Abstract
Bioinformatics is an essential discipline for biologists. It also has a reputation of being difficult for those without a strong quantitative and computer science background. At Lancaster University, we have developed modules for the integration of bioinformatics skills training into our undergraduate biology degree portfolio. This article describes those modules, situating them in the context of the accumulated quarter century of literature on bioinformatics education. The constant evolution of bioinformatics as a discipline is emphasized, drawing attention to the continual necessity to revise and upgrade those skills being taught, even at undergraduate level. Our overarching aim is to equip students both with a portfolio of skills in the currently most essential bioinformatics tools and with the confidence to continue their own bioinformatics skills development at postgraduate or professional level.
Highlights
Introduction toBio-Linux Coursework None Exam MCQ ReportReport analysing Report demonstrating protein for demonstrating competence in selection, and its competence in techniquesBayesian techniques phylogeneticsEssays (2 from 4 Essays (2 from 3 options) options)Table 2 illustrates how the bulk of the bioinformatics delivery at Lancaster takes place in 2nd and 4th years
Bioinformatics has benefitted over the years by influxes of computer science graduates, at times of transition, e.g. when microarrays, systems biology or deep sequencing made their first appearances each with a whole raft of new problems to be solved
Many spend most of their time using existing software tools to analyse data produced in the lab, and need to know only enough programming to be able to organize their data workflows. This distinction between the “pure” bioinformaticians engaged in software development, and the “applied” bioinformaticians engaged in data analysis is often based on undergraduate degree background: computer scientists being the former and biologists the latter
Summary
Most of the readers of this article will probably know the answer to the above question and, if they read further, may wonder why I feel it necessary to offer a potted history of the field. Bioinformatics has benefitted over the years by influxes of computer science graduates, at times of transition, e.g. when microarrays, systems biology or deep sequencing made their first appearances each with a whole raft of new problems to be solved. Many spend most of their time using existing software tools to analyse data produced in the lab, and need to know only enough programming to be able to organize their data workflows This distinction between the “pure” bioinformaticians engaged in software development, and the “applied” bioinformaticians engaged in data analysis is often based on undergraduate degree background: computer scientists being the former and biologists the latter. This is a narrower remit, but one which presents its own challenges
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