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gEAR: Gene Expression Analysis Resource portal for community-driven, multi-omic data exploration

Nature Methods volume 18, pages 843–844 (2021)Cite this article

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The gEAR portal provides a single platform for data deposition, display, analysis and interrogation, with a focus on allowing users to customize the site and their own data displays. The gEAR portal supports uploaded datasets for a range of organisms, including human, mouse, rat, zebrafish, chicken and marmoset. Multiple datasets can be displayed side-by-side within a display profile, showing not only a user’s own data in a visual form of choice, but also allowing the user to select from hundreds of previously uploaded public datasets to display alongside their own for cross-experimental validation and discovery. Integration of the Epiviz4 tool allows epigenetic modifications to be viewed in the context of gene expression. The portal is also designed to display a gene’s species-specific annotation and links out to information on other systems such as the UCSC Genome Browser5, CCDS6, Ensembl7, OMIM8, and a variety of domain-specific resources. Further features available via drop-down menus include links to the raw data in the Gene Expression Omnibus (GEO)2, the associated publication, a tool to compare gene expression across conditions, a tool to store and export gene lists for downstream analysis of differentially expressed genes, and a single-cell RNA-seq Analysis Workbench.

The gEAR portal’s specialized workflow for single-cell RNA-seq analysis enables a user to upload their dataset and use the Analysis Workbench to perform a stepwise analysis, including quality control, principal component analysis, clustering, dimensionality reduction and marker gene identification. All analyses within the gEAR portal require no coding knowledge and are available through user-friendly interface controls. Users can also upload precomputed analyses, a feature that is particularly useful when hosting annotated and published datasets. Biologists may upload expression or epigenetic data to gEAR (publicly or privately) and perform a rapid initial analysis. Users then curate their datasets to choose how they should be displayed, selecting from options such as bar or line plots, colorized anatomical scalable vector graphics (SVGs), violin plots, or t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) displays. Data displays can be customized to match the displays in publications. Individual datasets or profiles can be shared privately with collaborators initially, then publicly via a short permalink URL as a primary method of data availability within a publication.

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Fig. 1: gEAR overview.

Code availability

The gEAR code is available on GitHub (https://github.com/IGS/gEAR) or Zenodo (https://doi.org/10.5281/zenodo.4818596) and released under the GNU Affero General Public License v3.0.

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Acknowledgements

We are grateful to the members of the Hearing Restoration Project for serving as the focus group in developing this tool and their continuous feedback. We thank R. Marini for technical assistance. This work was funded by the Hearing Restoration Project (R.H.); R01-DC013817 and R01-DC019370 (R.H.); R24- 96MH114815 (R.H. and O.W.); and R01-GM114267 (H.C.B).

Author information

Authors and Affiliations

  1. Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA

    Joshua Orvis, Brian Gottfried, Ricky S. Adkins, Yang Song, Dustin Olley, Brian Herb, Carlo Colantuoni, Robert L. Carter, Seth A. Ament, Owen White, Anup Mahurkar & Ronna Hertzano

  2. Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA

    Jayaram Kancherla & Hector Corrada Bravo

  3. Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA

    Jayaram Kancherla & Hector Corrada Bravo

  4. Department of Computer Science, University of Maryland, College Park, MD, USA

    Jayaram Kancherla & Hector Corrada Bravo

  5. Department of Otolaryngology–Head and Neck Surgery, Galilee Medical Center, Nahariya, Israel

    Amiel A. Dror

  6. Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, Israel

    Amiel A. Dror

  7. Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA

    Kevin Rose, Elena Chrysostomou, Beatrice Milon, Maggie S. Matern & Ronna Hertzano

  8. Laboratory of Cochlear Development, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA

    Michael C. Kelly & Matthew W. Kelley

  9. Molecular Otolaryngology and Renal Research Laboratories, Department of Otolaryngology, University of Iowa, Iowa City, IA, USA

    Hela Azaiez

  10. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Carlo Colantuoni

  11. Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA

    Seth A. Ament

  12. Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA

    Ronna Hertzano

Authors
  1. Joshua Orvis
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  2. Brian Gottfried
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  3. Jayaram Kancherla
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  14. Brian Herb
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  17. Seth A. Ament
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  18. Matthew W. Kelley
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  19. Owen White
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  20. Hector Corrada Bravo
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  21. Anup Mahurkar
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  22. Ronna Hertzano
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Contributions

Portal design: J.O., B.G., R.S.A., A.M. and R.H. Data archive and infrastructure: J.O., B.G., R.S.A., J.K., M.C.K., H.C.B., S.A.A., C.C., B.H., O.W. and A.M. Graphic design: A.A.D. User interface: J.O., B.G., R.S.A., D.O. and A.M. User manual: J.O., Y.S., K.R., E.C. and R.H. DVD and deafness gene/locus integration: H.A., J.O, and R.H. Providing unpublished data: K.R., M.C.K, M.W.K. and R.H. Project management: R.L.C., A.M. and R.H. Data upload and analysis: J.O., D.O., Y.S., E.C., B.M., M.S.M. and R.H. Writing the manuscript: J.O., B.M., A.M. and R.H.

Corresponding author

Correspondence to Ronna Hertzano.

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The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Eran Mukamel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Orvis, J., Gottfried, B., Kancherla, J. et al. gEAR: Gene Expression Analysis Resource portal for community-driven, multi-omic data exploration. Nat Methods 18, 843–844 (2021). https://doi.org/10.1038/s41592-021-01200-9

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