A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.
Bottom Line: In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR.Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling.The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.
We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.
Mentions: After an examination of the reliability of differential expression analysis of genes, we next examined the quantification of RNA using four consistency tests exploiting ground truths built into the study design (Fig. 4). First, we considered titration-order consistency as introduced in Figure 1c. This metric is affected both by systemic distortions reducing accuracy and random variations reducing reproducibility. The majority of genes (59%) titrated correctly (Fig. 4a), with little disagreement between platforms (Supplementary Table 5). Genes with large differential expression performed best, with all genes showing consistent titration in several HiSeq 2000 and SOLiD sites, and no contradiction regarding the direction of change (blue curve). For the second built-in truth, we examined the A/B mixing ratio recovery (Fig. 1d) as another test reflecting accuracy and reproducibility. We observed the correct ratio for the majority of genes (Fig. 4b), with better agreement at higher expression levels (top 25%). Notably, the scatter of genes marked as titrating in this plot indicates that consistent titration does not guarantee a reliable recovery of the mixing ratio (and vice versa).