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The dynseq browser track shows context-specific features at nucleotide resolution

Subjects

High-throughput experimental platforms have revolutionized the ability to profile biochemical and functional properties of biological sequences such as DNA, RNA and proteins. By collating several data modalities with customizable tracks rendered using intuitive visualizations, genome browsers enable an interactive and interpretable exploration of diverse types of genome profiling experiments and derived annotations. However, existing genome browser tracks are not well suited for intuitive visualization of high-resolution DNA sequence features such as transcription factor motifs. Typically, motif instances in regulatory DNA sequences are visualized as BED-based annotation tracks, which highlight the genomic coordinates of the motif instances but do not expose their specific sequences. Instead, a genome sequence track needs to be cross-referenced with the BED track to identify sequences of motif hits. Even so, quantitative information about the motif instances such as affinity or conservation as well as differences in base resolution from the consensus motif are not immediately apparent. This makes interpretation slow and challenging. This problem is compounded when analyzing several cellular states and/or molecular readouts (such as ATAC-seq and ChIP–seq) simultaneously, as coordinates of enriched regions (peaks) and the set of active transcription factor motifs vary across cell states.

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Fig. 1: WashU Epigenome Browser session for deciphering sequence architecture of a cis-regulatory element.

Data availability

Data and models used to create vignettes are available at: https://doi.org/10.5281/zenodo.6582100. See Supplementary Table 1 for browser-specific functionalities

Code availability

Code to reproduce vignettes is available at: https://doi.org/10.5281/zenodo.7019993. The dynseq tracks use the BigWig file format. A tutorial is available at: https://kundajelab.github.io/dynseq-pages/. The dynseq track is supported by:

• UCSC Genome Browser (https://genome.ucsc.edu). Documentation is available at: https://genome.ucsc.edu/goldenpath/help/bigWig.html#dynseq. Source code is available at: https://github.com/ucscGenomeBrowser/kent.

• HiGlass/Resgen (https://higlass.io; https://resgen.io). Dynseq is implemented as a plugin. Source code is available at: https://github.com/kundajelab/higlass-dynseq/.

• WashU Epigenome Browser (https://epigenomegateway.wustl.edu). Source code is available at: https://github.com/lidaof/eg-react. Documentation is available at: https://eg.readthedocs.io/en/latest/tracks.html#dynseq.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grant numbers U01HG009431 and U01HG012069 to A.K.; R01HG007175, U01CA200060, U24ES026699, U01HG009391, UM1HG011585, U41HG010972 and U24HG012070 to T.W.; 5U41HG002371 to B.J.R., B.T.L. and M.H.

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Contributions

S.N. and A.K. conceived the project. S.N., A.B., D.L., B.J.R., B.T.L. and P.K. implemented the software. V.R. and A.P. trained machine learning models. S.N., P.K., F.L., T.W., M.H. and A.K. supervised the software development and/or analyses. S.N. drafted the initial manuscript and revised it with feedback from A.K. All authors approved the final manuscript.

Corresponding author

Correspondence to Anshul Kundaje.

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Competing interests

A.K. is scientific co-founder of Ravel Biotechnology, is on the scientific advisory board of PatchBio, SerImmune, AINovo, TensorBio and OpenTargets, is a consultant with Illumina and owns shares in DeepGenomics, Immuni and Freenome. All other authors have no competing interests to declare.

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Nature Genetics thanks Bernardo de Almeida and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Note, Supplementary Figures 1-2, Supplementary Table 1

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Nair, S., Barrett, A., Li, D. et al. The dynseq browser track shows context-specific features at nucleotide resolution. Nat Genet 54, 1581–1583 (2022). https://doi.org/10.1038/s41588-022-01194-w

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