Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics


We present a liquid chromatography–mass spectrometry (LC-MS)-based method permitting unbiased (gene prediction–independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resolution isoelectric focusing (HiRIEF) at the peptide level in the 3.7–5.0 pH range and accurate peptide isoelectric point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, respectively. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: HiRIEF LC-MS enables unbiased proteogenomics in higher eukaryotes.
Figure 2: Analysis of distribution of novel peptides into different categories.
Figure 3: New gene models.


  1. Krug, K., Nahnsen, S. & Macek, B. Mol. Biosyst. 7, 284–291 (2011).

    CAS  Article  Google Scholar 

  2. Kelkar, D.S. et al. Mol. Cell. Proteomics 10, M111.011627 (2011).

    Article  Google Scholar 

  3. Fermin, D. et al. Genome Biol. 7, R35 (2006).

    Article  Google Scholar 

  4. Granholm, V. & Käll, L. Proteomics 11, 1086–1093 (2011).

    CAS  Article  Google Scholar 

  5. Baerenfaller, K. et al. Science 320, 938–941 (2008).

    CAS  Article  Google Scholar 

  6. Brosch, M. et al. Genome Res. 21, 756–767 (2011).

    CAS  Article  Google Scholar 

  7. Evans, V.C. et al. Nat. Methods 9, 1207–1211 (2012).

    CAS  Article  Google Scholar 

  8. Edwards, N.J. Mol. Syst. Biol. 3, 102 (2007).

    Article  Google Scholar 

  9. Bitton, D.A., Smith, D.L., Connolly, Y., Scutt, P.J. & Miller, C.J. PLoS ONE 5, e8949 (2010).

    Article  Google Scholar 

  10. Sevinsky, J.R. et al. J. Proteome Res. 7, 80–88 (2008).

    CAS  Article  Google Scholar 

  11. Tanner, S. et al. Genome Res. 17, 231–239 (2007).

    CAS  Article  Google Scholar 

  12. Beck, M. et al. Mol. Syst. Biol. 7, 549 (2011).

    Article  Google Scholar 

  13. Hörth, P., Miller, C.A., Preckel, T. & Wenz, C. Mol. Cell. Proteomics 5, 1968–1974 (2006).

    Article  Google Scholar 

  14. Lengqvist, J., Uhlen, K. & Lehtio, J. Proteomics 7, 1746–1752 (2007).

    CAS  Article  Google Scholar 

  15. Cargile, B.J., Sevinsky, J.R., Essader, A.S., Stephenson, J.L. Jr. & Bundy, J.L. J. Biomol. Tech. 16, 181–189 (2005).

    PubMed  PubMed Central  Google Scholar 

  16. Ingolia, N.T., Lareau, L.F. & Weissman, J.S. Cell 147, 789–802 (2011).

    CAS  Article  Google Scholar 

  17. Kalyana-Sundaram, S. et al. Cell 149, 1622–1634 (2012).

    CAS  Article  Google Scholar 

  18. Stedman, H.H. et al. Nature 428, 415–418 (2004).

    CAS  Article  Google Scholar 

  19. Djebali, S. et al. Nature 489, 101–108 (2012).

    CAS  Article  Google Scholar 

  20. Lindblad-Toh, K. et al. Nature 478, 476–482 (2011).

    CAS  Article  Google Scholar 

  21. Wiśniewski, J.R., Zougman, A. & Mann, M. J. Proteome Res. 8, 5674–5678 (2009).

    Article  Google Scholar 

  22. Reiter, L. et al. Mol. Cell. Proteomics 8, 2405–2417 (2009).

    CAS  Article  Google Scholar 

  23. Bjellqvist, B. et al. Electrophoresis 14, 1023–1031 (1993).

    CAS  Article  Google Scholar 

  24. Cargile, B.J., Sevinsky, J.R., Essader, A.S., Eu, J.P. & Stephenson, J.L. Electrophoresis 29, 2768–2778 (2008).

    CAS  Article  Google Scholar 

  25. Park, C.Y., Klammer, A.A., Käll, L., MacCoss, M.J. & Noble, W.S. J. Proteome Res. 7, 3022–3027 (2008).

    CAS  Article  Google Scholar 

  26. Käll, L., Canterbury, J.D., Weston, J., Noble, W.S. & MacCoss, M.J. Nat. Methods 4, 923–925 (2007).

    Article  Google Scholar 

  27. Stamatoyannopoulos, J.A. et al. Genome Biol. 13, 418 (2012).

    Article  Google Scholar 

  28. Kent, W.J. et al. Genome Res. 12, 996–1006 (2002).

    CAS  Article  Google Scholar 

  29. Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Mol. Cell. Proteomics 11, M111.014050 (2012).

    Article  Google Scholar 

  30. Akan, P. et al. Genome Med. 4, 86 (2012).

    CAS  Article  Google Scholar 

  31. Stranneheim, H., Werne, B., Sherwood, E. & Lundeberg, J. PLoS ONE 6, e21910 (2011).

    CAS  Article  Google Scholar 

  32. Ramsköld, D., Wang, E.T., Burge, C.B. & Sandberg, R. PLoS Comput. Biol. 5, e1000598 (2009).

    Article  Google Scholar 

Download references


Funding from the Swedish Research Council, Swedish Cancer Society, Stockholm's county council, Stockholm's cancer society and EU FP7 project GlycoHit is gratefully acknowledged. Support by BILS (Bioinformatics Infrastructure for Life Sciences) and J. Boekel in publishing the MS raw files is gratefully acknowledged. We thank the SciLifeLab genomics facility for experimental support and J. Lundeberg for the A431 sequence data. We thank E. Bereczki (Karolinska Institutet) for the kind gift of the N2A cell line. We thank K. Lindblad-Toh for critical reading of the manuscript. We acknowledge the late B. Bjellqvist for his early contribution in the development of IPG-IEF and peptide pI prediction.

Author information

Authors and Affiliations



J.L., R.M.M.B., L.M.O., L.K. and H.J.J. conceived of and designed the experiments. R.M.M.B. and H.J.J. performed the IEF separations and MS analysis. M.H., L.M.O. and R.M.M.B. performed the data analysis of RNA-seq experiments. R.M.M.B. performed the peptide pI calculations. L.K., J.L., R.M.M.B. and V.G. designed the database restriction workflow and performed the 6FT searches. L.K. and V.G. designed the novel-only TDA approach. Å.P.-B. performed the single-nucleotide polymorphism data analysis and calculated Ensembl annotation statistics. R.M.M.B., L.M.O., H.J.J. and J.F. performed proteomics data analysis. R.M.M.B., L.M.O. and J.L. wrote the manuscript. All authors were involved in discussion of the manuscript and approved the final manuscript.

Corresponding author

Correspondence to Janne Lehtiö.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15 (PDF 1952 kb)

Supplementary Table 1

Conventional proteomics performance as measured by number of PSMs, peptides, protein groups and corresponding genes. (XLSX 10 kb)

Supplementary Table 2

Novel peptides identified by proteogenomics in H. sapiens and supporting evidence. (XLSX 103 kb)

Supplementary Table 3

Novel peptides identified by proteogenomics in M. musculus and supporting evidence. (XLSX 35 kb)

Supplementary Table 4

Proteins identified by conventional proteomics in H. sapiens. (XLSX 4938 kb)

Supplementary Table 5

Proteins identified by conventional proteomics in M. musculus. (XLSX 895 kb)

Supplementary Data 1

Annotations of MS2 spectra pertaining to the novel peptides. (ZIP 6962 kb)

Supplementary Data 2

Custom tracks for data visualization in the UCSC genome browser. (ZIP 766 kb)

Supplementary Software

PredpI algorithm (ZIP 2988 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Branca, R., Orre, L., Johansson, H. et al. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nat Methods 11, 59–62 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing