RNA-Seq promises to be used in clinical settings as a gene-expression profiling tool;however,questions about its variability and biases remain and need to be addressed.Thus,RNA controls with known concentrations and s...RNA-Seq promises to be used in clinical settings as a gene-expression profiling tool;however,questions about its variability and biases remain and need to be addressed.Thus,RNA controls with known concentrations and sequence identities originally developed by the External RNA Control Consortium(ERCC) for microarray and qPCR platforms have recently been proposed for RNA-Seq platforms,but only with a limited number of samples.In this study,we report our analysis of RNA-Seq data from 92 ERCC controls spiked in a diverse collection of 447 RNA samples from eight ongoing studies involving five species(human,rat,mouse,chicken,and Schistosoma japonicum) and two mRNA enrichment protocols,i.e.,poly(A) and RiboZero.The entire collection of datasets consisted of 15650143175 short sequence reads,131603796(i.e.,0.84%) of which were mapped to the 92 ERCC references.The overall ERCC mapping ratio of 0.84% is close to the expected value of 1.0% when assuming a 2.0% mRNA fraction in total RNA,but showed a difference of 2.8-fold across studies and 4.3-fold among samples from the same study with one tissue type.This level of fluctuation may prevent the ERCC controls from being used for cross-sample normalization in RNA-Seq.Furthermore,we observed striking biases of quantification between poly(A) and RiboZero which are transcript-specific.For example,ERCC-00116 showed a 7.3-fold under-enrichment in poly(A) compared to RiboZero.Extra care is needed in integrative analysis of multiple datasets and technical artifacts of protocol differences should not be taken as true biological findings.展开更多
文摘RNA-Seq promises to be used in clinical settings as a gene-expression profiling tool;however,questions about its variability and biases remain and need to be addressed.Thus,RNA controls with known concentrations and sequence identities originally developed by the External RNA Control Consortium(ERCC) for microarray and qPCR platforms have recently been proposed for RNA-Seq platforms,but only with a limited number of samples.In this study,we report our analysis of RNA-Seq data from 92 ERCC controls spiked in a diverse collection of 447 RNA samples from eight ongoing studies involving five species(human,rat,mouse,chicken,and Schistosoma japonicum) and two mRNA enrichment protocols,i.e.,poly(A) and RiboZero.The entire collection of datasets consisted of 15650143175 short sequence reads,131603796(i.e.,0.84%) of which were mapped to the 92 ERCC references.The overall ERCC mapping ratio of 0.84% is close to the expected value of 1.0% when assuming a 2.0% mRNA fraction in total RNA,but showed a difference of 2.8-fold across studies and 4.3-fold among samples from the same study with one tissue type.This level of fluctuation may prevent the ERCC controls from being used for cross-sample normalization in RNA-Seq.Furthermore,we observed striking biases of quantification between poly(A) and RiboZero which are transcript-specific.For example,ERCC-00116 showed a 7.3-fold under-enrichment in poly(A) compared to RiboZero.Extra care is needed in integrative analysis of multiple datasets and technical artifacts of protocol differences should not be taken as true biological findings.
