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Context-aware transcript quantification from long-read RNA-seq data with Bambu

Nature Methods volume 20, pages 1187–1195 (2023)Cite this article

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Abstract

Most approaches to transcript quantification rely on fixed reference annotations; however, the transcriptome is dynamic and depending on the context, such static annotations contain inactive isoforms for some genes, whereas they are incomplete for others. Here we present Bambu, a method that performs machine-learning-based transcript discovery to enable quantification specific to the context of interest using long-read RNA-sequencing. To identify novel transcripts, Bambu estimates the novel discovery rate, which replaces arbitrary per-sample thresholds with a single, interpretable, precision-calibrated parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve quantification for both novel and known transcripts. We apply Bambu to quantify isoforms from repetitive HERVH-LTR7 retrotransposons in human embryonic stem cells, demonstrating the ability for context-specific transcript expression analysis.

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Fig. 1: Bambu enables simultaneous transcript discovery and quantification from Nanopore RNA-seq data.
Fig. 2: A calibrated machine-learning full-length transcript classifier improves transcript discovery accuracy.
Fig. 3: Transcript quantification on spike-in data shows improvement with varying novel discovery rates.
Fig. 4: Full-length and unique read support provide evidence on expressed transcripts.
Fig. 5: Bambu enables the discovery and quantification of highly repetitive genes.

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Data availability

The SG-NEx samples are available through GitHub (https://github.com/GoekeLab/sg-nex-data), ENA (PRJEB44348) and AWS open data (https://registry.opendata.aws/sgnex/). Processed data associated with figures and tables are available on code ocean55. The PacBio data are available through SRA (SRP036136). The Arabidopsis data are available through ENA (PRJEB32782).

Code availability

Bambu is a R package for transcript discovery and quantification across multiple samples that is maintained on Bioconductor at https://www.bioconductor.org/packages/bambu/. The source code and a detailed documentation are available on GitHub at https://github.com/GoekeLab/bambu/. We used the BambuManuscriptRevision branch version of Bambu for analysis performed in this manuscript (https://github.com/GoekeLab/bambu/tree/BambuManuscriptRevision). All analysis code is available on Code Ocean55.

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Acknowledgements

This work was supported by funding from the Agency for Science, Technology and Research (A*STAR) and the National Medical Research Council. M.L. was supported by R01 HG009937.

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  1. These authors contributed equally: Ying Chen, Andre Sim.

Authors and Affiliations

  1. Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore

    Ying Chen, Andre Sim, Yuk Kei Wan, Keith Yeo, Joseph Jing Xian Lee, Min Hao Ling & Jonathan Göke

  2. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore

    Yuk Kei Wan

  3. Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA

    Michael I. Love

  4. Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA

    Michael I. Love

  5. Department of Statistics and Data Science, National University of Singapore, Singapore, Republic of Singapore

    Jonathan Göke

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Contributions

Y.C. and A.S. designed and implemented the computational method. J.G. conceived the project. Y.C., A.S. and J.G. designed the study and experiments and analyzed data. Y.K.W., K.Y., J.J.X.L. and M.H.L. contributed to the implementation of the computational method. M.L. contributed to the design of the computation method. Y.C., A.S. and J.G. organized and wrote the paper with contributions from all authors.

Corresponding author

Correspondence to Jonathan Göke.

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Competing interests

J.G. received travel and accommodation expenses to speak at the Oxford Nanopore Community Meeting 2018. All other authors declare no competing interests.

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Nature Methods thanks Chung Chau HON and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling editors: Lei Tang and Lin Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Supplementary Figs. 1–8, Supplementary Text, Supplementary Text Figs. 1–5, Supplementary Text Tables 1–7 and Supplementary Notes.

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Supplementary Table

Description of samples used for data analysis and annotation information on novel retrotransposon-derived transcripts based on Bambu.

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Chen, Y., Sim, A., Wan, Y.K. et al. Context-aware transcript quantification from long-read RNA-seq data with Bambu. Nat Methods 20, 1187–1195 (2023). https://doi.org/10.1038/s41592-023-01908-w

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