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Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing

Abstract

We developed a targeted RNA sequencing method that couples microfluidics-based multiplex PCR and deep sequencing (mmPCR-seq) to uniformly and simultaneously amplify up to 960 loci in 48 samples independently of their gene expression levels and to accurately and cost-effectively measure allelic ratios even for low-quantity or low-quality RNA samples. We applied mmPCR-seq to RNA editing and allele-specific expression studies. mmPCR-seq complements RNA-seq for studying allelic variations in the transcriptome.

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Figure 1: The development and performance of mmPCR-seq.
Figure 2: Characterization of novel RNA-editing sites identified by mmPCR-seq.
Figure 3: ASE analysis of mmPCR-seq data.

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References

  1. Nishikura, K. Annu. Rev. Biochem. 79, 321–349 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wahlstedt, H., Daniel, C., Ensterö, M. & Öhman, M. Genome Res. 19, 978–986 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Li, J.B. et al. Science 324, 1210–1213 (2009).

    Article  CAS  PubMed  Google Scholar 

  4. Maas, S., Kawahara, Y., Tamburro, K.M. & Nishikura, K. RNA Biol. 3, 1–9 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Pastinen, T. & Hudson, T.J. Science 306, 647–650 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Montgomery, S.B. et al. Nature 464, 773–777 (2010).

    Article  CAS  PubMed  Google Scholar 

  7. Pickrell, J.K. et al. Nature 464, 768–772 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ramaswami, G. et al. Nat. Methods 9, 579–581 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ramaswami, G. et al. Nat. Methods 10, 128–132 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ng, S.B. et al. Nature 461, 272–276 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Levin, J.Z. et al. Genome Biol. 10, R115 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mercer, T.R. et al. Nat. Biotechnol. 30, 99–104 (2012).

    Article  CAS  Google Scholar 

  13. Zhang, K. et al. Nat. Methods 6, 613–618 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Eran, A. et al. Mol. Psychiatry 18, 1041–1048 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Main, B.J. et al. BMC Genomics 10, 422 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zhang, K. et al. Nat. Genet. 38, 382–387 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. Andreson, R., Möls, T. & Remm, M. Nucleic Acids Res. 36, e66 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Polson, A.G. & Bass, B.L. EMBO J. 13, 5701–5711 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ensterö, M., Daniel, C., Wahlstedt, H., Major, F. & Öhman, M. Nucleic Acids Res. 37, 6916–6926 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gommans, W.M., Mullen, S.P. & Maas, S. Bioessays 31, 1137–1145 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sun, W. Biometrics 68, 1–11 (2012).

    Article  PubMed  Google Scholar 

  22. Hindorff, L.A. et al. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Montgomery, S.B., Lappalainen, T., Gutierrez-Arcelus, M. & Dermitzakis, E.T. PLoS Genet. 7, e1002144 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lappalainen, T., Montgomery, S.B., Nica, A.C. & Dermitzakis, E.T. Am. J. Hum. Genet. 89, 459–463 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. MacArthur, D.G. et al. Science 335, 823–828 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Li, H. & Durbin, R. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Trapnell, C., Pachter, L. & Salzberg, S.L. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Trapnell, C. et al. Nat. Biotechnol. 28, 511–515 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Li, X., Yin, X. & Li, J. Bioinformatics 26, i191–i198 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank M. Snyder for access to the Fluidigm Access Array system and W. Sun for advice on TReCASE analysis. R.Z. was partially supported by a Dean's fellowship from Stanford University School of Medicine. G.R. was supported by a Stanford Graduate Fellowship. This work was supported by the US National Institutes of Health (GM102484); Ellison Medical Foundation and United States–Israel Binational Science Foundation (to J.B.L.); and Edward Mallinckrodt, Jr. Foundation (to S.B.M.).

Author information

Authors and Affiliations

Authors

Contributions

R.Z. developed and optimized the mmPCR-seq method with the help from G.R., K.S.S., S.B.M. and J.B.L. R.Z. and X.L. performed computational analyses with help from S.B.M. and J.B.L. G.T. provided the brain samples. R.Z., X.L., S.B.M. and J.B.L. wrote the paper.

Corresponding authors

Correspondence to Stephen B Montgomery or Jin Billy Li.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–17, Supplementary Tables 1–9 and Supplementary Notes 1–6 (PDF 2193 kb)

Supplementary Data 1

Targeted RNA editing sites and ASE sites (XLSX 58 kb)

Supplementary Data 2

Primer information (XLSX 81 kb)

Supplementary Data 3

Validation of A-to-I events (XLSX 51 kb)

Supplementary Data 4

Known and novel nonrepetitive recoding sites (XLSX 19 kb)

Supplementary Software

Perl script for multiplex PCR primer design (ZIP 3389 kb)

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Zhang, R., Li, X., Ramaswami, G. et al. Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing. Nat Methods 11, 51–54 (2014). https://doi.org/10.1038/nmeth.2736

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