Skip to main content

Thank you for visiting nature.com. 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.

  • Resource
  • Published:

Balancing efficient analysis and storage of quantitative genomics data with the D4 format and d4tools

Nature Computational Science volume 1, pages 441–447 (2021)Cite this article

Subjects

An Author Correction to this article was published on 15 February 2022

This article has been updated

A preprint version of the article is available at bioRxiv.

Abstract

Modern DNA sequencing is used as a readout for diverse assays, with the count of aligned sequences (read depth) representing the quantitative signal for each underlying cellular phenomena. Existing data formats for quantitative genomics assays are, however, limited in either the analysis speeds they enable, the disk space they require or both. We have developed the dense depth data dump (D4) format and tool suite, with the goal of balancing improved analysis speeds with file size. The D4 format is adaptive in that it profiles a random sample of aligned sequence depth from the input sequence file to determine an optimal encoding that enables fast data access. We demonstrate that the D4 format offers substantial speed improvements over existing formats for random access, aggregation and summarization, while also achieving better or comparable file sizes. This performance enables scalable downstream analyses that would be otherwise difficult.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Depth distribution for WGS and RNA-seq datasets.
Fig. 2: The D4 format encoding strategy.
Fig. 3: Optimizing the choice of k given the trade-off between the size of the primary and secondary tables.
Fig. 4: Performance of D4 compared with other formats.
Fig. 5: Comparison of file sizes and analysis times across multiple WGS and RNA-seq datasets.

Similar content being viewed by others

Data availability

The 1000 Genomes data were downloaded from http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20190425_NYGC_GATK/. Bulk RNA-seq samples were downloaded from the ENCODE project website (www.encode.org) and a list of URLs to download all samples used is available at the D4utils GitHub repository (https://github.com/38/d4-format). The WGS dataset is sample HG002 from the Genome in a Bottle Project, and the RNA-seq dataset is sample ENCFF976QSN from the ENCODE project16. Source data are provided with this paper.

Code availability

The code underlying the software described and the data analyzed in this study are available at: D4utils, https://github.com/38/d4-format; D4 Rust API, https://docs.rs/d4; D4 Python API, https://github.com/38/pyd4. Code has also been deposited on Zenodo17 at https://zenodo.org/record/4684595#.YJQ272ZKidZ.

Change history

References

  1. Sasani, T. A. et al. Large, three-generation human families reveal post-zygotic mosaicism and variability in germline mutation accumulation. Elife 8, e46922 (2019).

    Article  Google Scholar 

  2. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  Google Scholar 

  3. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

  4. 2 Chromatin patterns at transcription factor binding sites. Nature https://doi.org/10.1038/nature28171 (2019).

  5. Pedersen, B. S., Collins, R. L., Talkowski, M. E. & Quinlan, A. R. Indexcov: fast coverage quality control for whole-genome sequencing. Gigascience 6, 1–6 (2017).

    Article  Google Scholar 

  6. Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. bigWig and bigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).

    Article  Google Scholar 

  7. Frequently asked questions: data file formats. Genome Browser https://genome.ucsc.edu/FAQ/FAQformat.html (2021).

  8. Koranne, S. Handbook of Open Source Tools 191–200 (Springer, 2011); https://doi.org/10.1007/978-1-4419-7719-9_10

  9. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  10. Fritz, M. H.-Y., Leinonen, R., Cochrane, G. & Birney, E. Efficient storage of high throughput DNA sequencing data using reference-based compression. Genome Res. 21, 734–740 (2011).

    Article  Google Scholar 

  11. Shao, Z., Reppy, J. H. & Appel, A. W. Unrolling lists. SIGPLAN Lisp Pointers VII, 185–195 (1994).

    Article  Google Scholar 

  12. Pedersen, B. S. & Quinlan, A. R. mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34, 867–868 (2018).

    Article  Google Scholar 

  13. The SAM/BAM Format Specification Working Group Sequence Alignment/Map Format Specification (GitHub, 2021); http://samtools.github.io/hts-specs/SAMv1.pdf

  14. Wang, Z., Weissman, T. & Milenkovic, O. smallWig: parallel compression of RNA-seq WIG files. Bioinformatics 32, btv561 (2015).

    Article  Google Scholar 

  15. Li, H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 30, 2843–2851 (2014).

    Article  Google Scholar 

  16. ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  17. Hou, H., Quinlan, A. & Pedersen, B. Efficient analysis of quantitative genomics data with the D4 format. Zenodo https://doi.org/10.5281/ZENODO.4684595 (2021).

Download references

Acknowledgements

We acknowledge helpful comments from members of the Quinlan laboratory, as well as funding from the National Institutes of Health (NIH) grants HG006693, HG009141 and GM124355 awarded to A.Q.

Author information

Authors and Affiliations

  1. Department of Human Genetics, University of Utah, Salt Lake City, UT, USA

    Hao Hou, Brent Pedersen & Aaron Quinlan

  2. Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA

    Hao Hou, Brent Pedersen & Aaron Quinlan

  3. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA

    Aaron Quinlan

Authors
  1. Hao Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brent Pedersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aaron Quinlan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.H. conceived of the D4 encoding strategy, proposed the associated algorithms, implemented all software, conducted all analyses and contributed to the manuscript. B.P. described the original problem that led to the D4 format and contributed to the manuscript. A.Q. supervised the project and led the writing of the manuscript.

Corresponding author

Correspondence to Aaron Quinlan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Christopher Lee, Mikel Hernaez, Christoph Lange and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Table 1 and notes.

Source data

Source Data Fig. 1

Depth histogram data for Fig. 1.

Source Data Fig. 3

Primary table sizes as a function of the choice of k for Fig. 3.

Source Data Fig. 4

Times required to create and query files for Fig. 4.

Source Data Fig. 5

Sizes and analysis times from multiple datasets for Fig. 5.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, H., Pedersen, B. & Quinlan, A. Balancing efficient analysis and storage of quantitative genomics data with the D4 format and d4tools. Nat Comput Sci 1, 441–447 (2021). https://doi.org/10.1038/s43588-021-00085-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-021-00085-0

This article is cited by

Search

Advanced search

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