique Molecular Indices (UMIs)
Abstract
Interest in forensic RNA analysis has increased over the last years. RNA molecules present in forensic samples can accurately be quantified via quantitative PCR (qPCR), however, due to the limited number of markers that can be assayed simultaneously per reaction, qPCR is less suitable for applications requiring gene expression quantification of large marker sets. Few years ago, massively parallel targeted RNA-sequencing (targRNAseq) allowing to simultaneously and accurately quantify several hundreds of markers has been added to the forensic genetic tool set. However, typical targRNAseq protocols include a multiplex-PCR-step to amplify selected targets which potentially introduces bias and limits accurate gene expression quantification. Unique Molecular Indices (UMIs) have been invented to overcome this limitation and have been implemented in protocols from some vendors. In this study, we compared two targeted RNAseq protocols assaying expression of a set of 121 forensically relevant mRNA biomarkers: The Ion Ampliseq targeted RNA sequencing panel (Thermo Fisher Scientific), which employs a multiplex-PCR without the use of UMIs, and the QIAseq targeted RNA panel (QIAGEN), which uses UMIs prior to multiplex amplification. Both protocols were tested on replicated samples and dilution series and compared with respect to sensitivity and accuracy of gene expression quantification. The UMI-based protocol exhibited decreased sensitivity in comparison to the non-UMI-based alternative, however, making use of UMI technology greatly improved gene expression quantification accuracy. We thus recommend the use of UMI-based protocols for targeted RNA sequencing for applications requiring accurate gene expression quantification.
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: benchmarking
- research focus: variant calling
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 2
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