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Those involved in teaching bioinformatics may wish to check out or contribute to the efforts of the ICSB Education Committee who are seeking input to identify a consensus bioinformatics curriculum.

They’ve started a blog for this purpose, including a draft curriculum (.docx file) drawn from an initial survey.

With no disrespect to them the initial survey is limited given the large size of the field, being drawn from just 41 individuals. Nevertheless let’s briefly look at a few points raised.

They show a graph of responses to the question ’What are the most important topics that someone should understand in order to work in the field of computational biology / bioinformatics?’

most-important-aspect-of-bioinfo-to-teach

I have to admit I don’t quite agree that programming and then statistics are the most important.

Let’s presume the student is already enrolled in a biology major. In that context programming and statistics are certainly important practical background that are unlikely to be covered by the rest of their courses.

A key element missing though, that I’ve touched on before, is the theoretical biology that is used in and underpins computational biology. To me that is the most important background. It is not necessarily taught much in ‘straight’ biology courses.

This naturally allies with the established techniques used in the field and with an understanding of how bioinformatics has developed. (Frequent readers here will know I believe it’s important to understand the history of your field.)

They suggest that the curriculum be broken into two broad topic areas:

  • computation, mathematics and statistics (in yellow in the graph above)
  • chemistry and biology (in blue in the graph above)

There was also a suggestion that ISMB’s topic areas be used as the topic areas for a curriculum (see .docx file).

Footnotes

I’d elaborate further, but perhaps readers are grateful I haven’t time! Feel free to add your thoughts in the comments.

Other articles on Code for life:

Teaching bioinformatics at high school

Choosing an algorithm — benchmarking bioinformatics

Research project coding v. end-user application coding

New bioinformatics journal — EMBnet.journal

Literate and test-driven programming (in bioinformatics)

Retrospective–The mythology of bioinformatics

Know the history of your field, be it science or pottery

Reproducible research and computational biology