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- 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
Needs & Goals
1) A more efficient way for a biologist to find the optimal experimental procedure
Need: A better way for biologists to communicate their hard-earned knowledge with each other - both what worked, and what didn’t work.
Biologists have to navigate a large amount of information and go through tedious trial-and-error to identify which particular variant of an experimental protocol is appropriate for their situation. At present, there is a communication barrier; a lot of “little details” on a procedure are absent from the “official” published protocol in a journal, and interviewing other biologists one-on-one is time consuming. There is a need for a better way for biologists to communicate their hard-earned knowledge with each other - both what worked, and what didn’t work. This would hopefully reduce the trial-and-error process, as people would avoid doing things that were found to fail.
Goal: To improve knowledge sharing between biologists about these poorly-understood experimental protocols.
In achieving this goal, it would also be important to keep track of why a particular variation of a protocol exists, because the conditions for one experiment may be different in important ways from the conditions of another (eg: ChIP protocols vary based on the antibody used).
2) An easier way for biologists to analyze their data
Need: Biologists need to analyze their data in order to determine the success of their experiments and publish results.
Biologists frequently collaborate with computer scientists to analyze their experimental results. Often, the computer scientists will write a command-line tool which they can easily run with data provided by the biologists to perform the analysis. However, biologists often want to run the analysis on many different data sets with many different parameters, so they either have to make many requests of the computer scientists, or run the script themselves.
Goal: Have an easy way for biologists to run computational tools that their collaborators create.
Biologists are generally not comfortable with a command line interface and find it daunting to run code provided by their collaborators. They would like to have an easily accessible history of commands they have run in the past, as well as a tool which would prompt them to enter the necessary command line options and explain the meaning of each option. They would also like to see useful examples and possible caveats for each command.
3) An easier way for biologists to analyze their data
Need: Computational Biologists need to present their work to biologists in a way which is easy for them to use without substantial help.
Computational Biologists create the scripts which are used to analyze biological data sets. Since Biologists typically lack familiarity with programming and command-line utilities, computational biologists often need to spend a lot of time explaining the use of their scripts to biologists. Furthermore, computational biologists often spend a lot of time writing helper scripts to run different analyses. This is time-consuming and generally unnecessary for the computational biologist.
Both lab-bench and computational biologists alike would also benefit from maintaining information about the script such as notes on what certain inputs mean or special caveats when using the script. This kind of information is helpful if someone wants to come back to the script later on and edit it for a slightly different purpose.
Goal: Create a user-friendly interface for each of their scripts which biologists can use to analyze data themselves, with minimal help.
Pursuant to the needs outlined above, computational biologists would benefit from a programmer-facing interface which would allow them to define the function of a script in a way which is easy-to-understand and maintainable (for example, if the script needs to be edited later). The programmer would also like to present special information to the biologist (or his/her later self) about the parameters of the script as well as any special caveats to know when using the script.