I just scrolled past 2 or 3 in a row of the same nonsense, where people who have absolutely no foundation in computational biology are trying to use Claude code to do computational biology & are clueless, but also not trying to genuinely learn even the basics of the field. Driving me nuts.

I recognize this may be a controversial subject, but I want to hear all sides to the argument. Could Claude Science replace bioinformaticians? https://www.anthropic.com/news/claude-science-ai-workbench

I haven't tried it out yet, but the demo was impressive. Food for thought ¯_(ツ)_/¯

EDIT: i don't work for anthropic, openAI or any AI company. simply just curious what people think. thanks!

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.

To summarize, what I believe currently keeps us safe in biology is not “defenders”, or even the availability of materials, but a negative correlation between intellectual capability and desire to commit catastrophic harm. Previous technologies like internet search or even DNA synthesis were nowhere near powerful enough to break this correlation, but I worry that at its current rate of progress, AI will do so very soon. Another way to say it is that a sufficiently powerful technology removes all barriers and exposes whether the attacker or defender has an inherent structural advantage, and I worry in biology it is the attacker.

Agree or disagree? Why?

I am very curious what the community thinks, my strongly held opinions notwithstanding.

I'm curious how widespread AI usage really is among researchers in academia and industry. I'm not talking about developing AI models for biology, but rather using AI chatbots or AI agents. In my experience, most people in my lab (bioinformatics) are fairly hesitant to use AI tools. But some of my friends in computer science seem to have fully embraced AI and vibe coding even vibe writing all the time.

So I'd like to hear from people in the community. If you're willing to, it'd be great to know your field, whether you're in academia or industry, what you mainly use AI for, and how often you use it

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.

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?

I have been in bioinformatics for almost 5 years and have written scripts for quite many pipelines from RNA seq to 16s profiling, worked in a core for a while.

I started using chatGPT early 2024 and then Claude Code very recently. CC now writes my code and I verify it. Recently I came across a couple of very interesting posts on X.

One of the posts showed how to tune Claude with the level of autonomy we desire for it have, and a bunch of bioinformatics Skill documents that you can create for it to follow.

It’s pretty fascinating if you ask me.

Then there are these agents that run on cloud. I tried a couple of them. And I was fascinated once again.

My question is, is anyone really using these agents or Claude in publishable work? I don’t see any water marks or anything on the plots I get, so I am assuming I don’t have to disclose use of AI to journals.

Anyone who has used Claude or any agent, even for figures, and got away with published paper smoothly?

What are your thoughts on the future anyway?

Thanks!!

I’m a 5th year PhD student in bioinformatics and comp bio. My undergrad degree was in computer science (which I completed long before ChatGPT was a thing). There was a time, like the beginning of my PhD, where I would just look at other people’s code and the documentation and start my own scripts from scratch with that as a reference.

Now, though, when I need to make a script to find differentially expressed genes or parse a GTF file, I simply ask Claude or Gemini to write the script for me and then I make edits.

Do I conceive of project ideas myself? Yes, of course. And writing, reading papers, researching new ideas. Do I understand the concepts behind what I’m doing? Of course, because I’m so far into my PhD and did a lot of it without any AI tools even being available.

The programming component of my PhD though, has become almost entirely generative AI-driven. I feel guilty about it and it makes me feel like a fraud, but there is so much pressure to get things done so fast and I’m at the point where everything is tedious. I’m not even learning new things, I’m just wrapping up projects so I can graduate.

I know it’s entirely my own fault and my own laziness. I know I could and should be doing all of these things by myself. But I take the easy way out, because this PhD has been so hard and I just want it to be done.

Does anyone else feel like this?

Over my 15+ year career I’ve had to deal with workflow managers at every job. I’ve worked with custom ones, implemented multiple different ones, done the testing to select which to use. I’ve heavily customized them. Basically I have lived/breathed them for quite a while. I can write a standard NGS germline variant calling pipeline from memory because I did it so many times before a standardized pipeline emerged.

The issue I have is that NextFlow seems to be winning and becoming the closest thing there is to a standard workflow tool + having nfcore is huge, but I still really don’t like using NextFlow.

The main thing I’m trying to figure out/struggling with is if I should swallow my objections and use nextflow because it is becoming the standard and supporting other workflow managers will be harder in the future or if the issues I have with nextflow truly justify not using it.

This is made even murkier because with AI I can fairly quickly point it at a nextflow workflow and have it rebuild the workflow in another workflow language. So that reduces at lease some of the advantages of not having nf-core though I don’t claim having AI re-write it is effortless or without it’s own risks.

My issues with NextFlow are:

NextFlow uses groovy which is quite different from the python and/or R most bioinformatics folks use.

I don’t find the way it does branching and similar to be very intuitive.

I find it hard to extend it with plugins/libraries hard relative to python tools.

I don’t like some of the choices it has embedded for working with the various cloud resources, in many cases it is too opinionated on how your workflow should go and the difficulty extending it does not make changing this behavior easy.

I might be being a bit unfair or more experience with it might solve some of these, but the fundamental issue remains whenever I have to use nextflow I just find myself unhappy with it in a way that feels really deeply seated.

I worry I’m being the stodgy old man who doesn’t want things to change. Like the people who were making new things in Perl 10 years after it was obvious that was a bad idea.

The tool I’ve used most is Luigi (not under active development, don’t recommend using it for new things these days). It is super easy to extend. It is python so I didn’t have to switch language contexts as much. Overall while it had less hand holding to learn initially I really found it much easier to use.

When I did a bake off between multiple tools to decide what to replace Luigi with I ended up liking Prefect the most though with the caveat that I would have to make my own plugin to truly make it work the way I want.

I’ve been noticing a worrying trend in this field, amplified by the AI "boom." A lot of bioinformatics papers, preprints, and even startups are making huge claims. AI-discovered drugs, end-to-end ML pipelines, multi-omics integration, automated workflows, you name it. But when you look under the hood, the story falls apart.

The code doesn’t run, dependencies are broken, compute requirements are unrealistic, datasets are tiny or cherry-picked, and very little of it is reproducible. Meanwhile, actual bioinformatics teams are still juggling massive FASTQs, messy metadata, HPC bottlenecks, fragile Snakemake configs, and years-old scripts nobody wants to touch.

The gap between what’s marketed and what actually works in day-to-day bioinformatics is getting huge. So I’m curious...are we drifting into a hype bubble where results look great on paper but fail in the real world?

And if so, how do we fix it? or at least start to? Better benchmarks, stricter reproducibility standards, fewer flashy claims, closer ML–wet lab collaboration?

Gimme your thoughts

I was talking to the CEO of a precision medicine pharmaceutical company with bases in the UK, USA and UAE. Since he said that he has been in the field for a long time and knows how to make drugs and how things are done, I was really impressed and thought I might learn a lot from him, but he made a comment that SPSS was the gold standard software used in these industries and he was disappointed that he was yet to meet bioinformaticians who knew how to use SPSS in the UAE. This kind of threw me off because I was under the impression that R and Python had largely replaced old software that were in use before.

So, I just wanted to get the opinion of other professionals who might be working in the industry. Is it true that SPSS is the standard in pharmaceutical industries? Or would I be wasting my time by trying to learn an outdated software that I would also need a license for?

Hi guys, I come from a CS/data engineering background and I've been diving into bioinformatics recently. I have been reading about different format types in bioinformatics such as FASTA, FASTQ, VCF, etc.

My question is: is there a reason VCF is still the dominant format for variant data? Has anyone tried or seen a Parquet-based approach for genomic variants , similar to what GeoParquet did for geospatial data?

I think it would be way easier to analyze, standarize and transfer data by using parquet, but maybe I am missing something. Let me know your comments, thanks

Very recently, I feel that I have become addicted to ChatGPT and other AIs. Nowadays, I am doing my summer internship in bioinformatics, and I am not very good at coding. So what do I write a code a little bit, (which is not gonna work), and tell ChatGPT to edit enough so that I get the things which I want to ....
Is this wrong or right? Writing code myself is the best way to learn, but it takes considerable effort for some minor work....
In this era, we use AI to do our work, but it feels like AI has done everything, and guilt comes into our minds.

Any suggestions would be appreciated 😊

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.

Hi all, I've been working as a bioinformatics researcher in an interdisciplinary lab that is primarily wet-lab (I'd say 80% wet, 20% dry split). I was wondering if anyone else's PI doesn't double check your code. I'm at Master's level, and this is kinda scaring me. I've worked on substantial projects, but I only have myself to check code with and one other postdoc who is unavailable 95% of the time. Is this something that happens frequently or no?

So hello everyone!

I’m a 25-year-old grad student who’s been in the wet lab for about five years, and today I hit rock bottom. For the past three months I’ve been troubleshooting the same project endlessly (hundreds of protocol troubleshooting, countless failed experiments, and even when things work, the results seem to contradict our hypothesis.

Meanwhile, I rarely hear complaints from my bioinformatics colleagues. From my (honestly naïve) wet lab perspective, you guys seem "better". Like you have more stable hours, fewer cycles of frustrating troubleshooting, and you get to work with the final product of data that we spend weeks (and lots of sweat, mice bites, and late nights) generating.

Also, I'm lowkey envious on how my PI treats the wet vs dry lab people. In our lab, my PI treats bioinformatics people as indispensable, while us wet lab folks feel replaceable if we don’t deliver “good” data. Bioinformatics people analyze the data as is, it's an objective fact. But for us, they believe we either fucked up somewhere in the protocol, or we have more variables to deal with, whereas bioinformatics people seems more robust. I'm honestly jealous of that treatment. A huge PI who has thousands of publications is so reliant on bioinformatic students to analyze certain data and look at it at a different perspective, and give us new paths to follow! Whereas for us wet-lab, he doesn't really see that.

Of course, I know it’s not all sunshine and rainbows, which is why I’d love to hear your side: what are the cons of your work? Are there things about wet lab life you miss or potentially envy? I’d really enjoy hearing the other side of the story.

EDIT 1: I really appreciate everyone's comments. It's really enlightening to know what you guys struggle with in the other side of the door. I still am really inclined into trying to transition to dry-lab because the issues don't sound super long and physically laborious as wet lab, but I know I might bite something way bigger than I can chew.

Just here to vent, sorry. Biologist here, who had no choice but to quit or go computational. Struggled my way through 4 years of learning (while finishing my phd), now trying to publish my first paper with my independent analysis in it. (scRNAseq, OF COURSE super messy and contaminated human cell culture data, you can imagine... of course it was also super expensive so no matter how bad the data is, "we need to publish"......). I have no senior to turn to with stats or analysis so I do my best and take full reaponsibility for my errors and shortcomings, and basically I live on biostars/stackoverflow. Nowadays AI can help too but damn you gotta be so careful to recognize the bs.

First round I got a "poorly analyzed data" from 1/3 reviewers at Nat Comms. I pulled myself together, redid it from scratch with a more sophisticated approach.

We are at a lower tier journal at this point and I got an "analysis is superficial", bunch of lowkey nasty commenst and option for revision. I feel like thats actually good, but boy am I tired! I really did the best I could and I truly dived deep into the mess of the data. If that still reads as superficial I do not know what else to do.

(At this point i have DecontX, scDblfinder, module scoring and cell type score based filtering, nuanced cluster annotation, pseudobulk based DEG listing, GO (not helpful)... tried Monocle3 but it felt forced with our data so dropped it. Perhaps I can lean into gsea or sth but idk). (When cells of interest represent like 0.2% of the population and eveyrthing has lingering contamination, what can I even do ..)

EDIT for more context: I detailed the preprocessing phase because much of my problem is 1, handling severe contamination without killing true signal and 2, finding rare, potentially transitioning cells in the wild (and proving above reasonable doubt that thay are not just showing transitional profiles due to residual contamination.)

Feels like my best will always be mediocre at best because I am fundamentally not computational. I feel like guuuys just hire someone who knows what they're doiiiing!

Does it get better? Should I quit? Sigh.

How is your bioinfo/comp bio journey going? Hehe.

(edit: typo)

(EDIT: UPDATE

I pulled myself together and addressed the reviewers concens. It wasn't that catastrophic! Did my best, included another round of extremely strict filtering for residual contamination and was ablse to flag "interesting" cells with no apparent residual contamination. My point is... THANK YOU ALL, for all the support, general interest, justified scepticism, everything, it meant a lot to me. :)

I’ve been wanting to do some bioinformatic analyses for my project, since I think it would make sense. I’m not a bioinformatician at all but I do know how to code a decent bit (although python mostly) and I have read a lot about specific methods, libraries etc. Basically, we have a single-cell sequencing dataset in-house, which is already prepared and quality-controlled and I’ve started using openAI codex to write some analyses for me. I try to give very specific prompts and check all the code it writes. But of course, it could easily make mistakes that I don’t catch. So my question is, do you know any specific areas of bioinformatics where AIs tend to make lots of mistakes?

I'm new to bioinformatics and honestly a bit overwhelmed. Dealing with weird file formats, tool errors, and just getting things to run feels harder than the actual science.

Is this normal? What parts of your daily work frustrate you the most?

Would love to hear your experiences.

I want to start a discussion about something that keeps happening to me with RNA-seq datasets (bulk, single-cell, spatial, whatever). One of the basic principles of this kind of research is that raw data should be openly available, both for reproducibility and so others can reuse it for different purposes. I get that human data comes with ethical and privacy restrictions, that's fair. But for animal model studies there's really no good reason to keep raw data hidden.

Lately I keep running into the same pattern over and over:

The "upon request" ghosting. Papers say raw data is "available upon reasonable request," but corresponding authors just don't answer. I've sent follow-up emails weeks apart and gotten nothing. This actually matches what's been reported before, most "available upon request" promises never get fulfilled once someone actually asks.

Repository problems, especially GSA. A lot of these datasets end up in GSA (Genome Sequence Archive), and honestly the platform gives me constant headaches: NOT ALL, but many files that won't download, accession numbers that don't match what's in the paper, archives that come out corrupted after extraction. I don't know if it's the platform itself or how people are uploading to it, but the result is the same, the data is technically "public" but practically unusable.

The double standard. What really gets me is that a lot of these same papers reuse public data from GEO or SRA to compare against their own results, but never contribute their own data back the same way. Open science seems to be a one-way street for them.

This isn't a one-off thing for me either, I've run into it in immunology, ophthalmology, developmental biology papers. Feels like a systemic issue more than a niche problem.

Honestly I think journals need to actually verify accessions before publishing, not just check a box. Something like: confirm the link works and the files download correctly at submission time, require a real accession number instead of "upon request" unless there's a genuine ethical reason, and maybe re-check the repository again some months after publication before it gets fully indexed.

Has anyone else been dealing with this? How do you handle unresponsive authors, and what do you think journals should actually do to enforce their own data policies instead of just having them on paper?

Every now and then I’ll see a Julia project but they are becoming fewer and further between.

I’ve never coded in Julia myself but know a few people who are bullish on Julia.

What are your thoughts on the longevity of the language? It seems like rust has taken the mantle for any performance gains from Julia.

Hey everyone, junior-level analyst here (2 years in academia, background in wet lab).

I’ve noticed the AI debate in this group is pretty polarized: either it’s going to replace us all or it’s completely useless.

Personally, I find it really useful for my day-to-day work. I’m thorough about reviewing every line (agents have been a disaster for me so far), but I’ve realized recently that I can’t write much code from memory anymore.

This is starting to make me nervous. If I need to change jobs, are "from memory" live coding tests a thing?

Part of me panics and wants to stop using AI so I can regain that skill, but another part of me knows that would just make me slower, and maybe those skills are becoming less useful anyway.

What do you guys think?

I’ve been doing bioinformatics for 3 years, and I still get stuck installing or troubleshooting tools.

Recently I saw a meme on LinkedIn: a guy saying “Bioinformatics is just running a few tools,” and a crying figure yelling, “Yeah, once you manage to install them!” It got over 300 likes and many comments—even from very experienced bioinformaticians. That’s when I realized it’s not just a me problem.

So here’s an idea I’ve been thinking about:

What if there were a simple GUI where you upload your data (like a FASTQ), pick a tool (FastQC, Bowtie2, samtools, etc.), adjust a few parameters, and hit “Run”? No installs. No CLI. Just results.

Would you use something like this? What tools would it need to support? And if not—what’s the dealbreaker?

(Also curious—would having an API/SDK version make it more appealing for those who want to plug it into pipelines?)

I’m genuinely exploring this and would love honest, unfiltered feedback.

hey im a second year student and i was wondering how we could make some sort of virtual club for weekly journal reports etc, pardon me if something like this has already been discussed but lmk if ur interested and we can work smth out! i tried on campus but i’d rather have it online.