3 ms·

Well, I learned to code in order to do bioinformatics. I didn't start out as a programmer looking to specialize. I agree that it seems that programming + X is

by xaa 10y ago

Well, I learned to code in order to do bioinformatics. I didn't start out as a programmer looking to specialize. I agree that it seems that programming + X is often a much more powerful combination than programming or X alone. But I would say to anyone starting out that it is better to head towards, and get the formal qualifications for X, and learn programming on the side (or if in college, get the major in X and a minor in CS). The reasons are that programming is relatively easy to learn outside of classes, and programming itself really doesn't require formal qualifications. For me, biology has been orders of magnitude harder to learn than coding. With coding, you can learn by doing, and you get immediate feedback from the compiler/interpreter. Not so with biology. You can be mistaken for weeks/months/years and never realize it until you read just the right article. And often, domain-specific knowledge like biology seems to be composed of thousands of tiny details without too many general principles, whereas if you learn a few basic principles for programming, you can learn pretty much any new language or framework easily. I don't know if this is generally true of all specializations, but if so, it would behoove someone to get started on learning the specialization-related information ASAP, because that will take much longer to reach proficiency than for programming.

noname123 10y ago
Thanks xaa for your thoughtful comments. I've taken a few MOOC on Bioinformatics. Biology certain seems much harder to get feedback esp. if you work in the wet lab, but I'm curious however about what particulars in Bioinformatics in your opinion is harder to learn (assuming that a computer person has taken Biology 101, aware of DNA to RNA, transcription, translation, mutations, variants, alleles, genotypes, haplotypes, basic cell biology; and maybe let's say even a step up, familiar with basic's of processing NGS files, processing assembly, variant calling, RNA-Seq and ChIPSeq differential analysis). Is it the statistics (finding the right statistics inference test and knowing what are the pitfalls of your models)? Or is it understanding the underpinnings of the biology behind a particular pathway you're studying? (e.g., knowing how to perform or iterate on the wet lab experiments of doing in-vivo or in-vitro experiments). Thanks again for your comments.
- xaa 10y ago
  > is it understanding the underpinnings of the biology behind a particular pathway you're studying Yes, this is it. You are right that the really general basic concepts (what is DNA? how does transcription work?) are not all that hard to learn. And it is certainly part of the core job skills to know, e.g., how to process sequencing data and how to use statistics. But in the real world, bioinformaticians work in collaboration with wet-lab biologists. So, a typical project for me might look like this: collaborator comes in and tells me that his lab studies a particular protein X that operates at the presynaptic terminal in neurons. And they are collecting data about how some perturbation to X affects other cellular systems. The data will often be some combination of sequencing/array data and more specific wet-lab experiments (western blots, electrophysiology, etc). So, in that case, to really do my job well, I have to go back and read in depth about the presynaptic interface, the major proteins and the mechanisms that work there. I may already know the generalities, but to really be able to interpret the data correctly, I need to know the details. Now, imagine doing this process 10-20X a year for different collaborations and totally different biological systems. It's very hard to keep up. Now, it is certainly possible to be the kind of bioinformatician who is "give me your sequencing data and I'll give you the DE genes back", treating everything as a purely technical problem. But these kinds of bioinformaticians are not as much sought after because the wet-lab biologist doesn't want Excel spreadsheets full of lists, they really want to know "what do my results mean?". And to answer that really requires both the bioinformatician and the wet-lab biologist to understand what the other is doing at a more than superficial level. In short, the hard part isn't learning the things that are used in every project, the hard part is learning the domain-specific information that is relevant to each individual project.

by xaa 10y ago

noname123 10y ago

Thanks xaa for your thoughtful comments. I've taken a few MOOC on Bioinformatics. Biology certain seems much harder to get feedback esp. if you work in the wet lab, but I'm curious however about what particulars in Bioinformatics in your opinion is harder to learn (assuming that a computer person has taken Biology 101, aware of DNA to RNA, transcription, translation, mutations, variants, alleles, genotypes, haplotypes, basic cell biology; and maybe let's say even a step up, familiar with basic's of processing NGS files, processing assembly, variant calling, RNA-Seq and ChIPSeq differential analysis). Is it the statistics (finding the right statistics inference test and knowing what are the pitfalls of your models)? Or is it understanding the underpinnings of the biology behind a particular pathway you're studying? (e.g., knowing how to perform or iterate on the wet lab experiments of doing in-vivo or in-vitro experiments). Thanks again for your comments.

xaa 10y ago
> is it understanding the underpinnings of the biology behind a particular pathway you're studying Yes, this is it. You are right that the really general basic concepts (what is DNA? how does transcription work?) are not all that hard to learn. And it is certainly part of the core job skills to know, e.g., how to process sequencing data and how to use statistics. But in the real world, bioinformaticians work in collaboration with wet-lab biologists. So, a typical project for me might look like this: collaborator comes in and tells me that his lab studies a particular protein X that operates at the presynaptic terminal in neurons. And they are collecting data about how some perturbation to X affects other cellular systems. The data will often be some combination of sequencing/array data and more specific wet-lab experiments (western blots, electrophysiology, etc). So, in that case, to really do my job well, I have to go back and read in depth about the presynaptic interface, the major proteins and the mechanisms that work there. I may already know the generalities, but to really be able to interpret the data correctly, I need to know the details. Now, imagine doing this process 10-20X a year for different collaborations and totally different biological systems. It's very hard to keep up. Now, it is certainly possible to be the kind of bioinformatician who is "give me your sequencing data and I'll give you the DE genes back", treating everything as a purely technical problem. But these kinds of bioinformaticians are not as much sought after because the wet-lab biologist doesn't want Excel spreadsheets full of lists, they really want to know "what do my results mean?". And to answer that really requires both the bioinformatician and the wet-lab biologist to understand what the other is doing at a more than superficial level. In short, the hard part isn't learning the things that are used in every project, the hard part is learning the domain-specific information that is relevant to each individual project.