Expression Angler

Expression Angler

Expression Angler

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With coding support from Jim Fan, I recently gave a UX design makeover to a 10-year-old bioinformatics tool written by my supervisor, Dr. Nicholas Provart.  The Expression Angler was launched in 2005 as part of the original collection of visual analytics tools on the BAR (Toufighi et al 2005). It calculates the correlation coefficients for expression for all gene expression vectors as compared to the one for the AGI ID or gene name that a user enters. In other words, it helps users identify Arabidopsis genes with similar expression patterns to any gene of interest. Although many people have used the tool successfully, the layout and messaging on the page makes it very difficult to learn how to use. Here is a screengrab of the original tool:

The original Expression Angler by Toufighi et al. (2005).

The original Expression Angler by Toufighi et al. (2005).

In addition to finding similar expression patterns to a known gene, the tool also offers a “custom bait” feature (added after the Toufighi et al. 2005 publication) which allows users to find genes that are up-regulated or down-regulated in specific tissues. That means users can search for genes by describing an expression pattern they may be interested in (e.g., “show me the top 25 genes that are expressed in the roots but not in the flowers”). This is a very useful feature, however once again, the information flow and messaging makes it very difficult for users to learn. The interface is 100% text based and users need to enter their query via a series of text boxes then scroll down several page lengths to find a ‘Submit Query’ button.

The original “custom bait” feature in Expression Angler.

The original “custom bait” feature in Expression Angler.

I updated the tool by simplifying the messaging and adding a graphic user interface for selecting tissues. Instead of a table of input boxes, users enter their search query by assigning hot or cold colors to a graphic representation of the tissues they are interested in. Effectively, users “paint” an electronic fluorescent pictograph as a search query, and the tool then searches for genes that match it.

Click a tissue then use the slider to set a level.

Click a tissue then use the slider to set a level.

The user follows four clearly outlined steps:

  1. Select a view.
  2. Set values by clicking on tissues.
  3. Limit the number of results to find. (Top 10? Top 25?)
  4. Press the search button.

Here is the results page of the above query:

These are the top 10 genes that match the gene expression profile entered above.

The output page displays the top 10 genes that match the gene expression profile queried above.

This new version of the tool exists as a standalone tool on the BAR and will soon be available on Araport. It will also be included as part of the gene selection panel in ePlant. We have written a manuscript that combines this tool with Dr. Ryan Austin’s yet-to-be publish PhD work from Nicholas Provart’s lab several years ago. His work consisted of designing “custom baits” for several different conditions, predicting cis-elements in sets of promoters identified with the baits, exploring these promoters using a tool he developed called Cistome, then testing the cis-elements in planta. This new expression angler tool serves as the front end of the “custom bait” method.

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