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by __keshav
1980 days ago
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Computational biology is a pretty broad term. Usually things that have to do with computers & biology are bioinformatics or computational biology. Briefly, for bioinformatics you’d need things like C++ under your belt and interest to come up with ways to make things work really fast and optimal on huge sequencing datasets. Comp bio is a super fun field to be in. For me it's mostly using computers to do biology. But it’s a mixture of domain knowledge in bio, a good grasp of stats, and a whole lot of programming (usually not terribly difficult tasks, though). Basic python and R are what you absolutely need to know (I started with intermediate python, no R). To do comp bio well, you need to learn computational statistics. I can’t stress enough how much knowing statistics matters in this case because there are so many assumptions that all sorts of libraries make about sequencing data and you need to decide for yourself how you’ll go about things and produce good science. On a practical level for comp bio, I suggest: 1. Learning python & R 2. Basic knowledge 3. Knowing what your fave labs use for techniques (eg NGS? What kind of NGS?) and learning how it works, 4. Learning probability & statistics (lin alg always helps too) 5. If you got bored, learn clustering methods... Because, good god people in this field love seeing pretty tSNE figures and 98% of them have no idea how they just produced what they did but make biological assumptions based on it. You’ll probably have to learn them anyway |
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But as broad advice:
If you know a broad technique you're interested in, see which immunology lab does that and go from there.
If you already know the types of immunology questions you wanna go after, find the lab that studies that question, then see which techniques they use and learn those.