New platforms and protocols for RNA sequencing have come on board in recent years, giving rise to new studies comparing mRNA performance, and the journal Nature Biotechnology recently published a focus report featuring five such studies. Three of the studies looked particularly at outcomes from the large-scale U.S. Food and Drug Administration-led Sequencing Quality Control (SEQC) study, a multisite, cross-platform analysis of RNA-sequencing measurement performance in a controlled setting, also known as MicroArray Quality Consortium III.

In one of the SEQC studies, researchers evaluated RNA sequencing performance across multiple laboratory sites using Illumina HiSeq, Life Technologies SOLiD, and Roche 454 platforms. The timing was ripe to perform this cross-platform and cross-lab study as large projects like the Encyclopedia of DNA Elements, The Cancer Genome Atlas, and various efforts of the International Cancer Genome Consortium have been completed. Reproducibility across labs is crucial as the technology moves closer to routine clinical use.

The investigators used reference RNA samples with built-in controls to assess RNA sequencing for junction discovery and differential expression profiling. This was compared to microarray and quantitative PCR (qPCR) data using complementary metrics. If specific filters were used, relative expression was found to be accurate and reproducible across sites and platforms. However, that did not hold true for absolute measurements.

From the study summary: “… 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.”

The sheer size of the data set used and complexity of measurements expands the knowledge of the RNA-seq and how it can be used for transcriptome profiling. More than 100 billion reads (10 terabases) of RNA-seq data were analyzed, which, according to the study authors, is the largest study of its kind to date. The study and data collection constitute “a milestone in the development and dissection of RNA-seq as a method for transcriptome profiling,” wrote the investigators.

Read more about the study.