Letter | Published:

Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

Nature Biotechnology volume 28, pages 511515 (2010) | Download Citation

Abstract

High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation1,2,3. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.

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Acknowledgements

This work was supported in part by the US National Institutes of Health (NIH) grants R01-LM006845 and ENCODE U54-HG004576, as well as the Beckman Foundation, the Bren Foundation, the Moore Foundation (Cell Center Program) and the Miller Research Institute. We thank I. Antosechken and L. Schaeffer of the Caltech Jacobs Genome Center for DNA sequencing, and D. Trout, B. King and H. Amrhein for data pipeline and database design, operation and display. We are grateful to R. K. Bradley, K. Datchev, I. Hallgrímsdóttir, J. Landolin, B. Langmead, A. Roberts, M. Schatz and D. Sturgill for helpful discussions.

Author information

Affiliations

  1. Department of Computer Science, University of Maryland, College Park, Maryland, USA.

    • Cole Trapnell
    •  & Steven L Salzberg
  2. Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.

    • Cole Trapnell
    • , Geo Pertea
    •  & Steven L Salzberg
  3. Department of Mathematics, University of California, Berkeley, California, USA.

    • Cole Trapnell
    •  & Lior Pachter
  4. Division of Biology and Beckman Institute, California Institute of Technology, Pasadena, California, USA.

    • Brian A Williams
    • , Ali Mortazavi
    • , Gordon Kwan
    •  & Barbara J Wold
  5. Genome Sciences Center, Washington University in St. Louis, St. Louis, Missouri, USA.

    • Marijke J van Baren
  6. Department of Molecular and Cell Biology, University of California, Berkeley, California, USA.

    • Lior Pachter
  7. Department of Computer Science, University of California, Berkeley, California, USA.

    • Lior Pachter

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Contributions

C.T. and L.P. developed the mathematics and statistics and designed the algorithms; B.A.W. and G.K. performed the RNA-Seq and B.A.W. designed and executed experimental validations; C.T. implemented Cufflinks and Cuffdiff; G.P. implemented Cuffcompare; M.J.v.B. and A.M. tested the software; C.T., G.P. and A.M. performed the analysis; L.P., A.M. and B.J.W. conceived the project; C.T., L.P., A.M., B. J.W. and S.L.S. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Lior Pachter.

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    Supplementary Text and Figures

    Supplementary Tables 1–3, Supplementary Figs. 1–11 and Supplementary Methods

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    Genes with complex isoform expression dynamics in C2C12 myogenesis

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DOI

https://doi.org/10.1038/nbt.1621