During this project, the human practice was an integral part of our work. When we first started workshopping ideas for the project we decided we wanted to do something that would actually make a difference and fill an existing need. To do this we began by contacting bioinformaticians working at Linköping University and asking them what was currently missing from their field.
The answer we got was in the form of a problem that needed solving. Currently, the clinicians are unable to analyze the data generated from their work and therefore need the help of bioinformaticians to do this. The importance of this kind of data analysis is currently increasing since methods like RNA-sequencing are becoming more accessible and therefore more widely used. To bridge this gap between clinicians and data-analysis we decided to develop the software ClusteRsy, for transcriptome analysis and biomarker discovery.
Since we wanted this software to have real value to our potential users we realized we would need feedback from several different groups during development. We, therefore, began by setting up a meeting with clinicians to get their opinions on what they would need from software like ClusteRsy. They said they wanted something that was easy to use and easy to learn, regardless of background.
These became our main criteria. To make sure we met them we decided we would have several sessions of beta testing where we would invite people with varying backgrounds to test ClusteRsy and give us relevant feedback. During the course of the project, we had time for two different beta-testing sessions.
During the first beta testing we mainly wanted feedback on user-friendliness. This way we would get early feedback on what could be improved in order for ClusteRsy to be easy to use. For this, we invited our PIs, bioinformaticians, and the experimental part of our team and they gave us varied feedback. For this, we made sure to give the users as little information as possible about how to use ClusteRsy, in order to truly test what would need clarification for a first-time user.
The feedback we got was that ClusteRsy was quite difficult to understand. The users had a hard time understanding how the input data worked and in general how to use the software and how the different parameters should be set to get good results. From the bioinformaticians, we also got feedback in the form of different features that could be added in order to improve the user experience and add versatility to ClusteRsy.
From this feedback we added a click-through tutorial to the homepage where we showed the different elements of the software and explained how to use it. We also added more explanations in the form of clickable buttons. In order to make it easier for the user to navigate the parameters we added an advanced-settings option for the more complicated parameters and let the user use default settings. We also gave the user the option to inspect the objects created by ClusteRsy so they could easily investigate their results, then we collected all the different types of objects in a database tab and added an option to download the results in the format most compatible with the industry-standard visualization tools.
When we had the second beta testing we were almost finished with ClusteRsy and therefore wanted to investigate how well we actually met the criteria we started with. For this, we invited the clinicians we had a meeting at the beginning of the project, bioinformaticians, our PIs, two other iGEM teams (Rochester and Imperial College London), and an employee at AstraZeneca we also had had contact with before.
Here we got feedback on both the improvements made since the first beta testing as well as on how well the tool fulfilled the initial criteria. The people who had used ClusteRsy during the first beta testing said that the improvements made satisfactorily addressed their initial concerns, which gave us confidence in our design process. The clinicians asked for more detailed instructions, preferably in the form of video tutorials. This would help when instructing new users and therefore make it a lot easier to learn how to use ClusteRsy.
Because of this feedback we decided to make video tutorials and upload them to YouTube. These will guide future users and aid anyone interested in using ClusteRsy.
