Comparison of high-throughput sequencing data compression tools

Nature Methods volume 13, pages1005–1008(2016)Cite this article

Subjects

Abstract

High-throughput sequencing (HTS) data are commonly stored as raw sequencing reads in FASTQ format or as reads mapped to a reference, in SAM format, both with large memory footprints. Worldwide growth of HTS data has prompted the development of compression methods that aim to significantly reduce HTS data size. Here we report on a benchmarking study of available compression methods on a comprehensive set of HTS data using an automated framework.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

Rent or Buy

All prices are NET prices.

References

  1. 1

    Giancarlo, R., Rombo, S.E. & Utro, F. Brief. Bioinform. 15, 390–406 (2014).

    CAS  Article  PubMed  Google Scholar 

  2. 2

    Holland, R.C. & Lynch, N. GigaScience 2, 5 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3

    Deorowicz, S. & Grabowski, S. Algorithms Mol. Biol. 8, 25 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4

    Roguski, L. & Deorowicz, S. Bioinformatics 30, 2213–2215 (2014).

    CAS  Article  PubMed  Google Scholar 

  5. 5

    Dutta, A., Haque, M.M., Bose, T., Reddy, C.V. & Mande, S.S. J Bioinform. Comput. Biol. 13, 1541003 (2015).

    CAS  Article  PubMed  Google Scholar 

  6. 6

    Bonfield, J.K. & Mahoney, M.V. PLoS One 8, e59190 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7

    Nicolae, M., Pathak, S. & Rajasekaran, S. Bioinformatics 31, 3276–3281 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8

    Hach, F., Numanagić, I., Alkan, C. & Sahinalp, S.C. Bioinformatics 28, 3051–3057 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9

    Grabowski, S., Deorowicz, S. & Roguski, L. Bioinformatics 31, 1389–1395 (2015).

    CAS  Article  PubMed  Google Scholar 

  10. 10

    Patro, R. & Kingsford, C. Bioinformatics 31, 2770–2777 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11

    Cox, A.J., Bauer, M.J., Jakobi, T. & Rosone, G. Bioinformatics 1415–1419 (2012).

  12. 12

    Zhang, Y. et al. BMC Bioinformatics 16, 188 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13

    Jones, D.C., Ruzzo, W.L., Peng, X. & Katze, M.G. Nucleic Acids Res. 40, e171 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14

    Benoit, G. et al. BMC Bioinformatics 16, 288 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15

    Kingsford, C. & Patro, R. Bioinformatics 31, 1920–1928 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16

    Zhang, Y., Patel, K., Endrawis, T., Bowers, A. & Sun, Y. Gene 579, 75–81 (2016).

    CAS  Article  PubMed  Google Scholar 

  17. 17

    Li, H. et al. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18

    Tarasov, A., Vilella, A.J., Cuppen, E., Nijman, I.J. & Prins, P. Bioinformatics 31, 2032–2034 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19

    Hsi-Yang Fritz, M., Leinonen, R., Cochrane, G. & Birney, E. Genome Res. 21, 734–740 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20

    Bonfield, J.K. Bioinformatics 30, 2818–2819 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21

    Hach, F., Numanagić, I. & Sahinalp, S.C. Nat. Methods 11, 1082–1084 (2014).

    CAS  Article  PubMed  Google Scholar 

  22. 22

    Ochoa, I., Hernaez, M. & Weissman, T. J. Bioinform. Comput. Biol. 12, 1442002 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23

    Voges, J., Munderloh, M. & Ostermann, J. Predictive coding of aligned next-generation sequencing data. In Proc. 2016 Data Compression Conference 241–250 (IEEE, 2016).

Download references

Acknowledgements

This research was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Frontiers program 'Cancer Genome Collaboratory' project (S.C.S., F.H., I.N.); the Vanier Canada Graduate Scholarships program (I.N.); National Institutes of Health (NIH) (R01GM108348 to S.C.S.); National Science Foundation (NSF) (1619081 to S.C.S.); Indiana University Grant Challenges Program Precision Health Initiative (S.C.S.); Wellcome Trust (098051 to J.K.B.); Leibniz Universität Hannover eNIFE grant (J.V. and J.O.); Swiss Platform for Advanced Scientific Computing (PASC) PoSeNoGap project (C.A. and M.M.). We would also like to thank the authors of evaluated compression tools for providing support for their tools and replying to our bug reports.

Author information

Author notes

  1. Ibrahim Numanagić and James K Bonfield: These authors contributed equally to this work.

Affiliations

  1. School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada

    Ibrahim Numanagić, Faraz Hach & S Cenk Sahinalp

  2. Wellcome Trust Sanger Institute, Hinxton, UK

    James K Bonfield

  3. Vancouver Prostate Centre, Vancouver, British Columbia, Canada

    Faraz Hach & S Cenk Sahinalp

  4. Institut für Informationsverarbeitung, Leibniz Universität, Hannover, Germany

    Jan Voges & Jörn Ostermann

  5. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Claudio Alberti & Marco Mattavelli

  6. School of Informatics and Computing, Indiana University, Bloomington, Indiana, USA

    S Cenk Sahinalp

Authors
  1. Ibrahim Numanagić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James K Bonfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Faraz Hach
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jan Voges
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jörn Ostermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Claudio Alberti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marco Mattavelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. S Cenk Sahinalp
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The study was initiated by I.N., C.A. and M.M. I.N. designed the benchmarking framework and performed the experiments. J.K.B. evaluated the framework. I.N., J.K.B., J.V., J.O., F.H., C.A., M.M. and S.C.S. contributed to writing the manuscript. S.C.S. and F.H. oversaw the project.

Corresponding author

Correspondence to S Cenk Sahinalp.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1, Supplementary Tables 1–7 and Supplementary Notes 1–6. (PDF 2002 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Numanagić, I., Bonfield, J., Hach, F. et al. Comparison of high-throughput sequencing data compression tools. Nat Methods 13, 1005–1008 (2016). https://doi.org/10.1038/nmeth.4037

Download citation

Further reading

Search

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