We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States 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 these and 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.

RNA-seq facilitates unbiased genome-wide gene-expression profiling. However, its concordance with the well-established microarray platform must be rigorously assessed for confident uses in clinical and regulatory application. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same set of liver samples of rats under varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOA). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is highly correlated with treatment effect size, gene-expression abundance and the biological complexity of the MOA. RNA-seq outperforms microarray (90% versus 76%) in DEG verification by quantitative PCR and the main gain is its improved accuracy for low expressed genes. Nonetheless, predictive classifiers derived from both platforms performed similarly. Therefore, the endpoint studied and its biological complexity, transcript abundance, and intended application are important factors in transcriptomic research and for decision-making.

RNA-directed DNA methylation, one of several RNA interference-mediated pathways in the nucleus, has been documented in plants and in human cells. Despite progress in identifying the DNA methyltransferases, histone-modifying enzymes and RNA interference proteins needed for RNA-directed DNA methylation, the mechanism remains incompletely understood. We screened for mutants defective in RNA-directed DNA methylation and silencing of a transgene promoter in Arabidopsis thaliana and identified three drd complementation groups. DRD1 is a SNF2-like protein required for RNA-directed de novo methylation. We report here that DRD2 and DRD3 correspond to the second-largest subunit and largest subunit, respectively, of a fourth class of DNA-dependent RNA polymerase (polymerase IV) that is unique to plants. DRD3 is a functionally diversified homolog of NRPD1a or SDE4, identified in a separate screen for mutants defective in post-transcriptional gene silencing. The identical DNA methylation patterns observed in all three drd mutants suggest that DRD proteins cooperate to create a substrate for RNA-directed de novo methylation.

SummaryArabidopsis thaliana is a host for the sugar beet cyst nematode Heterodera schachtii. Juvenile nematodes invade the roots and induce the development of a syncytium, which functions as a feeding site for the nematode. Here, we report on the transcriptome of syncytia induced in the roots of Arabidopsis. Microaspiration was employed to harvest pure syncytium material, which was then used to prepare RNA for hybridization to Affymetrix GeneChips. Initial data analysis showed that the gene expression in syncytia at 5 and 15 days post-infection did not differ greatly, and so both time points were compared together with control roots. Out of a total of 21 138 genes, 18.4% (3893) had a higher expression level and 15.8% (3338) had a lower expression level in syncytia, as compared with control roots, using a multipletesting corrected false discovery rate of below 5%. A gene ontology (GO) analysis of up-and downregulated genes showed that categories related to high metabolic activity were preferentially upregulated. A principal component analysis was applied to compare the transcriptome of syncytia with the transcriptome of different Arabidopsis organs (obtained by the AtGenExpress project), and with specific root tissues. This analysis revealed that syncytia are transcriptionally clearly different from roots (and all other organs), as well as from other root tissues.