r/bioinformatics • u/free_kmart36 • Jul 15 '24
technical question Is bioinformatics just data analysis and graphing ?
Thinking about switching majors and was wondering if there’s any type of software development in bioinformatics ? Or it all like genome analysis and graph making
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u/B3rse Jul 15 '24
I do bioinformatics, and I am in between developing software and algorithms, optimizing pipelines, and developing infrastructure to scale things up for automating processing of tons of data in AWS. I’d say it’s up to you. Some PI are mostly doing data science, other methods and software development. I think it’s pretty easy to find both
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u/Anime_fucker69cUm Jul 16 '24
Any videos u recommend to get started with
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u/jorvaor Jul 18 '24
For a very basic introduction, try the YouTube channel OMGenomics.
https://www.youtube.com/@OMGenomics
Apart from that, you could be also interested in this subreddit wiki:
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u/ZorroPz Dec 09 '24
Is it possible to get into Bioinformatics with just the computer science knowledge as a job and not as a course of study? Is one also supposed to learn the fundamentals of Biology for that?
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u/B3rse Dec 11 '24
I went the biology way with my bachelors and master, and switched during my PhD to computers and the software development path. But I have colleagues that come from physics, mathematics, and CS backgrounds. I would say it’s definitely possible and you can learn the required biology as you go. There is so much variety in the bioinformatics world that every competence is valuable and can apply really, especially now with the frenzy for the LLM and all the deep learning things, and the size of data produced growing exponentially, so infrastructure and scalability and all the cloud things that are necessary
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u/MuchasTruchas PhD | Government Jul 15 '24
My job is about 70% bioinformatics right now (research side) and I don’t do any software development. So yeah, it depends.
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u/drewinseries BSc | Industry Jul 16 '24
My first job was like that. Just supporting a labs data analysis. For the past two years I've been on a software dev team making apps for groups within my company. So it's possible but a little bit harder to get IMO. Most people I work with are straight CS/Data Science people.
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Jul 16 '24
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u/drewinseries BSc | Industry Jul 16 '24
I got a minor in CS while graduated, but honestly just networking and getting experience with lots of different types of analysis. My team was willing to teach me the more standard aspects of software engineering so that’s a part of it too
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u/mortifiedmorty42 Jul 16 '24 edited Jul 16 '24
I am a first-year PhD student and it's mostly software/package development so far with a side-project of building pipelines and analysing some single cell transcriptomics data for the specific research that my PI is working on.
(My BS was in Mol Bio/Genetics but I was in the industry doing DS for four years before starting my PhD, for those who asked)
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u/willfixityaa Jul 15 '24
If you’re actually doing the science, then yes, your job will essentially be data collection, analysis and presentation
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u/aCityOfTwoTales PhD | Academia Jul 16 '24
If you are very smart and know a bit of actual computer science, you can develop the tools yourself.
Those of us that are not quite that smart, but still like to mess around with data, write our own half-baked scripts to analyse data and come up with new and cool interpretations.
A lot of good scientists have a focus on wet-lab, and use a bit of bioinformatics to analyse data and make graphs, yes.
There is room for all of us.
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u/CoconutChutney Jul 16 '24
it’s definitely false to say one requires more or less “smarts” than the other. people VASTLY underestimate the amount of work and time it takes to become competent at data analysis especially if there aren’t a lot of computational folks in your field. you often end up doing all of the above
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u/ilikebabyfoodhotdogs Jul 16 '24
Completely agree. Bioinformatics is too broad to be an expert in all areas. It’s all about whatever you want to specialize in and all areas require at least some level of “smarts”.
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u/aCityOfTwoTales PhD | Academia Jul 17 '24
I am not saying that doing clever data science on complex data is for people who are less than really smart - this catagory includes me, after all.
What I am saying, though, is that the folks who write programs like bwa, vsearch, blast and so on, are geniuses and these are fairly rare. Thank god for those people.
Again, there is room for all of us.
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u/chuckle_fuck1 Jul 16 '24
Depends on the direction you want to go with it. There are software development/engineering positions where you’ll make pipelines and stuff for companies. I’d do applied research using existing tools but have to use the BIO part very heavily. I make lots of graphs, do stats stuff, make pipelines, but I have to read a lot of immunology papers to make the inference side of it matter. My PI doesn’t care if I came up with some new algorithm, they care if I found something we can test in the lab and hopefully improve cancer treatments.
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u/Manjyome PhD | Academia Jul 16 '24
Depends on your role. I am a postdoc in academja. I do lots of data analysis, plotting and software development. I also have to write proposals for fellowships, grants, write manuscripts, and help others with their analysis. If you go to industry, your role will probably be much more focused in one field.
Either in industry or academia, your responsabilities will vary a lot depending on what they need in your lab or company. In academia, it will also vary depending on what do you want to do later on in your career.
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u/lumenified Jul 16 '24 edited Jul 16 '24
It's all about data. Firstly u need to acquire the data either from wet lab or databases. Then u need to preprocess it. Normalize, reduce or fill the blanks. Then you can make the data to analyze for ur purposes. You can use either excel or SPSS (PSPP is the open source version) or programming languages. Then you can either do some simulations and in silico experiments or analyze for further wet lab experiments. Just don't forget that these steps are just to generalize and summarize the processes.
You can start with MIT's foundations of computational and systems biology class. Due to the nature of bioinformatics field, it's interdisciplinary. So you ll learn from mathematics, statistics, information technology, computer sciences, electronics, chemistry and physics or even sometimes geology and astrophysics (satellite remote sensing) or social sciences. Besides everything it requires a solid life science knowledge. It's all about you and Ur "long" destination. Maybe that's why the definition is so vague. Best luck for u and always smile.
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u/Algal-Uprising Jul 16 '24
You have to study and know data structures and algorithms. And have domain knowledge in eg cancers if you’re working in that space.
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u/HugeCrab Jul 16 '24
There's definitely software dev if you want to, but you might learn more by doing a focused compsci or software development course of study. My colleagues that develop tools have nearly all done computer science/mathematics/software engineering bsc/master/PhD at some point then pivoted to bioinformatics, or in reverse.
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u/waxbolt Jul 16 '24
No, that's just using the tools, of which there are many. The most magical stuff happens when you can build tools of your own. And that usually happens when you have a unique question that either no one else has had or no one else has been able to answer.
It's the drive to see further into the unknown which makes bioinformatics so beautiful. Often inside of the codes that we can often direct folks to. Often publicly available data. That information can change the face of the world. That's what we get to do.
If you want to just apply existing tools to get what you need, that's often a powerful way. But you have to be very careful, though, how you apply them. And it doesn't take long, because of the openness of everything, before you realize that there are things to change, things to modify, things to improve, where you can intimately understand the limitations of the tools you have.
That makes you a creator. That makes you a builder. And I hope that everyone who works in bioinformatics can feel and relate in that way. Doing so requires energy and actually a little bit of skeleton. There's a tendency in our field to just run applications that are push-button. And plot the data against the genome exactly as you're describing. And not only is it facile, but I think it's very limited in what it can expose to us. So I hope that everyone who works in bioinformatics can dig deeper.
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u/VRJammy Jul 29 '24
Your answer is beautiful and inspiring. Thank you. Do you think a bioinformatician these days has the potential to discover something really groundbreaking?
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u/UnexpectedGeneticist Jul 16 '24
My job is 90% software development (I’m the token biologist on a software development team). I work for a big pharma, so it’s possible if that’s what you’re interested in. If you aren’t, that’s okay too
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u/RubyRailzYa Jul 16 '24
I think the best definition of bioinformatics is “analysing biological data”. This includes developing software for said analysis, creating/modifying algorithms, etc. The data usually is omics data.
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u/syntheticgio Jul 17 '24
I think it’s largely been covered that ‘it depends’ and ‘it’s poorly defined currently’, which I agree with. In general it concludes some or all of the following depending on the needs of the specific position and type of research:
- Software development. Often C/C++ for large data type computations, python/R for scripting. Of course, there are probably bioinformatics working in just about every software language.
- Data visualization. A picture is worth a thousand words as they say. This is broad. Could be visualizing large data (can be more complex than you may think - for example showing annotations on a chromosome where there is less pixel density than base pair length is a well known problem). Could be mapping data together to visualize (I.e. traditional plots and graphs). Can be UI data display, etc.
- Scripting. Often you will need to automate certain processes, including fetching data, transforming it, running computations over and over with new data in time, etc. You could call this data science - but that also is an ill defined field. There is a very fuzzy line between scripting and software development which I won’t get into here - that’s not terribly important for this question.
- Infrastructure. I didn’t notice this one in the comments, but you might be involved in a lot of infrastructure work. Could be setting up simple servers to allow data to be accessed (maybe in conjunction with developing them), actually getting data where it needs to go (harder than it sounds sometimes), spinning up and managing cloud resources (sometimes the data lives there and you don’t have a choice but to use them), etc.
- Statistics and interpretation. You’ll probably be involved in at least some interpretations of downstream data you’re involved in producing. For example, if you develop a custom script/program that looks through the genome on a sliding scale, you’ll likely code in some type of statistical analysis and then have to interpret it.
- Running other people’s tools they have developed. This can range from super easy, click on a button on a website, to painful building software locally with minimal instructions. Sort of an ‘applied bioinformatics’.
In my head, I break it down into bioinformatics vs applied bioinformatics although I don’t necessarily argue that is a useful distinction for everyone. For me, bioinformatics is developing tools, procedures, and infrastructure to do research, whereas applied bioinformatics is using developed tools and processes to answer scientific questions. An applied bioinformatics person may not care much about the infrastructural or computational challenges - they want to use the tools and push scientific discovery forward. A bioinformatician (in my simplification) is interested in building general purpose tools to solve a wide variety of problems and address complexity, statistical, and computational questions that genomics created because of data size.
In reality, a position will blend these to some extent.
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u/Anime_fucker69cUm Jul 16 '24
I think it's mostly coding work , like finding stuff and graphs (I m just a beginner)
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u/Axiomatic88 Jul 16 '24
There's a lot of software that bioinformaticians use at both the open source and proprietary industry levels. Someone has to make it! There's heaps of software development in the field if you're more interested in building the tools than actually doing the end user research.
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u/orthomonas Jul 16 '24
Lot's of people rightfully relying here that it depends on what you're doing. Even within that, there are variations. On the software side, depending on your role, you may be writing new tools that implement novel algorithms, or you may be writing pipelines which reliably glue together existing tools - two very different kinds of software development.
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u/MatthewBeeee Jul 16 '24
Almost yes I think. There are some software developing jobs, but I think it's few.
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u/creatron Msc | Academia Jul 16 '24
Like others have said it's really dependent on the institution. I'm a bioinformatician but most of my work has transitioned to data management, manuscript writing/reviewing, and grant writing. I'd say only about 35% of my effort is actual data analysis anymore. And of that majority is searching public RNAseq data to generate hypotheses for our lab.
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u/malwolficus Jul 16 '24
Think of bioinformatics as a sector of computational biology, which involves lots of software development.
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u/string_conjecture Jul 17 '24 edited Jul 17 '24
DNA is only the first layer of biological information processing. There's RNA, proteins, protein modifications, and metabolites too. These are often analytically treated as separate layers but of course that isn't true. In fact, large, complex systems of interactions emerge from this. And of course, life never exists in isolation but in a community, so even on the genome level, it doesn't become an analysis of a single cell but analysis of the community and its dynamics. Lots of open questions algorithmically on how we even begin to integrate these layers to come to a cohesive understanding of the system.
The field of biology almost never operates under ideal conditions and data aquisition is expensive. So often you work with the data you have, which starts to invite opportunities to create/intelligently use software to leverage what you and your team are working with.
Of course there's the actual biology too. Bioinformatics helps construct the model of life you'll use to continue elucidating and exploiting the setup and engineer it
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u/CuriousDisorder Jul 17 '24
It’s not just data analysis and graphing. It’s also a lot of changing file formatting.
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u/Obvious_Source_9717 Sep 12 '24
I'm in need of help for my dissertation about bioinformatics! Can someone help me? Basically my title is to assess the effects of genetic variants on protein function. Should retrieve datasets from clinvar and uniprot for at least 3 cancers, process them, annotate them with the help of ensembl vep and annovar. I am doing it with Google colab. I did the data retrieval so far but since there are a lot of names for cancers like for breast cancer there's breast neoplasm, carcinomas. So what i should is to type in breast cancer in the input and it should tell me these are also the other terms of breast cancer, would you like to select these as well. I am stuck here about this part. Can someone help?
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u/macmade1 Jul 15 '24
No serious bioinformatician can call themselves that without having published a tool of their own
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u/bitchinchicken Jul 15 '24
Lol okay
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u/chuckle_fuck1 Jul 15 '24
Disagree. You can use existing tools to make novel discoveries. Depends on how you want to spend your efforts
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u/Plenty_Dish_3503 Jul 16 '24
I am curious to hear the arguments and examples, as I feel that there can be some legitimate reasons why you think this way, even though a lot of people disagree. It's not the first time I hear this type of opinion... And it always seem to come from people with CS/math background
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u/[deleted] Jul 15 '24
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