A practical scale for manuscript readiness

New feature - subscribe for free monthly emails with the preprint summaries in the 11 focus areas on unpeer.org

Unpeer — Discover Preprints Unpeer scores and ranks BioRxiv preprints by impact, novelty, and rigor. Search, filter, and discover the strongest preprint science.

This is better context than I can fit in a tweet unpeer.org/about

Curious to hear if there is overlap in the proteomics preprints you found versus those on Unpeer

Also journals are very very slow. Unpeer is fast

My ultimate goal is to build something that replaces all the bad parts about journals while keeping the reasons we have journals to start with. Slow and stochastic review replaced by consistent AI review. Keep quantitative proxy for importance for use by hiring committees or potential readers

Great question. Nearly 30,000 preprints/month. Human review is slow, inconsistent, and doesn’t scale. Journals still take our time and money. We need AI to filter. If everyone switches to unpeer for preprint curation, we don't need journals anymore. Fast, free, signal from noise

I spent $150 on AI to review all the >15,000 preprints posted on biorxiv @biorxivpreprint.bsky.social in 2026 so you don't have to.

Results sorted into focus areas (Lenses) online free at unpeer.org

Not perfect. But it scales.

Unpeer — Discover Preprints Unpeer scores and ranks BioRxiv preprints by impact, novelty, and rigor. Search, filter, and discover the strongest preprint science.

Made some UI changes to unpeer.org and now you can subscribe to your monthly virtual issues of interest via email for free.

Out of over 5,000 preprints that appear on @biorxivpreprint.bsky.social every month, get a quick sense of strengths and weaknesses from AI reviews, free

Unpeer — Discover Preprints Unpeer scores and ranks BioRxiv preprints by impact, novelty, and rigor. Search, filter, and discover the strongest preprint science.

If you are fed up with journals and peer review then check this out unpeer.org

Single‐Injection Multi‐Omics Analysis by Direct Infusion Mass Spectrometry onlinelibrary.wiley....

---
#proteomics #prot-paper

Vibe Coding Comes to Omics How Jesse Meyer built a fully functional proteomics data analysis application in under 10 minutes, using four prompts, no handwritten code, and under $2

Vibe Coding Comes to Omics - How @j-my-sci.bsky.social from @cedarssinaihsu.bsky.social built a #proteomics data analysis application in under 10 minutes, using four prompts, no handwritten code, & under $2 www.theanalyticalscientist.com/issues/2026/... #coding #vibecoding #bioinformatics

HT @j-my-sci.bsky.social #academia #research #science #superhuman

It was peer reviewed. Figure 2 directly compares the vibe coded platform to a local reference pipeline using simulated data with known ground truth. That validation step was improved during review.

Thanks for sharing this. I’ve found that hands on use clarifies both the strengths and the limits. In experienced hands these tools can accelerate iteration, but they still require judgment and validation like anything else in science. I try to emphasize that balance in the paper too.

The debate around vibe coding in science is exactly what should be happening. New automation always raises real concerns. The path forward is not blind adoption or blanket rejection, but shared standards

Grateful you shared this. The intent was to spark exactly this discussion: how we can use AI to speed up tool building while keeping validation, reproducibility, and rigor front and center. I very much welcome the conversation.

Models absolutely inherit bias. That is a real concern. But this paper is not training AI to infer biology from literature. It uses an LLM as a coding assistant to scaffold a standard proteomics workflow, then validates the outputs against a reference pipeline with known ground truth.

I am a fan of judicious vibe-coding, but it requires training in a methodology to evaluate results. E.g., I pointed a wet lab PhD student to a LLM to code an excel formula for decoding mass spec composition strings. I think this is OK where you have an orthogonal method to validate your results.

What specifically don’t you like about the paper? It explains the approach to generating code, tests that the code produces correct results for a dataset with known properties, and provides the prompts, code, and data. The discussion includes caveats and areas needing further work.

in one month i vibe coded, trained, and eval'd a new family of deep learning models for de novo peptide sequencing. I applied ideas from ML preprints from Oct and Dec 2025 and achieved comparable performance to Casanovo on a single consumer-grade GPU

I have had several related experiences. Building in days huge deep learning ideas, testing many variants, and finding quickly that it's not a worthwhile. Imagine that a PhD student had instead done that manually over years and then had nothing for their dissertation

If you have tried vibe coding, you know it can be a super power for experienced coders 👇

On the "vibe coding omics analysis is here" demo paper, and some responses (run for the hills!), a thread for myself:
- we know that LLM-assisted or even driven coding is here. if you haven't tried it even in the last 3 months, you are behind
- yes it is powerful and enabling

1/7

Thanks for reading it and for your perspective

Most genius ideas seem obvious in hindsight

We are all software engineers now

No apologies needed! Just wait until you try the coding interfaces like Claude Code or antigravity! Antigravity will write thousands of lines for you if you give it clear long term goals and test definitions

We got the NOA for an MPI R01 from the NIA yesterday.

Greatful for all the collaborators, facilitators, mentors, and trainees in my group who made this possible.