Bio-informatics

Cuck here. AMA

why did you leave /v/ ?

Thank you so much for your answer. Do you mind giving me your email adress? I still have a lot of (personal) questions about bio-informatics and your work as a genomist.

They made fun of my switch

I'm an unemployed gamer like 99% of the board but I hope you will find someone who can help. Nier Automata is fucking awesome.

Nier Automata is awesome indeed

Is learning Python enough?

Learn how to work my python and ill tell you

No worries. I would rather not give my email address out on here, it may be sci but it's still Veeky Forums. I am happy to answer any questions here but I appreciate that if you have personal ones you are not going to want to ask them.

It's a start. A great deal of scripting in this field is done using python, thats for sure. Though Sanger and a few other places like to use perl for some reason...
I would also get familliar with bash scripting too, since a lot of what you can do in bash is very useful in bioinf for its speed and concise nature. R is also invaluable.
A deep understanding of your biological field is, of course, essential. As is a full understanding of what question you are attempting to answer in your work and how you might go about doing that. Looking through literature at the way the staple tools work (the plethora of assemblers and aligners etc) work is also essential and always ongoing, since a lot of these tools have actually very simple mechanics if you strip them down.

I have zero experience in programming. Realistically, how long would it take for me to learn all that on my own?

>bash
>very useful in bioinf
>for its speed

Oh god, I can't, my sides...

Is this literally a troll tier profession? I feel like a 2nd year ITT Tech student could revolutionize this field.

Should I get double major in CS/Biology, Veeky Forums?

No you idiot, its speed in writing. you can accomplish in terms of data extraction in 1 line what in C would take 10. You have fallen into the trap that so many people fall into which is "fastest to run is best lol", this is absolute nonsense during an actual project when you are on a time contraint for the actual writing of scripts, which, unless you are building something where you need to squeeze every bit of efficiency out of it you can (which is almost never), means that time spent actually writing is the main concern. Therefore concise languages and avoiding unnecessary writing wins (hence python dominating the field). Also, as I said before, the field is largely populated by computer scientists who are pretty proficient in their field.

not long, as long as you use them pretty much daily. Python is famously easy to learn because of its intuitive syntax and general lack of weirdness.

Concise captures what you're describing, and you're right that a bash script can be much more concise than C code, but speed doesn't fit. Using the words "speedy" anywhere near "bash script" is just bad.

>means that time spent actually writing is the main concern.

So you're implying that your code write time is a meaningful amount of your overall time? That's some serious brainlet tier stuff. Why not just use excel for something that trivial?

You keep doing you though senpai, while the real data scientists are running gpu clusters.

Have you guys ever came on a petri dish, on some stupid bacteria? What does it happen? Does the sperm attack the bacteria? Do the bacteria eat your sperm like the microscopic whores they are?

I want to study Population genetics which is a similar field. I know biology and math but zero programming. How much programming do I need?

of course writing code takes up a meaningful amount of time when you are constructing bioinformatics pipelines. This is why you try and reduce the amount of time spent writing. Also, I am pretty sure I started off by saying that I am a biologist, which means I clearly wont be producing as much code in the same time as a CS will, but it also means I don't have to be babied through the actual biology of it and don't need to spend any time trying to familliarising myself with the various 'omics' before I can approach them in research.

Good luck in science if your position is "anyone who doesn't know what I know is a brainlet".

>Why not just use excel for something that trivial?
No idea what you mean, you are confusing yourself.

I genuinely don't care what you consider a 'real data scientist'. My research gets published which means, at the very least, it is progression of some kind. Stay in your world where the only thing that matters is what you say you do rather than what you produce if you like, but leave me out of it, I have actual applicable research to carry out.

You would be analysing genomic variation and likely building pipelines to glue together various tools to do this so a modest ability to script would be pretty much essential. However, if you are familliar with biology and mathematics then this wont be a problem, the more mathematics you know the more a lot of the CS concepts will feel familliar to you and the rest is just syntax.

I like bash but calling it concise annoys me because it has such poorly defined semantics. Bash is useful because it's so popular on unix systems but it's the opposite of intuitive. Like for example modifying a file in place. I only use bash for quickies that are easy to audit; I'd use python or c++ for anything more serious.

>So you're implying that your code write time is a meaningful amount of your overall time?
It's extremely important. How can you not realize this?

I completely understand and totally agree, I seem to be irritating one person for calling it 'fast' and another for calling it 'concise'!. I do not use it for anything large scale, mostly just for quickly running tools, which saves me a lot of time, and for example, extracting key data points from output files. All of my actual scripting is done using python.

Picking up the programming necessary to work as a Bioinformatistician isn't that hard. I learned python on my own before I started working in the lab and I'm fine. They'll help you out, but you really need to make an effort to learn (I've extensively used BioPython and bs4 libraries in my work). Also knowing how to use the Linux command line and R never hurts.

I'm currently a first year Ph.D. student in a BioInfo program. I graduated with a Biochemistry degree with no computer science experience before starting in the lab.

is correct. Biological research is at a point now where we're over saturated with data and no one with the expertise to analyze it and make a story out of it because 9/10 biologists that I've met and went to undergraduate with were the 'Oh I'm smart but bad at math and stats, teehee xD' mindset...We need people good at quantitative analysis to make sense of anything.

But even more important is using tools developed by others.

You want a head start? Look at any RNA-seq paper. They'll have the raw data available stored on a database for you to look at and analyze on your own. You'll have to do a lot of google searching for how to get everything formatted properly and the proper tools downloaded, but it'll give you first hand experience about what it's like to work as a Bioinformatistician.

This is literally what I'm doing in my Quantitative Systems Biology class right now. The link to the paper we're analyzing is: ncbi.nlm.nih.gov/pmc/articles/PMC4224148/

This will sound shallow, but what's the pay like for industry jobs? (r and d)

What is the average day like? What are the major things being studied? How much is evolutionary biology applied and do you ever go out in the field?
Oh and most important, how fun is it?

Better than in academia, but further than that I have no idea. I was in industry as a research Parasitologist for a few years but academia is where I intend to stay.

My average day involves writing code to glue together various bioinf tools so I can basically push large data sets through this pipeline, and reading papers on approaches other people have used for similar research or methods I might find useful. For example, at the moment I am analysing the k-mer spectra of various different protozoan isolate genomes to look isolate, species and genus specific sequences.

The majority of bioinformatics focusses on cancer biology and analysis of cancer genomes. At the moment there seems to be quite a bit of emphasis on metagenomics too, since in-silico analysis of metagenomic data sets will dominate future clinical microbiology. There is always emphasis on efficient genome assembly too, but a lot of the research is looking at read library analysis since it circumvents the need to carry out assembly.

Evolutionary biology pretty much underpins the field. Genome analysis IS evolutionary biology since sequence variation is the mechanics of species variation. All bioinformatics is built on evolutionary biology (as all biology is).

I love what I do, I cannot think of anything I would find more interesting. It also helps that it truly is the future of biology because of the current bottleneck at the data analysis stage.

Undergrad biofag here who had to learn some Python for bachelor thesis. About 1-2 weeks is enough for you to start doing useful stuff.
If you work harder you can do it in even shorter amounts of time.
Would you recommend bioinformatics for somebody who's interested in the protein side of things?
I've applied to a bioinformatics master's program but had a few doubts lately.

I'm not the guy you're replying to, but I still don't understand.

The way I mean it, (and probably the way the other user meant it) most of the time developing a program is spent on thinking on how to do it and planning the software architecture, the actual typing of the code is a meaningless fraction of the actual time being spent.

I've nothing to do with bioinformatics though, but i do work as a software developer.

>Want to study the ecosystem in the field but know there are know jobs
Guess I am going into Bioinformatics/Populations biology. Any type actually let me do field study at all?

Community Ecology is a field of study under "Population Biology". Does it also have the job availability of population genetics and bioinformatics?

>This is literally what I'm doing in my Quantitative Systems Biology class right now. The link to the paper we're analyzing is: ncbi.nlm.nih

Thank you so much

Is NCBI ever going to clean up the mass of redundant data and start making sure that redundant sequences can't be logged more than once?

Yes, bio-informatics contains a lot of molecular biology. Proteomics included.

What is the percentage of biology, chemistry, mathematics, statistics, informatics (including programming) you get during the study?

Like, what I've seen its:
Biology: 35% Molecular biology, microbiology, biotechnology, biochemistry, etc.
Chemistry: 5% the basics such as hydrogen bonds etc.
Mathematics and statistics 25% the good ol' shit
Informatics: 30% bioPython, bioPerl and such

I'm scared that the the study is programming>biology

>95%

100%??? Brainlet pleb

not in bioinformatics buti did program a global dna sequence alignment using the needleman wunsch algorithm and traceback

Is this field more computational ( simulation) or more processing data biologists made.

Really depends where you work, but it's 50/50 in the most cases.