01916nas a2200253   4500008004100000022001400041245013800055210006900193260001600262300001400278490000700292520118400299653000801483653001201491653000901503100001101512700001601523700000501539700001501544700000501559700001601564700001101580856007101591       2014                        eng d  a1546-169600aA comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.0 acomprehensive assessment of RNAseq accuracy reproducibility and   c2014 Aug 24  a903–9140 v323 aWe 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.10aNGS10aRNA-seq10aSEQC1 aSu, Z.1 aLabaj, P.P.1 a1 aDopazo, J.1 a1 aMason, C.E.1 aShi, L  uhttp://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2957.html