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Mammalian NET-seq analysis defines nascent RNA profiles and associated RNA processing genome-wide


The transcription cycle of RNA polymerase II (Pol II) correlates with changes to the phosphorylation state of its large subunit C-terminal domain (CTD). We recently developed Native Elongation Transcript sequencing using mammalian cells (mNET-seq), which generates single-nucleotide–resolution genome-wide profiles of nascent RNA and co-transcriptional RNA processing that are associated with different CTD phosphorylation states. Here we provide a detailed protocol for mNET-seq. First, Pol II elongation complexes are isolated with specific phospho-CTD antibodies from chromatin solubilized by micrococcal nuclease digestion. Next, RNA derived from within the Pol II complex is size fractionated and Illumina sequenced. Using mNET-seq, we have previously shown that Pol II pauses at both ends of protein-coding genes but with different CTD phosphorylation patterns, and we have also detected phosphorylation at serine 5 (Ser5-P) CTD-specific splicing intermediates and Pol II accumulation over co-transcriptionally spliced exons. With moderate biochemical and bioinformatic skills, mNET-seq can be completed in 6 d, not including sequencing and data analysis.

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Figure 1: Overview of the mNET-seq procedure.
Figure 2: Examples of gel purification steps in the mNET-seq method.
Figure 3: Example of a mNET-seq profile for the SIK1 gene.

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We thank S. Murphy for critically testing this protocol, and A.R. Fialho Grosso for advice on bioinformatics analysis. This work was supported by funding to N.J.P. (Wellcome Trust Programme no. 091805/Z/10/Z and European Research Council (ERC) Advanced grant no. 339270) and to M.C-F. (Fundação Ciência e Tecnologia, Portugal).

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Authors and Affiliations



T.N., M.C.-F. and N.J.P. designed the protocol. All authors wrote the paper. T.N. developed the protocol and performed all experiments. T.G. bioinfomatically analyzed the data.

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Correspondence to Takayuki Nojima or Nicholas J Proudfoot.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 mNET-seq vs GRO-seq

Correlation between log2 (RPM) of Total Pol II mNET-seq and GRO-seq data for genome windows of 500kb. Both samples show very good correlation (Pearson's Correlation Coeficient (PCC) = 0.93, p-value < 2.2e-16). GRO-seq data (GEO accession number GSM1518913) and RNA-seq data used to determine gene expression (GEO accession number GSM1155630) were from previously published studies (Andersson, R. et al. Nat. Commun. 5, 5336 (2014); Lacoste, N. et al. Mol. Cell 53, 631-644 (2014)).

Supplementary Figure 2 mNET-seq Pol II specificity

Boxplot distribution comparison of log2 reads per base (Rpb) values for Total RNA Pol II mNET-seq in expressed protein coding, tRNA and rRNA genes. For protein coding genes, only the region [TSS, TSS+100] is used, to consider a region without reads corresponding to splicing intermediates (which appear at the end of exons), and also to use a region with a size similar to that of tRNA and rRNA genes to avoid any size bias when normalizing.

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Supplementary Figures 1 and 2 (PDF 237 kb)

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Nojima, T., Gomes, T., Carmo-Fonseca, M. et al. Mammalian NET-seq analysis defines nascent RNA profiles and associated RNA processing genome-wide. Nat Protoc 11, 413–428 (2016).

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