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Salmon provides fast and bias-aware quantification of transcript expression

Nature Methods volume 14, pages 417–419 (2017)Cite this article

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Abstract

We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA–seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. It is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which, as we demonstrate here, substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.

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Figure 1: Performance of Salmon.

References

  1. Hoadley, K.A. et al. Cell 158, 929–944 (2014).

    Article  CAS  Google Scholar 

  2. Li, J.J., Huang, H., Bickel, P.J. & Brenner, S.E. Genome Res. 24, 1086–1101 (2014).

    Article  CAS  Google Scholar 

  3. Weinstein, J.N. et al. Nat. Genet. 45, 1113–1120 (2013).

    Article  Google Scholar 

  4. Roberts, A., Trapnell, C., Donaghey, J., Rinn, J.L. & Pachter, L. Genome Biol. 12, R22 (2011).

    Article  CAS  Google Scholar 

  5. Love, M.I., Hogenesch, J.B. & Irizarry, R.A. Nat. Biotechnol. 34, 1287–1291 (2016).

    Article  CAS  Google Scholar 

  6. Morán, I. et al. Cell Metab. 16, 435–448 (2012).

    Article  Google Scholar 

  7. Teng, M. et al. Genome Biol. 17, 74 (2016).

    Article  Google Scholar 

  8. Kodama, Y., Shumway, M. & Leinonen, R. Nucleic Acids Res. 40, D54–D56 (2012).

    Article  CAS  Google Scholar 

  9. Patro, R., Mount, S.M. & Kingsford, C. Nat. Biotechnol. 32, 462–464 (2014).

    Article  CAS  Google Scholar 

  10. Bray, N.L., Pimentel, H., Melsted, P. & Pachter, L. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  Google Scholar 

  11. Lappalainen, T. et al. Nature 501, 506–511 (2013).

    Article  CAS  Google Scholar 

  12. SEQC/MAQ-III Consortium. Nat. Biotechnol. 32, 903–914 (2014).

  13. Frazee, A.C., Jaffe, A.E., Langmead, B. & Leek, J.T. Bioinformatics 31, 2778–2784 (2015).

    Article  CAS  Google Scholar 

  14. Li, B., Ruotti, V., Stewart, R.M., Thomson, J.A. & Dewey, C.N. Bioinformatics 26, 493–500 (2010).

    Article  Google Scholar 

  15. Roberts, A. & Pachter, L. Nat. Methods 10, 71–73 (2013).

    Article  CAS  Google Scholar 

  16. Langmead, B. & Salzberg, S.L. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

  17. Srivastava, A., Sarkar, H., Gupta, N. & Patro, R. Bioinformatics 32, i192–i200 (2016).

    Article  CAS  Google Scholar 

  18. t'Hoen, P.A. et al. Nat. Biotechnol. 31, 1015–1022 (2013).

    Article  CAS  Google Scholar 

  19. Foulds, J., Boyles, L., DuBois, C., Smyth, P. & Welling, M. in Proc. 19th ACM SIGKDD Int. Conf. Knowledge Discov. & Data Mining 446–454 (ACM, 2013).

  20. Bishop, C.M. et al. Pattern Recognition and Machine Learning (Springer, 2006).

  21. Hensman, J., Papastamoulis, P., Glaus, P., Honkela, A. & Rattray, M. Bioinformatics 31, 3881–3889 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Nariai, N. et al. BMC Genomics 15 (Suppl. 10), S5 (2014).

    Article  Google Scholar 

  23. Cappé, O. in Mixtures: Estimation and Applications (eds. Mengersen, K.L., Robert, C.P. & Titterington, D.M.) Ch. 2 (John Wiley & Sons, 2011).

  24. Hsieh, C.-J., Yu, H.-F. & Dhillon, I.S. ICML 15, 2370–2379 (2015).

    Google Scholar 

  25. Salzman, J., Jiang, H. & Wong, W.H. Stat. Sci. 26, 1 (2011).

    Article  Google Scholar 

  26. Nicolae, M., Mangul, S., Maă ndoiu, I.I. & Zelikovsky, A. Algorithms Mol. Biol. 6, 9 (2011).

    Article  Google Scholar 

  27. Turro, E. et al. Genome Biol. 12, R13 (2011).

    Article  CAS  Google Scholar 

  28. Li, X., David, G., Andersen, M.K. & Freedman, M.J. in Proc. Ninth Eur. Conf. Computer Syst. 27 (ACM, 2014).

  29. Jackman, S. & Birol, I. F1000Research 5, 1795 (2016).

    Google Scholar 

  30. Merkel, D. Linux J. 2014 (2014).

  31. Di Tommaso, P., Chatzou, M., Baraja, P.P. & Notredame, C. figshare https://dx.doi.org/10.6084/m9.figshare.1254958.v2 (2014).

  32. Brett, K.B.-J. & Greene, C.S. Preprint at https://doi.org/10.1101/056473 (2016).

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Acknowledgements

We wish to thank those who have been using and providing feedback on Salmon since early in its (open) development cycle. The software has been greatly improved in many ways based on their feedback. This research is funded in part by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through Grant GBMF4554 to C.K. It is partially funded by the US National Science Foundation (CCF-1256087, CCF-1319998, BBSRC-NSF/BIO-1564917) and the US National Institutes of Health (R21HG006913, R01HG007104). C.K. received support as an Alfred P. Sloan Research Fellow. This work was partially completed while G.D. was a postdoctoral fellow in the Computational Biology Department at Carnegie Mellon University. M.I.L. was supported by NIH grant 5T32CA009337-35. R.A.I. was supported by NIH R01 grant HG005220.

Author information

Authors and Affiliations

  1. Department of Computer Science, Stony Brook University, Stony Brook, New York, USA

    Rob Patro

  2. DNAnexus, Mountain View, California, USA

    Geet Duggal

  3. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Cambridge, Massachusetts, USA

    Michael I Love & Rafael A Irizarry

  4. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts, USA

    Michael I Love & Rafael A Irizarry

  5. Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Carl Kingsford

Authors
  1. Rob Patro
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  2. Geet Duggal
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  3. Michael I Love
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  4. Rafael A Irizarry
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  5. Carl Kingsford
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Contributions

R.P. and C.K. designed the method, which was implemented by R.P. R.P., G.D., M.I.L., R.I., and C.K. designed the experiments, and R.P., G.D., and M.I.L. conducted the experiments. R.P., G.D., M.I.L., R.A.I., and C.K. wrote the manuscript.

Corresponding authors

Correspondence to Rob Patro or Carl Kingsford.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Overview of Salmon’s method and components and execution timeline.

Salmon accepts either raw (green arrows) or aligned (gray arrow) reads as input. When processing quasi-mappings or aligned reads, Salmon executes an online inference algorithm. This ensures that transcript abundance estimates are available to estimate weights for the rich equivalence classes, and to consider the appropriate conditional probabilities when learning the experimental parameters and foreground bias models. After a fragment’s contributions to the online abundance estimates and bias models have been computed, the fragment is placed into an appropriate equivalence class (or one is created if it does not yet exist). Once all of the fragments have been observed, the initial abundances and fragment equivalence classes are passed to the offline inference module. The offline module learns the background bias models (based on initial abundance estimates) and then corrects the effective transcript lengths to account for the appropriate biases. Finally, the offline inference algorithm (EM or VBEM) is run over the reduced representation of the data until convergence. Once estimation is complete, posterior samples are generated via Gibbs sampling or a bootstrap procedure if the user has requested this.

Supplementary Figure 2 The false discovery rate (FDR) vs. sensitivity of detecting differentially expressed transcripts on Polyester simulated data

The false discovery rate (FDR) vs. the sensitivity of Salmon, Salmon (align), kallisto and eXpress on Polyester simulated RNA-seq data using empirically-derived fragment GC bias profiles. All methods were run with bias-correction enabled, but only Salmon’s model incorporates corrections for fragment GC bias. This leads to a large improvement in sensitivity at almost every FDR value.

Supplementary Figure 3 Abundance vs. fold change accuracy on Polyester simulated data

The log2 fold change between the estimated and true abundances as a function of the true abundance (measured in TPM), for all methods and for all replicates of both simulated “conditions” (each row displays points from all samples within a given condition). The top row corresponds to the 8 samples simulated from the data showing the weak fragment GC content bias, while the bottom row corresponds to the 8 samples simulated from the data showing the stronger fragment GC content bias. Points with an estimated log2 fold change of > 0.5 or < -0.5 are colored red. The fraction of red points appears in the upper right-hand corner of each plot. Salmon consistently demonstrates log fold changes closer to 0 than either kallisto or eXpress, across most of the range of expression.

Supplementary Figure 4 Consistency of estimates on SEQC data within and between centers

The distribution of the mean absolute error of (inverse hyperbolic sine-transformed) TPMs between different replicates of data from the SEQC [12] study. The A sample corresponds to universal human reference tissue (UHRR) and the B sample corresponds to human brain tissue (HBRR). When comparing the replicates that were sequenced at different centers, the inter-replicate distances are larger. However, we observe that Salmon’s bias correction methodology results in improved consistency (i.e., reduced distances) compared to the estimates produced by other methods, especially when comparing replicates sequenced at different centers, where we expect the effects of bias to be more pronounced.

Supplementary Figure 5 Salmon reduces false isoform switching

Transcripts demonstrating dominant isoform switching that results from technical bias. In the quantification estimates computed using kallisto and eXpress, these two-isoform genes show a change in the dominant isoform between conditions (an asterisk denotes a t-test on log2(TPM+1) with p < 1×10−6). However, Salmon directly corrects for technical biases that appear to underlie differences across sequencing center, revealing that the dominant isoform has not, in fact, switched across center.

Supplementary Figure 6 Quantification accuracy for Salmon, Salmon (align), kallisto and eXpress using RSEM-sim data.

The distribution of Spearman correlations over all 20 replicates of the RSEM-sim data for Salmon, kallisto and eXpress. Salmon and kallisto yield very similar distributions of correlations (no statistically significant difference), while both methods yield correlations greater than that of eXpress (Mann-Whitney U test, p = 3.39780 × 10−8).

Supplementary Figure 7 Effect of number of GC models

The effect of the number of conditional GC models used to account for correlation between fragment GC and sequence-specific bias. We choose the default to be 3 bins; the simplest model that demonstrates the majority of the benefit. Panels a, b and c show the result of varying the number of conditional GC models on an analysis of the GEUVADIS data for all genes, all transcripts, and genes with only two transcripts, respectively.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Tables 1–4, Supplementary Notes 1 and 2, and Supplementary Algorithms 1 (PDF 1950 kb)

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Patro, R., Duggal, G., Love, M. et al. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14, 417–419 (2017). https://doi.org/10.1038/nmeth.4197

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