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Visual Presentation as a Welcome Alternative to Textual Presentation of Gene Annotation Information

  • Jairav Desai
  • Jared M. Flatow
  • Jie Song
  • Lihua J. Zhu
  • Pan Du
  • Chiang-Ching Huang
  • Hui Lu
  • Simon M. Lin
  • Warren A. KibbeEmail author
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 680)

Abstract

The functions of a gene are traditionally annotated textually using either free text (Gene Reference Into Function or GeneRIF) or controlled vocabularies (e.g., Gene Ontology or Disease Ontology). Inspired by the latest word cloud tools developed by the Information Visualization Group at IBM Research, we have prototyped a visual system for capturing gene annotations, which we named Gene Graph Into Function or GeneGIF. Fully developing the GeneGIF system would be a significant effort. To justify the necessity and to specify the design requirements of GeneGIF, we first surveyed the end-user preferences. From 53 responses, we found that a majority (64%, p < 0.05) of the users were either positive or neutral toward using GeneGIF in their daily work (acceptance); in terms of preference, a slight majority (51%, p > 0.05) of the users favored visual presentation of information (GeneGIF) compared to textual (GeneRIF) information. The results of this study indicate that a visual presentation tool, such as GeneGIF, can complement standard textual presentation of gene annotations. Moreover, the survey participants provided many constructive comments that will specify the development of a phase-two project (http://128.248.174.241/) to visually annotate each gene in the human genome.

Keywords

Gene function Social networking Visualization Word cloud 

Notes

Acknowledgments

The authors would like to thank Martin Wattenberg, Matthew M Mckeon, and Jonathan Feinberg at IBM Research for helpful discussion of Wordle and comments on this manuscript. The authors would also like to thank Rhett Sutphin at NUCATS for exploring the application programing interface to Wordle.

References

  1. 1.
    Ashburner, M., C. A. Ball, et al. (2000). “Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.” Nature Genetics 25(1): 25–9.PubMedCrossRefGoogle Scholar
  2. 2.
    Childers, T. L., M. J. Houston, et al. (1985). “Measurement of individual-differences in visual versus verbal information-processing.” Journal of Consumer Research 12(2): 125–134.CrossRefGoogle Scholar
  3. 3.
    Harris, M. A., J. Clark, et al. (2004). “The Gene Ontology (GO) database and informatics resource.” Nucleic Acids Research 32(Database issue): D258–61.PubMedGoogle Scholar
  4. 4.
    Maglott, D., J. Ostell, et al. (2007). “Entrez Gene: gene-centered information at NCBI.” Nucleic Acids Research 35(Database issue): D26–31.PubMedCrossRefGoogle Scholar
  5. 5.
    Osborne, J. D., J. Flatow, et al. (2009). “Annotating the human genome with disease ontology.” BMC Genomics 10(Suppl 1):S6.PubMedCrossRefGoogle Scholar
  6. 6.
    Osborne, J. D., S. Lin, et al. (2007). “Other riffs on cooperation are already showing how well a wiki could work.” Nature 446(7138): 856.PubMedCrossRefGoogle Scholar
  7. 7.
    Plass, J. L., D. M. Chun, et al. (1998). “Supporting visual and verbal learning preferences in a second-language multimedia learning environment.” Journal of Educational Psychology 90(1): 25–36.CrossRefGoogle Scholar
  8. 8.
    Weiss, S. M. (2005). Text mining : predictive methods for analyzing unstructured information. New York, Springer.Google Scholar
  9. 9.
    Willett, P. (2006). “The Porter stemming algorithm: then and now.” Program-Electronic Library and Information Systems 40(3): 219–223.CrossRefGoogle Scholar
  10. 10.
    Wyer, R. S., Y. W. Jiang, et al. (2008). “Visual and verbal information processing in a consumer context: Further considerations.” Journal of Consumer Psychology 18(4): 276–280.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Jairav Desai
  • Jared M. Flatow
  • Jie Song
  • Lihua J. Zhu
  • Pan Du
  • Chiang-Ching Huang
  • Hui Lu
  • Simon M. Lin
  • Warren A. Kibbe
    • 1
    Email author
  1. 1.The Biomedical Informatics Center and The Robert H. Lurie Comprehensive Cancer CenterNorthwestern University Feinberg School of MedicineChicagoUSA

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