Dependency hell is real

Left side is programmer bros coming in to the field, and the right side is those of us who spend large portions of our time conforming to file formats lol

I figure many of us are having to work from home for a while. For those of you who can’t bring your experiments home, this is a great time to learn a little about computational biology, data analysis and visualization!

To help some of you, I’ve made a list of freely available resources that have helped me transition from the wet lab to the dry lab. Feel free to add to this list in the comments and if I missed anything that you are interested in, let me know and I'll add it.

If you have the cash to take paid online courses, the resources at Lynda, Coursera, Udemy are also great options. These tend to be better simply because they are designed as a coherent curriculum. However, there is no more information in those courses than what is freely available online.

I'm SCARED!

Don't be! Yes, algorithmic bioinformatics is intimidating, but there is a whole world of computational biology that doesn’t require a lot of knowledge in computer science. I’m a former wet-lab rat who transitioned to 75% dry-lab over the last few years and I can say these next few weeks is the perfect amount of time to get a basic understanding that will allow you to integrate these tools into your research.

Learning the basics of R

I personally believe R is the best language for wet lab biologists who want to get into data analysis. The numerous libraries available and accessible UI console (Rstudio) make it much more approachable than python. I also use python and can add some info if anyone specifically wants to learn it, but for the beginner biologist who is language agnostic, R is a great place to start.

R tutorials for biologists:

1. datacamp has some very basic and advanced tutorials that will walk you through installing R, setting up your environment, managing libraries, etc.

2. Swirl is an R library that provides tutorials on basic R syntax and statistical testing directly within the R environment. This is how I first learned the basics. start here!

3. Datamentor These written tutorials give a more in-depth description of the data structures and syntax of R. It is great for people who have some limited programming experience and as a companion to other tutorials.

4. MarinStatsLecture is a youtube channel with hours of videos providing tutorials on everything from study design to plotting figures.

5. BioConductor offers a huge list of resources (videos, github repos, slides, and books) that focus on using R for real biological data. This is a great resource for learning to use R for your specific niche topic.

6. Rmarkdown notebooks. lab notebooks are also important in computational biology. Rmarkdown notebooks are an easy way to log your code, plot figures, and export as a PDF. This is a good tutorial to get you started with notebooks.

Example biological datasets to help you begin exploring

Of course, learning on your own data is a productive option, but sometimes cleaning and loading data is a major hurdle. Luckily, R has a bunch of example datasets built in. Many of these are biological including elisa data of DNase, biochemical oxygen demand, growth patterns of orange trees.

In addition, the R bioinformatics suite Bioconductor has many more realistic and domain-specific datasets available from their website. e.g. NGS data, drug screens, microarrays.

Learning the basics of command line:

Not everything requires programming. Much of bioinformatics involves using software/packages that are executed on the command line. Executing these software requires a little bit of knowledge on the command line. starting with the basics (changing directories, seeing files) to more advanced shell scripts that can help automate your workflow and improve compute efficiency.

Command line / shell tutorials for biologists:

1. The 8 most useful shell commands for data science

2. Beginners guide to the bash terminal is a video where someone walks you through navigating the command line.

3. Bioinformatics 101 by Hadrien Gourle is a great place to learn about the command line and about various file formats and programs used in NGS analysis.

4. Exercises for NGS data processing by Umer Zeeshan Ijaz also NGS focused but provides some helpful tutorials that will be helpful to any domain

Data visualization and making figures

I imagine many people's interest in computer stuff ends at making beautiful figures. There are many ways to do this in most languages. I do most of my figure generation within the Rstudio IDE.

1. Fundamentals of Data Visualization by Claus O. Wilke is a fantastic resource for properly visualizing quantitative information. In addition to the book, he published a github repoof all figures written in R.

2. Columbia's intro to Data Visualization is the course page of a class taught by Agnes Chang. All slides and readings are feely available. Some advanced visualizations are programmed in D3.js

3. Tutorial of plotting with ggplot2 in R. I could have listed this in the R section as it provides some basic R tutorials. However, this provides all you need to start using ggplot2 to make beautiful figures, without the burden of details in the R tutorials listed above. ggplot is my favorite way of making quick, beautiful graphs.

I'm happy to take requests and answer questions. And please add to this list if you can!

Hi r/bioinformatics

I've been in industry for just over 10 years now, working mainly in precision medicine and biomarker discovery.

This is mainly related to the career advice related threads that pop up. There are clearly many people who want to make a living doing this and I've seen some great advice given.

What is often missing from the conversation is the context of bioinformatics as an industry. Industrial bioinformatics is, as a concept, essentially non-existent. There are pockets of it happening here and there, but almost all commercial bioinformatics has an academic approach to their work.

Why this is important?:

The need for bioinformatics is huge, but we are not trained to meet that need in ways that work for corporates. In our training we are scientists but industry needs us to be engineers. We can't do much about the training available at universities right now but I would urge new bioinformaticians to educate themselves on engineering principles like LEAN and TPS, explore how software development actually gets done, learn good fundamentals around documentation and git. Learn the skills necessary to make your work consistent, repeatable and auditable.

I'd be really interested what those of you with time in industry think. Have you had similar experiences with the needs within organisations? What has it been like building this plane as we try to land it? And what do you think new bioinformaticians should focus on besides their academic work?

PhD in Bioinformatics, and MS in Biostatistics. In the last year, I have applied to hundreds and hundreds of jobs broadly across academia and industry, and have not been able to land a single opportunity.

On a personal note, I am truly devastated that all the work of graduate school has amounted to nothing, and worse, has become a source of deep regret and humiliation to myself and my family. I have had to find any possible way to support myself and my family and this is not what I expected to find after working hard all these years. If I could “leave a review” of my PhD… it would unfortunately not be good.

I don't know how common this feeling is. I was tasked with analyzing RNA-seq data from relatively obscure samples, 5 in total from different patients. It is a poorly studied sample–not much was known about it. It was an expensive experiment and I was excited to work with the data.

There is an explicit expectation to spin this data into a high-impact paper. But I simply don't see how! I feel like I can't ask any specific questions about anything. There is just so much variation in expression between the samples, and n=5 is not enough to discern a meaningful pattern between them. I can't combine them either because of batch effects. And yet, out of all these pathways and genes that are "significantly enriched"–which vary wildly by samples that are supposed to pass as replicates, I have to find certain genes which are "important".

"Important" for what? The experiment was not conducted with any more specific question in mind. It feels like they just generated the data because they could and thought that an analyst could mine all the gold that they are sure is in there. As the basis for further study, I feel like I am setting up for a wild goose chase which will ultimately lead to wasted time and money.

Do you ever feel this way? I am not super experienced (1 year) but feel like a research astrologer sometimes.

as several users have repeatedly proposed:

How about we just ban most of the career related questions and make a detailed FAQ instead?

No.

There are a few things that need to be said, which I feel are worth repeating.

1. There is no such thing as a FAQ that answers every question. Most of the questions we get here are from people who are unable to find the answers they're seeing because someone's FAQ isn't up to date, or the FAQ for a piece of software is missing a detail. We will never be able to make or maintain a FAQ that deals with everyone's questions.

I have a blog that goes back to 2009, which has dealt with most of these questions, but every single person who asks a question asks because they think they have a unique circumstance that doesn't quite fit with whatever FAQ they've found - and at least a significant portion of them do. (There are, of course, a few lazy questions, but it only takes one person to reply to them and let them know that the internet exists, and they should probably just use google..)

If you think that all of their questions look identical, perhaps you've not invested the time into reading them and understanding why each one is slightly different from the submitters perspective.

2) Bioinformatics is a career in which people are mainly computer oriented, and thus look to online communities and/or support. Our success as a subreddit is because our users ARE all heavily invested in technology and the internet. /u/microbiology is not a great comparison because the vast majority of microbiologists have non-online support networks. They have text books of SOPs, they work in large facilities where they can gather at lunch time and share questions - they have entire departments dedicated to the microbiology where people can and do collaborate.

In contrast, bioinformaticians are harder to find, come from a wide variety of fields and often have very little in common, even if there are a handful of them in the same department. Online communities are about the only exposure many undergrads ever get to the field. r/bioinformatics is really the only place that many undergrads will be able to find a practicing bioinformatician where they can ask questions.

We ARE the bioinformatics community, as far as most undergrads go, even if only a fraction of practicing bioinformaticians hang out here. Can anyone suggest a better forum to find a live bioinformatician where you can ask questions?

3) WTF, why on earth would we ban undergrads or others from asking career related questions?

Who did you ask, when you were getting in to the field? I remember talking to profs about the area because I was lucky enough that there were two of them on campus when I was an undergrad. I didn't even know the field existed, and was fortunate that one of them knew what the word was to describe the field so that I could call it something other than "computers and biology". Those two professors were unbelievably kind and humoured my questions, let me invent my very own courses to learn the subject, and even graded an entire thesis on the subject when there were maybe 100 people in the field in the entire world. Back then, a bioinformatician might well not even called a Bioinformatician. They were just biologists playing with computers.

As far as I'm concerned, it IS OUR JOB as bioinformaticians to encourage undergraduates to ask us questions about the field, and to take the time to give back to them as our mentors gave to us.

If there are those among us who can't take the time to give back on the career advice threads, I don't have much sympathy for them demanding that I take down those very posts that they should be giving the most attention. Yes, you can skip over them, if you feel that your time is too important, but those posts are written by the next generation of bioinformaticians, and they deserve our time and our effort.

I don't have much sympathy for those for whom mentoring is just too much of a burden that they can't even skip the posts they don't want to read.

tldr: No, I will not ban career questions.

Hi All,

Here to vent. I cannot get over how two years ago when I entered my Master’s program the landscape was so different.

You used to find dozens of entry level bioinformatics positions doing normal pipeline development and data analysis. Building out Genomics pipelines, Transcriptomics pipelines, etc.

Now, you see one a week if you look in five different cities. Now, all you see is “Senior Bioinformatician,” with almost exclusively mention of “four or more years of machine learning, AI integration and development.”

These people think they are going to create an AI to solve Alzheimer’s or cancer, but we still don’t even have AI that can build an end to end genomics pipeline that isn’t broken or in need of debugging.

Has anyone ever actually tried using the commercially available AI to create bioinformatics pipelines? It’s always broken, it’s always in need of actual debugging, they almost always produce nonsense results that require further investigation.

I am sorry, but these companies are going to discourage an entire generation of bioinformaticians to give up with this Hail Mary approach to software development. It’s disgusting.

It's 2025. LLMs have been around a couple of years, but so far it's been mostly a novelty to me, I still do all my research and code manually, preferring to use stackoverflow or biostars for coding help, and google scholar for looking up research papers. However, I recognized the growing utility of LLMs and how much faster they could code new scripts than me in some cases, so I got a Clade subscription. Useful in some cases, not so much in others, but that new research tool sure is handy to comb through hundreds of papers at the same time...
May 2025. A new experimental tool comes out: Claude Code. I see it's potential immediately and boy, am I excited when I see how much it can do! "This could make my PhD go so much faster!" I think, especially with all the new experimental analyses that my PI is asking me to do.
The months go by and I think my PI has noticed that my productivity has increased because he starts giving me more and more stuff to do. It's OK, I can handle it - Claude Code is helping me keep up with the workload. I start noticing, though, that the couple of times that I needed or wanted to write a script manually that I'm having trouble remembering how to do things - and why bother remembering how to do that one particular bit of fasta file I/O, when Claude Code can do it so quickly and elegantly instead?
My debugging skills are still sharp - Claude often gets stuck on these esoteric bioinformatics pipelines, so I've still had to step in and stop it from spiraling into an endless debugging loop. But as the months keep flying by and as I keep trying to go back to writing code from scratch, I feel stuck, like I'm in a writer's block. It seems like I can't even remember basic syntax anymore.
Fast forward to 2026, and my PI gives me 4-5 new analyses to try every week. There was one week where he even gave me 10+ impossibly long things to try it's the first time I've ever had a heated argument with him. I'm struggling to keep up, but it's my 5th year of my PhD and I desperately need to graduate so I just keep working as hard as I can, Claude can help me stay afloat....
Except that now I'm realizing that I've let my raw coding ability become far too rusty. I can't be bothered to create even the most basic commands - why bother looking up how to input all those parameters when Claude can read the relevant files and format everything correctly in just a few seconds? Besides, If I start trying to do things from scratch again I won't be able to keep up with my increased workload.

I keep on going but I'm feeling kind of miserable. And then I realize it. I'm not actually enjoying running these analyses anymore. The simple joy of solving a difficult bioinformatics problem on your own is gone. I no longer write up complex pipelines from start to finish and get to see the rewards of my hard work - Claude just does everything, and what I've become is a garbage sorter - sorting through Claude's endless outputs and separating the good from the bad. On top of that, I keep churning out analysis after analysis to satisfy my PI's insatiable hunger for novel insights on the same datasets I've been working on since 2022. Even If I wanted to slow down and try to work through the code myself, I can't anymore - my PI is used to receiving new results just as quickly as I am used to getting fast responses from Claude, and If I can't deliver, my PI will become unsatisfied with my performance. There's a lot of stress on his shoulders as well as our lab has been struggling for funding and he's been writing many grants with my experimental analyses.

I am worried for when I finally graduate and it's time to apply for jobs in the industry - I've been seeing the posts about the state of the economy and the job market, especially in our field. I use to pride myself in my coding ability. It's what use to set me apart from everyone else in my lab and my department, but now it seems like the great equalizer has arrived, where everyone with a rudimentary understanding of the pipelines can work through them given enough prompting - Claude Code is improving every month!
I don't have my expert coding ability anymore, and scientists everywhere are struggling to find work; is there anything left that will set me apart in this competitive market? I doubt I could answer technical coding interviews at this point. Even if I get a job, Is a life of endless prompting and garbage sorting what awaits me?

I'm curious to know if anyone in here has had similar experiences or if their experience has been different from my own. I know that technology is always bound to evolve and change, but I want to know what kind of future I should be preparing myself for. Claude Code has completely changed how my PhD feels in less than a year.

Research support staff here just venting, but it feels like I’m constantly being asked to take a crappy dataset produced from a flawed experimental design and generate publication worthy results.

Even just basic stuff like trying to explain that there is a massive amount of contamination that makes analysis almost impossible and even if things run we can’t trust the answers that we get are met with blank stares that say “you’re the computer guy just make it happen.” Or another favorite is when a treatment variable and a technical covariate are perfectly confounded and when I’m presenting the issues with the design the PI says “well can’t we just ignore the technical variation and focus on our hypothesis?”

I just have no idea how so many labs justify spending thousands of dollars and hundreds of man hours on sequencing experiments that they have no idea how to analyze or even plan with no prior consultation. And then when I have to break the bad news that there’s hardly anything we can actually learn from the data because of fundamental errors they refuse to listen or consider adding some more replicates to disambiguate the results.

Bioinformaticians in public health are basically the tech support that no one asked for but everyone desperately needs. They’ll spend weeks crunching data and running complex algorithms only to come back with results that are 95% confidence interval for “We have no idea what’s going on.” They’ll hoard gigabytes of sequence data like it’s Pokémon cards, but ask them to explain their methods in plain English, and you’ll get a lecture that makes quantum physics sound like kindergarten math.

They act like they’re saving the world, but half the time, they’re just arguing over which alignment tool is slightly less terrible than the others. They’ll complain that epidemiologists “don’t get it,” but try to ask them a straightforward question, and they’ll start spouting jargon like they’re auditioning for a role as the Riddler in the next Batman movie. Their obsession with precision would be admirable if it didn’t result in them re-running analyses ten times because the p-value was 0.05001 instead of 0.05.

And let’s talk about their so-called “pipelines”—it’s like they built the most convoluted Rube Goldberg machine just to sort through a pile of data and find the same old stuff everyone already knew. But heaven forbid you suggest simplifying anything; they’ll act like you just proposed burning down the library of Alexandria. They’re so deep in the weeds with their scripts and code that they forget the whole point is to actually help people, not just generate pretty heatmaps to flex on Twitter.

Oh, and good luck getting them to finish anything on time. They’ll tell you the pipeline will be ready in a week, and three months later, they’re still “optimizing” it. Meanwhile, the public health crisis they were supposed to be tackling has come and gone. But sure, tell us more about how you’re planning to make your next Snakemake pipeline even more unreadable.

Blog : https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/

Server: https://golgi.sandbox.google.com/about

Paper: https://www.nature.com/articles/s41586-024-07487-w

"we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture, which is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues"

the possibilities are endless

Too excited to see how it will change the structural bioinformatics

There are a lot of posts fearfully adressing the relevance of studying and working with bioinformatics in a world of rapidly advancing AI. I thought I would give my thoughts as a senior scientist/professor, and hopefully have others pitch in on as well.

Firstly, let me set up the framework of what I believe is an archetypical bioinformatician - admittedly heavily inspired by myself, but if and when you disagree, set up your own archetype and lets discuss from there.

They studied biology/biotechnology/medicine in their undergrad, perhaps dappling in a bit of coding here and there, but were fundamentally biologist. As graduate students - MSc and/or PhD - they developed an affinity for the data science aspect of things, and likely learned that coding could accelerate their research quite a bit. Probably took a course or two on formal programming. They quickly learned that their talent for coding gave them an advantage in their scientific environment, and hence increasingly shifted their focused on it. They likely developed their coding skills on their own rather than formal training, and were probably the best - or only - bioinformatician around. Eventually, this person is now a biologist, capable of coding their way out of most problems by scripting pipelines with various prebuilt tools, and summarize the output in pretty figures.

We now have a person who understands biology and a understanding of data science sufficient to produce great science.

Compared to a real software engineer or a true data scientist, however, they suck. Their pipelines fail the second they are deployed to a server, the software is impossible to maintain and the algorithms are hopelessly inefficient. Seeing a software engineer fix such a pipeline is truly remarkable.

Then comes the LLMs - their coding abilities are miles beyond what most of us can do already, and they can do it in seconds. When it comes to coding, we have already lost the competition long ago.

Here is the kick: I don't think we should be competing with the LLMs at all. As a matter of fact, I think we should let them do the coding as much as we can - they are much better at it, they are mindblowingly faster and they make code that can actually be read and maintained.

So what is our role in this era? We go back to our roots. We are biologists that use computation to answer our questions, and just like the original computers increased our productivity exponentially by letting us skip the tedious tasks of manual labour, the LLMs will do the same.

Our responsibility is - at this point - is to have exceptional domain knowledge of our biology and extreme skepticism of the LLM outputs in order to produce the best science.

So if you wish to enter bioinformatics from a coding background, you probably shouldn't. A very important exception, however, is for those of you that are exceptional coders - we need you to make the assemblers, mappers, analyzers and statistical software that this whole field of ours is build on, although my experience tells me that you guys come from physics, maths and software engineering in the first place.

Provocative, I know - let me hear your thoughts.

EDIT: Happy to see a lot of opinions in the comments. As might be apparent in my own comments, this is not something I ham happy about, but rather find to be an unfortunate but inevitable consequence of the progress in AI. As a researcher and educator, I try my best to adapt to the changing landscape and this post is a reflection of my current thinking, although I am exited to be proven wrong.

I’ve been in the field for 16 years.

New technological developments are inherent in all science, and are arguably the most exciting part! But over the last year, the rapid onset of LLM use has become totally unavoidable. What began as “hey this is actually useful” has ended up feeling like “I spend my whole day managing an orchestrator agent that handles context continuity for a bunch of subagents doing the work that I used to love doing, or otherwise correcting slop code that works I guess but I hate looking at”.

Yes, it is possible to operate in this world without LLMs, but it feels like employer expectations have ballooned along with this tech, and now I’m expected to produce in a day what used to take a week or more of focused and mindful development. The pressure to keep up with people who actually know how to use these tools (I count myself in this group) is too high. But I hate it. It’s the worst part of being in a managerial position, all of the oversight and correction, none of the social aspect or financial benefit. And I feel increasingly removed from the science.

I guess I’m writing this because I wonder if anyone else in here feels the same way. This kind of work is solitary enough as it is.

Thanks for coming to my TED talk.

Not talking about whether the biology was right. Just: could I run this on a different machine and get the same result? Could I run it in 2 years?
No container. No version pinning. Conda environment.yml with numpy and no version specified. Reference genome downloaded manually, path hardcoded. Sample sheet generated by a script that no longer exists.
We talk about reproducibility constantly in this field. Papers about it. Talks about it. And then the actual pipelines look like this.
Not a rant, but genuinely curious what people think the root cause is. Time pressure? Nobody teaching this? Reviewers not caring?

Just a heads up that Ginkgo Bioworks has just released four huge new datasets in functional genomics and antibody developability on Hugging Face.

In particular, there are:

-Thousands of chemical perturbation conditions across diverse human cell types

• Dose–response and time-course gene expression & imaging data

• Biophysical developability profiles for hundreds of IgG antibodies, with matched sequence data

They are going to keep adding data and there will also be a challenge announced soon.

Recommend checking it out!

Data: https://huggingface.co/ginkgo-datapoints Blog: https://huggingface.co/blog/cgeorgiaw/gdp

Before you post to this subreddit, we strongly encourage you to check out the FAQ.

Questions like, "How do I become a bioinformatician?", "what programming language should I learn?" and "Do I need a PhD?" are all answered there - along with many more relevant questions. If your question duplicates something in the FAQ, it will be removed.

If you still have a question, please check if it is one of the following. If it is, please don't post it.

What laptop should I buy?

Actually, it doesn't matter. Most people use their laptop to develop code, and any heavy lifting will be done on a server or on the cloud. Please talk to your peers in your lab about how they develop and run code, as they likely already have a solid workflow.

What courses should I take?

We can't answer this for you - no one knows what skills you'll need in the future, and we can't tell you where your career will go. There's no such thing as "taking the wrong course" - you're just learning a skill you may or may not put to use, and only you can control the twists and turns your path will follow.

Am I competitive for a given academic program?

There is no way we can tell you that - the only way to find out is to apply. So... go apply. If we say Yes, there's still no way to know if you'll get in. If we say no, then you might not apply and you'll miss out on some great advisor thinking your skill set is the perfect fit for their lab. Stop asking, and try to get in! (good luck with your application, btw.)

Can I intern with you?

I have, myself, hired an intern from reddit - but it wasn't because they posted that they were looking for a position. It was because they responded to a post where I announced I was looking for an intern. This subreddit isn't the place to advertise yourself. There are literally hundreds of students looking for internships for every open position, and they just clog up the community.

Please rank grad schools/universities for me!

Hey, we get it - you want us to tell you where you'll get the best education. However, that's not how it works. Grad school depends more on who your supervisor is than the name of the university. While that may not be how it goes for an MBA, it definitely is for Bioinformatics. We really can't tell you which university is better, because there's no "better". Pick the lab in which you want to study and where you'll get the best support.

If you're an undergrad, then it really isn't a bid deal which university you pick. Bioinformatics usually requires a masters or PhD to be successful in the field. See both the FAQ, as well as what is written above.

How do I get a job in Bioinformatics?

If you're asking this, you haven't yet checked out our three part series in the side bar:

• part 1

• part 2

• part 3

What should I do?

Actually, these questions are generally ok - but only if you give enough information to make it worthwhile. No one is in your shoes, and no one can help you if you haven't given enough background to explain your situation. Posts without sufficient background information in them will be removed.

Help Me!

If you're looking for help, make sure your title reflects the question you're asking for help on. You won't get the right people looking, and the only person who clicks on random posts with un-related topic are the mods... so that we can remove them.

Job Posts

If you're planning on posting a job, please make sure that employer is clear (recruiting agencies are not acceptable, unless they're hiring directly.), The job description must also be complete so that the requirements for the position are easily identifiable and the responsibilities are clear. We also do not allow posts for work "on spec" or competitions.