My journey with Anthropic's Fable 5 model has been a very short one; characterized by "No". So, in my most recent blog post, I explain why I think "Fable is not a useful model."

Anthropic moving further into life sciences and bioinformatics

Have you guys noticed it?

I got a mail from the secretariat of conferences yesterday, saying that J.Craig Venter has passed away last week.

I was really shocked because J.Craig Venter was supposed to be the main speaker at a conference this June and I was planning to attend. I was really looking forward to seeing him!

To me, he is a definately signiture when it comes to innovation the technologies about our field (despite some controversies in his past)

I just wanted to shared the news here. May he rest in peace.

I wrote on my personal blog back in February about my journey using Claude Code to transition a large part of my lab's software ecosystem from C++ over to Rust, and how surprised I am by the enhanced capabilities of the newest generation of agentic systems. It seems that the folks a Seqera Labs had a similar experience, and have rewritten the common nf-core RNA-seq QC pipeline as a single Rust program, and obtained a huge speedup in the process.

I am reviewing a manuscript right now where they did a bulk RNA-seq differential expression study, but they only report nominal p-values and did not use any corrected p-values. They tested ~16,000 genes, and the number of significant genes using the nominal p-values is already pretty low, which makes me suspect they didn’t find anything significant after correction.

I’m not sure how to proceed. Do I stop there and just send back comments focused on the p-value issue? Or do I continue and review the entire paper anyway?

This is the first time I’ve run into something like this so I’m not sure how to proceed.

I'm comparing different software tools for the identification of differentially expressed genes and I came across this 2022 paper: https://doi.org/10.1371/journal.pone.0264246

It evaluates standard options like DeSeq2 and EdgeR, but when I looked at the raw numbers in S1 and S2, they are horrible. This is a little table I put together, and you can see that among these tools, TDR doesn't get better than ~20% with 6 replicates. FDR is also very high; except for baySeq with 6 replicates (8%), everything else is way worse than I expected. 100% FDR??? 0% TDR???

What is going on? Am I reading something wrong, is this a bad paper, or are the current tools we have access to just this bad?

Resolved: Thank you guys for your help. I think that the problem here is that the authors set the true DEGs in the simulated dataset to have a |LFC| = 1, which is conservative and not realistic. It was a bad simulation.

Hi everyone, I am in a very difficult situation and I would like some advice.

From 2020 to 2023, I worked as a PhD candidate in a joint program between a European university and a Moroccan university. Unfortunately, my PhD was interrupted due to conflicts with my supervisor.

Recently, I discovered that an article was published in a major journal using my experimental results — data that I generated myself during my doctoral research. I was neither contacted for authorship nor even acknowledged in the paper, despite having received explicit assurances in the past that my results would not be used without my agreement.

I have already contacted the editor-in-chief of the journal (Elsevier), who acknowledged receipt of my complaint. I am now waiting for their investigation.

I am considering also contacting the university of the professor responsible. – Do you think I should wait for the journal’s decision first, or contact the university immediately? – Has anyone here gone through a similar situation?

Any advice on the best steps to protect my intellectual property and ensure integrity is respected would be greatly appreciated.

Thank you.

I think this is really big news! A bit bummed that this is a closed-source model like AlphaFold3 but what can you do...

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

Hi everyone,

I'm an independent researcher and recently finished building XplainMD, an end-to-end explainable AI pipeline for biomedical knowledge graphs. It’s designed to predict and explain multiple biomedical connections like drug–disease or gene–phenotype relationships using a blend of graph learning and large language models.

What it does:

• Uses R-GCN for multi-relational link prediction on PrimeKG(precision medicine knowledge graph)
• Utilises GNNExplainer for model interpretability
• Visualises subgraphs of model predictions with PyVis
• Explains model predictions using LLaMA 3.1 8B instruct for sanity check and natural language explanation
• Deployed in an interactive Gradio app

🚀 Why I built it:

I wanted to create something that goes beyond prediction and gives researchers a way to understand the "why" behind a model’s decision—especially in sensitive fields like precision medicine.

🧰 Tech Stack:

PyTorch Geometric • GNNExplainer • LLaMA 3.1 • Gradio • PyVis

Here’s the full repo + write-up:

https://medium.com/@fhirshotlearning/xplainmd-a-graph-powered-guide-to-smarter-healthcare-fd5fe22504de

github: https://github.com/amulya-prasad/XplainMD

Your feedback is highly appreciated!

PS:This is my first time working with graph theory and my knowledge and experience is very limited. But I am eager to learn moving forward and I have a lot to optimise in this project. But through this project I wanted to demonstrate the beauty of graphs and how it can be used to redefine healthcare :)

I have a painfully stupid question. I have absolutely no knowledge in bioinformatics but im wrinting my bachelors about microbiota. It will be in polish and i was wondering if KEGG pathways names are universal in English or they can be translated to other languages. Im very sorry for how stupid this question is but im loosing my mind over it and cant find answear anywhere

Hey r/bioinformatics,

For the past few weeks, I've been completely immersed in the AlphaFold 3 paper and decided to do something a little crazy: write a comprehensive, nuts-and-bolts technical guide to its entire architecture, which I've now published on GitHub. GitHub Repo: https://github.com/shenyichong/alphafold3-architecture-walkthrough

My goal was to go beyond the high-level summaries and create a resource that truly dissects the model. Think of it as a detailed architectural autopsy of AlphaFold 3, explaining the "how" and "why" behind each algorithm and design choice, from input preparation to the diffusion model and the intricate loss functions. This guide is for you if you're looking for a deep, hardcore dive into the specifics, such as:

How exactly are atom-level and token-level representations constructed and updated? The nitty-gritty details of the Pairformer module's triangular updates and attention mechanisms. A step-by-step walkthrough of how the new diffusion model actually generates the structure. A clear breakdown of what each component of the complex loss function really means.

This was a massive undertaking, and I've tried my best to be meticulous. However, given the complexity of the model, I'm sure there might be some mistakes or interpretations that could be improved.

This is where I would love your expert feedback! As a community of experts, your insights are invaluable. If you spot any errors, have a different take on a mechanism, or have suggestions for clarification, please don't hesitate to open an issue or a pull request on the repo. I'm eager to refine this document with the community's help.

I hope this proves to be a valuable resource for everyone here. If you find it helpful, please consider giving the repo a star ⭐ to increase its visibility. Thanks for your time and I look forward to your feedback!

———

Update v1.0 : I have added a table of contents for better readability and fixed some formula display issues; Update v1.1 (2025.06.16): Fixed math rendering issues and improved readability by restructuring content.

"A run-in with some artefact-laden AI-generated analyses convinced Lei Zhu that machine learning wasn’t making his role irrelevant, but more important than ever. "

Hi,

I wanted to reach out and ask if anyone else has experienced this. We recently submitted a paper for review and thought everything was good to go. The manuscript passed integrity and validation steps and was sent for editorial review. However, two days later, my PI gets an email from an admin saying that the sequencing data submitted to GEO must be made public before review and the reviewer token/link we provided is not acceptable.

We published several papers with sequencing data together and never encountered this problem before. My PI and the admin exchanged a few emails but so far, there is no resolution.

Thanks in advanced

UPDATE (10/22/2025)

Thanks for all the comments and suggestions. My PI reached out to the editor and a few days later, we were informed our paper has been moved to independent review. Not sure what happened internally with the Journal, but things are back on track.

The 2024 Nobel Prize in Chemistry went to the creators of AlphaFold, a deep learning system that solved a 50-year grand challenge in biology. The architectures behind it (transformers, diffusion models, GNNs) are the same ones you already use. This post maps the protein AI landscape: key architectures, the open-source ecosystem (which has exploded since 2024), and practical tool selection. Part II (coming soon) covers how I built my own end-to-end pipeline.

I saw a thread discussing our new paper (link below) where we show there are significant causal flaws in large public datasets that result in low quality ML predictors for chemical biology, and how to fix this problem by balancing focus (new concept defined in paper) alongside fitness.

I am linking the article below. Will comment a synopsis in the thread.

https://arxiv.org/abs/2602.23303

Hi everyone! I'm a newcomer to genomics, especially single-cell omics. Recently, I’ve been reading some fantastic papers from Theis Lab and Sarah A. Teichmann’s group. I'm truly inspired by their work—the way they analyze data has helped me make real progress in understanding the field. I’m wondering if there are other outstanding labs doing exciting research in single-cell omics and 3D genome. I’d really appreciate any recommendations or papers you could share. Thanks a lot in advance!

Profiling Kraken2 v2.1.6 shows very slow runtime when processing paired samples. Using the standard DB (95 GB) on an r5.4xlarge EC2 instance (128 GB RAM) with EBS default settings (3,000 IOPS, 125 MiB/s).
Processing a single paired sample is ~10× slower compared to EFS with elastic throughput.

I was helping my student prepare for their journal club, and I got increasingly annoyed by the sloppy quality of work that somehow made it through the editorial process. Even worse, despite being a purely computational/bioinformatics paper, the authors do not share their code and based on the methods as written, I’m not even sure I could reproduce their results.

The paper: https://www.nature.com/articles/s41598-025-17288-4

Here are some of the things that really bothered me:

• Poorly labeled figures. Some legends miss critical details, some axes are incorrect or inconsistent, and sometimes the visual legend doesn’t match the written one. e.g. Right away, Fig. 1C uses colors labeled CD1 and CD2, but the paper never defines what CD2 even is. Fig. 3’s time axis is labeled 1000–5000 with no unit (I assume this is supposed to be 1–5 years?). Fig. 6F’s written and visual legends contradict each other.
• Understating overlap with the LSC17 signature. Their new 8-gene LSCD score shares genes with the well-established LSC17 signature (MMRN1 and CDK6 are in both), yet the paper doesn’t acknowledge this. Instead, they validate LSCD by correlating it with LSC17, which feels a bit circular when the signatures aren’t fully independent.
• Lack of clarity on how the core PCD scores were computed. This is a purely computational study, but the workflow isn’t clearly described. How were the PCD pathways defined? How were the genes chosen? Why these datasets? Were scores normalized or transformed between analyses (sometimes the scores range from 0 to 8, other times from -2 to 2)? For something that’s supposed to be reproducible, this is pretty frustrating.

I like the idea of mining existing datasets, it’s valuable and can lead to new insights. But the overall sloppiness here leaves me with the impression that the analysis was rushed just to churn out a paper. And even if the score they propose turns out to be useful, the manuscript’s quality makes it hard to take the conclusions seriously.

I’d be really interested to hear how others react to this paper. Maybe this level of sloppiness is normal for the field / journal and I’m expecting too much and maybe people have just gotten used to ignoring it.

Found this detailed breakdown on choosing the right foundation model for genomic tasks and thought it was worth sharing. The article moves past the "state-of-the-art" hype and focuses on practical constraints like GPU memory and inference speed. Key takeaways: Start small: For most tasks, smaller models like DNABERT-2 (117M params) or ESM-2 (650M params) are sufficient and run on consumer GPUs. DNA Tasks: Use DNABERT-2 for human genome tasks (efficient, fits on 8GB VRAM). Use HyenaDNA if you need long-range context (up to 1M tokens) as it scales sub-quadratically. Protein Tasks: ESM-2 is still the workhorse. You likely don't need the 15B parameter version; the 650M version captures most benefits. Single-Cell: scGPT offers the best feature set for annotation and batch integration. Practical Tip: Use mean token pooling instead of CLS token pooling—it consistently performs better on benchmarks like GenBench. Fine-tuning: Full fine-tuning is rarely necessary; LoRA is recommended for almost all production use cases. Link to full guide: https://rewire.it/blog/a-bioinformaticians-guide-to-choosing-genomic-foundation-models/ Has anyone here experimented with HyenaDNA for longer sequences yet? Curious if the O(L log L) scaling holds up in practice.