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.

  • Brief Communication
  • Published:

Streaming fragment assignment for real-time analysis of sequencing experiments

This article has been updated


We present eXpress, a software package for efficient probabilistic assignment of ambiguously mapping sequenced fragments. eXpress uses a streaming algorithm with linear run time and constant memory use. It can determine abundances of sequenced molecules in real time and can be applied to ChIP-seq, metagenomics and other large-scale sequencing data. We demonstrate its use on RNA-seq data and show that eXpress achieves greater efficiency than other quantification methods.

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

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Overview of eXpress.
Figure 2: Accuracy and efficiency of eXpress.
Figure 3: Example of abundance estimation by eXpress, RSEM and Cufflinks at different depths of simulated data for the three-isoform human gene UGT3A2 (ρt, relative abundance for target t).

Similar content being viewed by others

Accession codes


Sequence Read Archive

Change history

  • 04 December 2012

    In the HTML version of this article initially published online, errors in mathematical terms were present in the Online Methods section. The errors have been corrected in the HTML version.


  1. Lipman, D., Flicek, P., Salzberg, S., Gerstein, M. & Knight, R. Genome Biol. 12, 402 (2011).

    Article  Google Scholar 

  2. Wold, B. & Myers, R.M. Nat. Methods 5, 19–21 (2008).

    Article  CAS  Google Scholar 

  3. Hashimoto, T. et al. Bioinformatics 25, 2613–2614 (2009).

    Article  CAS  Google Scholar 

  4. Li, B. & Dewey, C.N. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  6. Chung, D. et al. PLOS Comput. Biol. 7, e1002111 (2011).

    Article  CAS  Google Scholar 

  7. Meinicke, P., Aßhauer, K.P. & Lingner, T. Bioinformatics 27, 1618–1624 (2011).

    Article  CAS  Google Scholar 

  8. Taub, M., Lipson, D. & Speed, T.P. Commun. Inf. Syst. 10, 69–82 (2010).

    Google Scholar 

  9. Cappé, O. & Moulines, E. J. R. Stat. Soc. Series B Stat. Methodol. 71, 593–613 (2009).

    Article  Google Scholar 

  10. Liang, P. & Klein, D. in Proc. Hum. Lang. Technol. Conf. North Am. Ch. Assoc. Comput. Linguist. 611–619 (ACL, 2009).

  11. Hansen, K.D., Brenner, S.E. & Dudoit, S. Nucleic Acids Res. 38, e131 (2010).

    Article  Google Scholar 

  12. Roberts, A. et al. Genome Biol. 12, R22 (2011).

    Article  CAS  Google Scholar 

  13. Lee, S. et al. Nucleic Acids Res. 39, e9 (2011).

    Article  Google Scholar 

  14. MAQC Consortium. et al. Nat. Biotechnol. 24, 1151–1161 (2006).

  15. Anders, S. & Huber, W. Genome Biol. 11, R106 (2010).

    Article  CAS  Google Scholar 

  16. Trapnell, C. et al. Nat. Biotechnol. (in the press).

  17. Branton, D. et al. Nat. Biotechnol. 26, 1146–1153 (2008).

    Article  CAS  Google Scholar 

  18. Stein, L.D. Genome Biol. 11, 207 (2010).

    Article  Google Scholar 

  19. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  20. Ahmadi, A. et al. Nucleic Acids Res. 40, e41 (2011).

    Article  Google Scholar 

Download references


This work was supported by US National Institutes of Health grant R01HG006129. A.R. was supported in part by a National Science Foundation graduate research fellowship. We thank H. Pimentel for developing Map2GTF for converting genome mappings to transcriptome mappings and incorporating it into TopHat to help with our analysis.

Author information

Authors and Affiliations



A.R. and L.P. developed the mathematics and statistics and designed the algorithms. A.R. implemented the method in eXpress. A.R. and L.P. tested the software and performed the analysis. A.R. and L.P. wrote the manuscript.

Corresponding author

Correspondence to Lior Pachter.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11 and Supplementary Tables 1 and 2 (PDF 3034 kb)

Supplementary Software

eXpress source code and compiled binary files. (ZIP 3800 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Roberts, A., Pachter, L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat Methods 10, 71–73 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

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