Systematic evaluation of RNA-Seq preparation protocol performance.

BACKGROUND: RNA-Seq is currently the most widely used tool to analyze whole-transcriptome profiles. There are numerous commercial kits available to facilitate preparing RNA-Seq libraries; however, it is still not clear how some of these kits perform in terms of: 1) ribosomal RNA removal; 2) read coverage or recovery of exonic vs. intronic sequences; 3) identification of differentially expressed genes (DEGs); and 4) detection of long non-coding RNA (lncRNA). In RNA-Seq analysis, understanding the strengths and limitations of commonly used RNA-Seq library preparation protocols is important, as this technology remains costly and time-consuming. RESULTS: In this study, we present a comprehensive evaluation of four RNA-Seq kits. We used three standard input protocols: Illumina TruSeq Stranded Total RNA and mRNA kits, a modified NuGEN Ovation v2 kit, and the TaKaRa SMARTer Ultra Low RNA Kit v3. Our evaluation of these kits included quality control measures such as overall reproducibility, 5' and 3' end-bias, and the identification of DEGs, lncRNAs, and alternatively spliced transcripts. Overall, we found that the two Illumina kits were most similar in terms of recovering DEGs, and the Illumina, modified NuGEN, and TaKaRa kits allowed identification of a similar set of DEGs. However, we also discovered that the Illumina, NuGEN and TaKaRa kits each enriched for different sets of genes. CONCLUSIONS: At the manufacturers' recommended input RNA levels, all the RNA-Seq library preparation protocols evaluated were suitable for distinguishing between experimental groups, and the TruSeq Stranded mRNA kit was universally applicable to studies focusing on protein-coding gene profiles. The TruSeq protocols tended to capture genes with higher expression and GC content, whereas the modified NuGEN protocol tended to capture longer genes. The SMARTer Ultra Low RNA Kit may be a good choice at the low RNA input level, although it was inferior to the TruSeq mRNA kit at standard input level in terms of rRNA removal, exonic mapping rates and recovered DEGs. Therefore, the choice of RNA-Seq library preparation kit can profoundly affect data outcomes. Consequently, it is a pivotal parameter to consider when designing an RNA-Seq experiment.

DNMT3L facilitates DNA methylation partly by maintaining DNMT3A stability in mouse embryonic stem cells.

DNMT3L (DNMT3-like), a member of the DNMT3 family, has no DNA methyltransferase activity but regulates de novo DNA methylation. While biochemical studies show that DNMT3L is capable of interacting with both DNMT3A and DNMT3B and stimulating their enzymatic activities, genetic evidence suggests that DNMT3L is essential for DNMT3A-mediated de novo methylation in germ cells but is dispensable for de novo methylation during embryogenesis, which is mainly mediated by DNMT3B. How DNMT3L regulates DNA methylation and what determines its functional specificity are not well understood. Here we show that DNMT3L-deficient mouse embryonic stem cells (mESCs) exhibit downregulation of DNMT3A, especially DNMT3A2, the predominant DNMT3A isoform in mESCs. DNA methylation analysis of DNMT3L-deficient mESCs reveals hypomethylation at many DNMT3A target regions. These results confirm that DNMT3L is a positive regulator of DNA methylation, contrary to a previous report that, in mESCs, DNMT3L regulates DNA methylation positively or negatively, depending on genomic regions. Mechanistically, DNMT3L forms a complex with DNMT3A2 and prevents DNMT3A2 from being degraded. Restoring the DNMT3A protein level in DNMT3L-deficient mESCs partially recovers DNA methylation. Thus, our work uncovers a role for DNMT3L in maintaining DNMT3A stability, which contributes to the effect of DNMT3L on DNMT3A-dependent DNA methylation.