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trackViewer: a Bioconductor package for interactive and integrative visualization of multi-omics data

Nature Methods volume 16, pages 453–454 (2019)Cite this article

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To the Editor — As high-throughput sequencing’s cost continues to decrease and its applications continue to grow, the amount of data and the number of data types produced by this method are expected to constantly expand. To integrate various types of data and generate hypotheses efficiently, researchers increasingly need concise and meaningful depictions of diverse, complex datasets. Several genome browsers and viewers have been developed for the visualization of genomic data1,2,3, but the majority of those tools do not have an easy programming interface that can be plugged into a pipeline. In addition, methylation, mutation, and single-nucleotide polymorphism (SNP) data require a special type of plot, called a lollipop or needle plot, to concisely depict the methylation, mutation, and SNP status. Although several tools have been developed to generate stand-alone lollipop plots (reviewed in ref. 4), all the genome browsers lack this function. In addition, it is difficult to use existing lollipop-plot tools to visualize dense mutation/SNP and methylation data.

Here we describe trackViewer, a Bioconductor package for the visualization of multi-omics data that can be integrated into any analysis pipeline in R. The installation guide, documentation, implementation details, data, and functionalities of trackViewer are provided in Supplementary Notes 1–6. trackViewer can be used not only to visualize coverage and annotation tracks, but also to generate lollipop and dandelion plots that depict sparse and dense methylation/mutation/variant data to facilitate an integrative analysis of diverse datasets5,6. The updated trackViewer (versions 1.19.27 and higher) has a web interface in addition to the R programming interface (Supplementary Video 5, Supplementary Note 7). Furthermore, with the ‘browseTracks’ function, users can generate interactive figures—that is, figures one can easily customize the features of by clicking, dragging, and typing (Supplementary Videos 1–4, Supplementary Note 3).

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Fig. 1: Examples to illustrate how trackViewer can be used for integrative visualization of multi-omics data.

Data availability

All datasets analyzed during this work are publicly available and are described in detail in Supplementary Note 6. In addition, we have created a Docker image that contains all data and scripts necessary for users to reproduce the figures as either static or interactive (Supplementary Note 2).

Code availability

trackViewer is freely available at https://bioconductor.org/packages/release/bioc/html/trackViewer.html. To make it easy to install and run, we have created a Docker image with trackViewer, RStudio, and all its R and system dependencies already installed (https://github.com/jianhong/trackViewer.documentation). In addition, the Docker image contains all data and scripts necessary for users to reproduce the figures as either static or interactive (Supplementary Note 2). Supplementary Note 7 provides step-by-step instructions on how to install and run the web application of trackViewer, with four examples.

References

  1. Robinson, J. T. et al. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  Google Scholar 

  2. Wang, J., Kong, L., Gao, G. & Luo, J. Brief. Bioinform. 14, 131–143 (2013).

    Article  Google Scholar 

  3. Haeussler, M. et al. Nucleic Acids Res. 47, D853–D858 (2019).

    Article  Google Scholar 

  4. Jay, J. J. & Brouwer, C. PLoS One 11, e0160519 (2016).

    Article  Google Scholar 

  5. Ben-David, E., Burga, A. & Kruglyak, L. Science 356, 1051–1055 (2017).

    Article  CAS  Google Scholar 

  6. Huang, D. et al. Proc. Natl Acad. Sci. USA 114, E3149–E3158 (2017).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank S. Han for editorial and web design assistance, and L. Ni and J. Yu for helpful discussion. L.J.Z. is partially supported by the NIH/NIDDK (grant R01DK076118).

Author information

Authors and Affiliations

  1. Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA

    Jianhong Ou

  2. Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA, USA

    Lihua Julie Zhu

  3. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA

    Lihua Julie Zhu

  4. Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA

    Lihua Julie Zhu

Authors
  1. Jianhong Ou
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Contributions

J.O. developed the software and generated Fig. 1 with input from L.J.Z. L.J.Z. drafted the manuscript with input from J.O. Both authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jianhong Ou or Lihua Julie Zhu.

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The authors declare no competing interests.

Supplementary information

Supplementary Information

Supplementary Notes 1–7

Reporting Summary

Supplementary Video 1

Generate and modify the interactive plot in Fig. 1a using trackViewer.

Supplementary Video 2

Generate and modify the interactive plot in Fig. 1b using trackViewer.

Supplementary Video 3

Generate and modify the interactive plot in Fig. 1c using trackViewer.

Supplementary Video 4

Generate and modify the interactive plot in Fig. 1d using trackViewer.

Supplementary Video 5

How to use the web interface of the Bioconductor package trackViewer.

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Ou, J., Zhu, L.J. trackViewer: a Bioconductor package for interactive and integrative visualization of multi-omics data. Nat Methods 16, 453–454 (2019). https://doi.org/10.1038/s41592-019-0430-y

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