In the past, I used the terms “bioinformatics” and “computational biology” somewhat interchangeably. I don’t do that anymore.
- Bioinformatics is about reusable tools and information resources for biology.
- Computational Biology is about biological insight.
It’s not necessarily a distinction between two different types of person. I know plenty of people who can do either type of work, building tools and then using them in their science. That said, people do seem to sort themselves to focus either on tool building or else on hypothesis testing.
The distinction is important because the metrics of project success are different, because we should manage projects and teams differently based on those metrics, and because career advancement and mentoring diverges rapidly between the two.
As always, I’m interested in your feedback, particularly if you disagree with me!
I’d like to call myself as translational bioinformatician, which is to collect data and build database and tool to solve clinical or biological problems. When I interview people,
I often like to ask them how they would call themselves. Are you a bioinformatics scientist, bioinformatics software engineer, bioinformatics database developer, statistical geneticist, or data analyst? I see most down-stream biological-Insight people prefer calling themselves as bioinformatics scientist than computational biologist.
The point about calling people by the name that they would prefer to be called is well taken. There is a lot of identity and ego wrapped up in your professional title. It can be jarring when “leadership,” makes changes to it … especially when they (we?) don’t seem to know the difference.
We’re going through this in some depth with the industry-wide transition away from “system administrators” and towards the new constellation of site reliability engineers, devops people, and so on. It’s all too easy for people to experience that change as the loss of “their” job (and thus their professional identity).
Hi Chris,
We have broken down the computational workflow and look at bioinformatics as an important but small component of the complete computational workflow. I’m sure I’m missing some stuff but, here’s how I have broken down the workflow. Happy to discuss offline if you like. – Jeffrey
1. Network
2. Sample Data Collection
3. Data Provenance
4. LIMS
5. Data Management
6. Data Tiering and Storage Integration
7. Computational Resources
8. Storage
9. Bioinformatics
10. Quality Control
11. Transportation
12. Security
I would have to know a lot more about the system being designed – the actual problem being solved – before I could develop an opinion on this framework.
With that caveat – that I might be missing your point – I think that I would use a different word for number 9. Maybe “computational methods” or “analysis.”
It’s really easy to wind up talking past each other when we don’t check in on what the words mean!
See this post (https://biology.stackexchange.com/q/3192/43), the answers, and the comments for a frustrating take on the topic. Your definition is one that is shared by many, but it is hardly a widely accepted definition.