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Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

Nature Biotechnology volume 33, pages 722–729 (2015)Cite this article

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An Erratum to this article was published on 05 February 2016

This article has been updated

Abstract

RNA-seq experiments generate reads derived not only from mature RNA transcripts but also from pre-mRNA. Here we present a computational approach called exon-intron split analysis (EISA) that measures changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. We apply EISA to 17 diverse data sets to show that most intronic reads arise from nuclear RNA and changes in intronic read counts accurately predict changes in transcriptional activity. Furthermore, changes in post-transcriptional regulation can be predicted from differences between exonic and intronic changes. EISA reveals both transcriptional and post-transcriptional contributions to expression changes, increasing the amount of information that can be gained from RNA-seq data sets.

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Figure 1: Comparison of exonic and intronic changes for single genes under controlled transcriptional and post-transcriptional perturbations.
Figure 2: Intronic changes reflect changes in transcriptional activity.
Figure 3: EISA recovers dominant role of transcriptional changes in neuronal differentiation.
Figure 4: EISA recovers post-transcriptional mechanism of miRNAs.
Figure 5: Quantification of transcriptional and post-transcriptional changes.
Figure 6: Read depth versus the number of quantifiable genes.

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Change history

  • 08 July 2015

    In the version of this article initially published online, several errors appeared in the HTML version. In the section “EISA recovers the role of transcription during neurogenesis,” the expression “(t1/2 = 1/α)” should have read “(t1/2 ~ 1/α)” in the sentence “mRNA half-life, on the other hand, is inversely proportional to the degradation rate (t1/2 = 1/α).” In the Online Methods, “Analysis of circadian dynamics data sets,” the symbol “<-” was given as “≤” in two cases and as “≤<” in one case; the formulas “coeffs ≤ lm(y ~ cbind(cos(w*t),-sin(w*t)))$coefficients”; “C ≤< sqrt(coeffs[2]^2 + coeffs[3]^2)”; “ϕ ≤ atan2(coeffs[3],coeffs[2])” should have been “coeffs <- lm(y ~ cbind(cos(w*t),-sin(w*t)))$coefficients”; “C <- sqrt(coeffs[2]^2 + coeffs[3]^2)”; “ϕ <- atan2(coeffs[3],coeffs[2]).” In addition, the corresponding authors are Dimos Gaidatzis, Lukas Burger and Michael Stadler, rather than Dimos Gaidatzis, Lukas Burger and Maria Florescu. The errors have been corrected in HTML version of this article.

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Acknowledgements

We thank D. Schübeler, A. Krebs, R. Ivanek and T.C. Roloff for feedback on the manuscript. We gratefully acknowledge funding from the Novartis Research Foundation.

Author information

Author notes

  1. Dimos Gaidatzis, Lukas Burger and Michael B Stadler: These authors contributed equally to this work.

Authors and Affiliations

  1. Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

    Dimos Gaidatzis, Lukas Burger, Maria Florescu & Michael B Stadler

  2. Swiss Institute of Bioinformatics, Basel, Switzerland

    Dimos Gaidatzis, Lukas Burger, Maria Florescu & Michael B Stadler

  3. University of Basel, Basel, Switzerland

    Maria Florescu

Authors
  1. Dimos Gaidatzis
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  2. Lukas Burger
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  3. Maria Florescu
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  4. Michael B Stadler
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Contributions

D.G., L.B. and M.B.S. designed the study; D.G., L.B., M.F. and M.B.S. analyzed the data. L.B., D.G. and M.B.S. wrote the manuscript.

Corresponding authors

Correspondence to Dimos Gaidatzis, Lukas Burger or Michael B Stadler.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Cummulative distributions for Δintron and Δexon separately for miR-targets and non-targets for the experiments in Figure 4ab (a) and in Figure 4c (b).

We tested statistically if the shift between miR-targets and non-targets is different in Δexon compared to Δintron by calculating the P-value for the interaction term in a two-way anova on the observed log2 fold changes given the miRNA target status (no site, site) as well as the region type (exon, intron). The resulting P-values are depicted in each panel.

Supplementary information

Supplementary Text and Figures

Integrated Supplementary Figure Template with Supplementary Figure 1 (PDF 119 kb)

Supplementary pdf

Supplementary Table 1: GEO identifiers and literature references of datasets used in this study. (XLS 30 kb)

Supplementary Scripts 1 and 2

Supplementary Scripts 1 and 2 (Supplementary_Scripts.tar) (TAR 10 kb)

Supplementary Data 1

Supplementary Data 1: Lists of non-overlapping genes (hg18 and mm9 assemblies, stranded and non-stranded protocols) used in this study (0.2MB). (TAR 180 kb)

Supplementary Data 2

Supplementary Data 2: Data for Figure 2 (7.7MB) (TAR 7870 kb)

Supplementary Data 3

Supplementary Data 3: Data for Figure 3 (1.0MB) (TAR 1010 kb)

Supplementary Data 4

Supplementary Data 4: Data for Figure 4 (4.8MB) (TAR 4820 kb)

Supplementary Data 5

Supplementary Data 5: Data for Figure 5 (7.0MB) (TAR 7070 kb)

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Gaidatzis, D., Burger, L., Florescu, M. et al. Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation. Nat Biotechnol 33, 722–729 (2015). https://doi.org/10.1038/nbt.3269

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