Who We Are, in no particular order:
- Michael Maddox
- Avanti Shrikumar
- Sumaiya Nazeen
- Rebecca Taft
The Problem We Are Solving
Biologists lack technology that suits their needs and skill sets. We identified two main shortcomings: the first is difficulty in using code written by computational biologists. The second is a lack of a good system for tracking different variations of experimental procedures.
User Analysis
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Observations and Interviews
1. V.
V. is a postdoc working at a Biology lab at MIT that requires a lot of data processing support. V. herself does not do any computational work, although often solicits the help of computer scientists in analyzing her data.
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As the years progress, however, lab notebooks can become bulky, and the task of flipping through them to identify the desired iteration of an experiment can be tedious. Some biologists use electronic lab notebooks, but they are still not optimized for tracking the various iterations of an experiment.
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Lessons learned:
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- Biologists like V. are comfortable using GUI workflows to perform computational tasks (eg: the Galaxy webserver).
- If there is no available GUI, biologists like V. are dependent on a programmer for performing the analysis. However, they must work closely with the programmer to ensure that there is full understanding of the question under investigation. Communication is paramount.
- Biologists like V. do not personally run scripts that a programmer has written, even if the programmer has a readily useable script that could perform the desired analysis.
- Novel experimental protocols are not set in stone; the level of detail that is published in a journal is much less than the level of detail that one could obtain from talking to the biologists who developed the protocol.
- Experimental protocols also vary depending on the conditions/reagents used (eg: Chromatin immunoprecipitation is different for different antibodies).
- The “little details” are often critical to understanding how to get your experiment to work. It is important to gather as much information as possible directly from the people who have done the experiment in the past.
- Negative results are important too; if an experiment fails, it is valuable to remember why it failed.
- There is currently no efficient way to share the ‘little details’ about an experimental procedure between biologists.
2. S.
S is a graduate student in the Biology department at MIT and needs to do biological data processing for her research. She needs to run big computational jobs on the lab’s cluster occasionally. She often notes down the required commands and parameters for a program she needs to run in EverNote (A free note taking software for macbook users), so that she can copy paste them later. But she often forgets where she has put her notes and also finds it difficult to search and find the location of the input files on the server if she needs to do the same analysis again. When she can’t locate her notes about the required commands, she looks at the help file in the command line but does not find it very helpful since it has a lot of text and she is only interested in finding how to run the program. She feels that, it would be easier for her to do data analysis if she could just fill the boxes with the parameters and select the command to run. She would also want to see examples, caveats and history of her previous selections.
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Lessons learned
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- Uncomfortable in using the commands and parameters to run a program, often forgets them
- Feels comfortable in filling in forms and selecting commands
- Looks up the command line help file to see how to run a program
- Wants to see command format, examples and possible caveats
- Wants to see the history of commands she ran in the past
3. X.
X. is currently primarily a computational biologist, but has a background in lab-bench biology. He has performed experiments before and is familiar with the needs of lab-bench biologists, but he himself rarely performs experiments at present (but will in the future).
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Finally, X. also agreed with V.’s assessment of the difficulty consolidating information about experiment protocols. X. said that the current method of combining handwritten notes is grossly inefficient and error-prone. Like V., X. would like to have some interface which provides an easy way to take shareable e-notes about particular experiment protocols with step-by-step instructions and comments from different team members about their experiences implementing that protocol.
4. J.
J. is a postdoc in a biology lab at MIT. We spoke with him about issues he has with finding a usable protocol for experiments in the lab, as well as issues with communicating with his collaborators in computational fields.
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- He didn’t know off the top of his head how to find the number of lines in a file, so he googled for “unix line numbers” and found some entries about the command “nl”
- Once he realized that nl did not do what he wanted, he searched more carefully and found the command “wc --l”, but misread the l as a 1.
- Eventually he tried just running “wc”, which outputs three unlabeled numbers. He guessed that the smallest of the three numbers was the number of lines, but was not sure.
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Lessons learned
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- Feels that published protocols do not contain all the necessary details
- Would like a way to easily share protocols and related notes within the lab as well as with collaborators
- Finds common tasks on a Unix terminal very difficult due to a lack of learnability of commands and command-line options
- Typically resorts to outside sources such as Google for documentation since it is easier to find usage examples
- Confusions such as “-l” with “-1” can occur, so they must be avoided from a programmer standpoint.
User Classes
Based on our interviews, we identified the following two user classes:
Biologists
- Most of their day-to-day work involves performing experiments in a wet lab
- They often need computational support for analyzing their experimental results
- Generally not comfortable with using a command-line interface
- Most of their detailed notes are kept in a lab notebook, not on the computer
Computational biologists
- They are typically computer scientists by training
- Typically have a basic knowledge of biology, but often need help from biologists to interpret biological significance of results
- Their work often involves writing scripts for analyzing biological data, but they do not have the resources to take the time to create user-friendly programs