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StringTie enables improved reconstruction of a transcriptome from RNA-seq reads

Nature Biotechnology volume 33, pages 290–295 (2015)Cite this article

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Abstract

Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.

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Figure 1: Transcript assembly pipelines for StringTie, Cufflinks and Traph.
Figure 2: Transcriptome assemblers' accuracies in detecting expressed transcripts from two simulated RNA-seq data sets.
Figure 3: Accuracy of transcript assemblers at assembling known genes, measured on real data sets from four different tissues.

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Sequence Read Archive

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Acknowledgements

These studies were supported in part by US National Institutes of Health grants R01-HG006677 (S.L.S.), R01-HG006102 (S.L.S.), R01-GM105705 (G.M.P.), R01-CA120185 (J.T.M.), P01-CA134292 (J.T.M.), and the Cancer Prevention and Research Institute of Texas (J.T.M.).

Author information

Authors and Affiliations

  1. Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, USA

    Mihaela Pertea, Geo M Pertea, Corina M Antonescu & Steven L Salzberg

  2. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA

    Mihaela Pertea, Geo M Pertea, Corina M Antonescu & Steven L Salzberg

  3. Department of Molecular Biology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Tsung-Cheng Chang & Joshua T Mendell

  4. Center for Regenerative Science and Medicine, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Tsung-Cheng Chang & Joshua T Mendell

  5. Simmons Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Joshua T Mendell

  6. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA

    Steven L Salzberg

  7. Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA

    Steven L Salzberg

Authors
  1. Mihaela Pertea
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  2. Geo M Pertea
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  3. Corina M Antonescu
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  4. Tsung-Cheng Chang
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  5. Joshua T Mendell
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  6. Steven L Salzberg
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Contributions

M.P. designed the StringTie method with input from S.L.S. M.P. and G.M.P. implemented the algorithms. C.M.A. ran all programs on the RNA-seq data and tuned their performance. J.T.M. and T.-C.C. produced the kidney cell line data and gave feedback on StringTie's performance. M.P. and S.L.S. wrote the paper. S.L.S. supervised the entire project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Steven L Salzberg.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1–11 and Supplementary Discussion (PDF 1024 kb)

Supplementary Software 1

StringTie code (ZIP 351 kb)

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Pertea, M., Pertea, G., Antonescu, C. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33, 290–295 (2015). https://doi.org/10.1038/nbt.3122

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