>>10217047Bioinformatics final year PhD here.
Yes, it's a great choice. As
>>10217162 said, big data in biology is just beginning and there is a massive bottle neck between producing the data and it actually getting analysed. Because of that there is always going to be a need for bioinformaticians.This is reflected in the fact that every research team seems to need one, whether they know it or not, by what their general research complaints are.
The downsides are:
- If another researcher finds out you are a bioinformatician, they will generally say "oh good, I need some data analysed/bioinformatics done, it should not be hard or take long but I don't know how to do it. Can you?". DO NOT FALL FOR THIS. This is a trap and a massive time sink since they generally underestimate how much cleaning and reformatting their data needs before analysis even begins, and then analysis can be maddening when the person who needs it done doesn't really know what it is they need done.
- A lot of senior researchers say they need bioinformaticians, but when it comes down to it, they don't have money for a dedicated one. What follows is them tacking on bits at the end of their PhD's students research outlines that say something like "perform in silico analysis". What they don't realise is that this is equivalent to saying "do lab stuff", and assuming a computer scientist who has never set foot in a bio lab is capable of this without disaster.
Some of the upsides:
- You will be called on by research teams to do analysis a lot, which means you can pick your battles and get in on lots of papers. Having said this, there is a trap here to be observed: always the bridesmaid, never the bride. That is, many papers where you are co-author and few where you are lead.
- You are generally surrounded by people who think what you do is basically witchcraft.
- Career paths everywhere.
