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Comparison of high-throughput sequencing data compression tools

Nature Methods volume 13, pages 1005–1008 (2016)Cite this article

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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.

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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.

Authors and 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
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  2. James K Bonfield
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  3. Faraz Hach
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  4. Jan Voges
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  5. Jörn Ostermann
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  6. Claudio Alberti
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  7. Marco Mattavelli
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  8. S Cenk Sahinalp
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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.

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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)

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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

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