8.1 years ago by
You can take inspiration from the Software Carpentry for Bioinformatician project. SC is a project to create an introductory documentation for bioinformaticians and researchers wishing to enter this field, it has been around for almost 10 years and is a highly recommended resource for any new researcher.
They also have many slides already available.
You can also look at the Programming4Scientist blog, which unfortunately seems to have not been updated lately, but has a good archive nevertheless.
let me see, an outline for a talk could be:
- introduce Margaret Dayhoff as the founder of bioinformatics. Explain how she created the PAM matrices to solve the problem of comparing two sequences, and to quantify the 'similarity' between two proteins.
- explain public databases, at least the NCBI ones and Uniprot.
- explain how computational tools can save time and money in a research project. You can make the example of drug modeling that makes easier to test new drug candidates. Most old school researches (and their students) think of bioinformatics as not a science, and that using computational tools is not research. You should try to convince them of the opposite, with good examples.
- explain how a software must be carefully tested before being considered complete. Wet scientists will like that, because they like designing tests for their experiments. You can make the analogy that, as a wet scientist has to design controls and markers for a western blot, a bioinformatician has to design the right tests to prove that her/his program works perfectly, without testing too much or not enough
- some wet biologists are afraid to share their data with someone else or to release it publicly. They think that since they have created the data, they should do all the analysis by themselves. Explain that, even if they have this right, a computational scientist with a good experience in analyzing data can make a better analysis, in a much shorter time. Moreover, it is important to compare the data produced by many independent researchers, convince that if they share their data, somebody else can compare their results with others and get a better picture of the hidden elephant.