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ePlant helps biologists visualize the natural connections between DNA sequences, natural variation (polymorphisms), molecular structures, protein-protein interactions, and gene expression patterns by combining several data visualization tools with a zoomable user interface.

Multiple levels of data through the same window.

It connects to several publicly available web services to download the latest genome, interactome, and transcriptome data for any number of genes or gene products you may be interested in. Data is displayed with a set of visualization tools that are presented with a conceptual hierarchy from big to small. Links between the different views help underscore connections between multiple levels of analysis.

Developed in Nicholas Provart’s lab in the department of Cell & Systems Biology at the University of Toronto, this version of ePlant is a major update from the original tool by Fucile et al. (2011).  ePlant was funded with grants from Genome Canada and NSERC.

Try It Here!

User Testing

We have completed two rounds of user testing at the International Conference of Arabidopsis Researchers (2014 in Vancouver & 2015 in Paris).

User Testing at ICAR 2015

Participants were instructed to “think out loud” as they performed the following tasks:

  1. Can you load ABI3?
  2. Which tissue is ABI3 most strongly expressed in?
  3. Where is ABI3 localized in the cell?
  4. Can you name an interaction partner for ABI3?
  5. Can you load AT1G16850?
  6. In what part of the world does AT1G16850 show the most natural variation?
  7. Can you find the data source for this view?
  8. Can you load ATAP3?
  9. Can you make a screen capture of the protein interactions view of ATAP3?
  10. What is the annotation for ATAP3?

ePlant user test

T-tests were run for all questions and differences between the groups were found to be not significant. This was expected, however, since we were not trying to improve the mean performance time across the years, but rather to confirm if users are able to perform most tasks within twenty seconds or less. I am happy to report that in most cases they are.

The tasks people had the most difficulty with were “Can you identify the data source for this view?” and “What is the annotation for ATAP3?”. In both cases participants reported that they found it difficult to find the buttons that link to this information, however once they found them they did not feel they should be moved anywhere else. They also had difficulty with “In which tissue is ABI3 most strongly expressed in?”, however in 2015 half of the participants completed the task in under ten seconds.

After using the tool for about ten minutes, participants were asked to complete a Google Forms questionnaire with a 7-point likert scale with the following questions:

  1. Please rate the quality of ePlant’s user interface.
  2. Please rate how useful ePlant is for Arabidopsis researchers.
  3. How would you describe the depth of information contained in ePlant?
  4. How would you compare ePlant against current methods for accessing the same information?
  5. How likely are you to use ePlant in a research project?
  6. How would you describe the depth of information contained in ePlant?
  7. How likely are you to use ePlant again?
  8. Please rate your overall user experience of using ePlant.

ePlant User Testing - Questionnaire Results

Responses across both years were generally positive, but there is a trend of improvement from one year to the next. The most positive responses are to “How likely are you to use ePlant in a research project?” and “How likely are you to use ePlant again?” These are essentially the same question, so it is comforting to know that the responses are virtually identical in 2015, at least. It is also comforting to see that in 2015 almost all participants responded with the most positive response for these questions. It suggests that ePlant successfully delivers on the objective to build a research platform that plant biologists want to use.

Quantitative data provides a snapshot of the overall efficacy of the tool, however the real booty from user testing is found in the qualitative data that was collected. Notes taken while coding the user testing screencasts produced a total of 88 new feature requests, bug reports, interface modifications, and other suggestions on how to improve the final product. These notes were entered into an online issue tracking platform called Pivotal Tracker that allows tasks to be sorted according to difficulty, and assigned to individual programmers to work on.

Pivotal Tracker


The intent is to complete the entire list by July 2016, in time for one final round of user testing at ICAR 2016 in Korea.

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