Vineeta (V) is a lab-bench biologist and Xing (X) is a computational biologist. Vineeta needs to analyze her data, so she asks Xing for help and he agrees to share his Montecarlo script with her.
Xing adds his script on BioMate by uploading the script, creating a descriptive name, writing out the command structure, and specifying important information about each parameter. The form-like interface allows him to easily edit the command structure and add or update the list of parameters. When he is satisfied with the specification, he can easily share the script with Vineeta by entering her email address.
Vineeta receives the shared script, and tries it out a few times on BioMate. She notices that the simulation runs for 100 iterations by default, which is not enough to effectively analyze her data. So she sends Xing an email with this feedback and states that she needs to be able to control the number of iterations. (Note that in this design, there is no built-in messaging system for Vineeta to send Xing her feedback and results because we think email will be sufficient for this task.)
Xing updates his script by adding a new parameter for the number of iterations. He easily shares the updated script on BioMate by uploading the updated version of the script, updating the command structure, and adding a new row for the new parameter.
Vineeta logs onto BioMate and sees the list of scripts that have been shared with her. This landing page is what both computational biologists and lab-bench biologists see, and it is reminiscent of the Dropbox landing page. By default the scripts are sorted by date, but the table interface allows users to easily sort by any column. The search field also allows users to easily locate the script they are looking for. Vineeta sees that Xing's script, montecarlo.pl, has been updated today. She right-clicks on the script and selects "Run...", which automatically runs the most recently added version.
Next Vineeta sees a list of required and optional parameters to fill out, which correspond to the parameters that Xing specified when sharing the script. Each entry field is labeled with a user-friendly name and is already pre-populated with the default values that Xing specified. If Vineeta is confused about any of the parameters, she can click on the "?" to view Xing's notes. This form-like interface is meant to be very familiar to lab-bench biologists and much less intimidating and error-prone than the command line.
When Vineeta is satisfied with her input, she can choose to generate the command, save her parameters, or write a note on this script. Saving the parameters ensures that her current input will become the default next time she runs this script. Adding a note allows her to write some free-form text associated with this script, which she can later refer back to. If she clicks "Generate Command Line", she sees a popup with the command text. She can then copy and paste this command into her terminal to run the script, or save the command to her notes so she can refer back to it later. This allows her to keep a command "history" since notes are tagged with the corresponding script as well as the date they were created.
Vineeta decides to run the script, so she copies and pastes the command into her terminal. She runs it several times with different parameters, and realizes that she gets the best results when the number of iterations is greater than 1000. She adds a note on BioMate with this information for future reference, and saves her parameters.
A few days later, Vineeta wants to rerun the Montecarlo script on some new input data. She locates the script on BioMate, and this time all she needs to change is the name of the input file since all of her other parameters have been saved. She can also view her notes to refresh her memory about how best to use the script and see her command history.
