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Goodbye @NASAAmes 🥲 Thank you for a stellar 6 years! 🚀
Mike Lee retweeted
Out today in @nature.com: our review on how single cells evolve into multicellular organisms, with Ozan Bozdag, @kai_tong_mc25, Peter Yunker, and Matt Herron.
rdcu.be/9ZuiKow9twKa
For an overview, check out the video below.
Mike Lee retweeted
Cross-feeding enables robust coexistence between four bacterial species pnas.org/doi/abs/10.1073/pna…
Mike Lee retweeted
Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-like molecules, and predicting the effects of genetic mutations. But these models are often expensive to run, potentially limiting their impact.
In our latest Science Blog, we share how Claude was able to optimize inference for more than 30 open-source models, making them 4x faster on average, partly by writing custom software for GPUs. We’re open sourcing all of the optimization code.
Read more: anthropic.com/research/claud…
Mike Lee retweeted
Improved ancestral genome reconstruction using a learned gene-content grammar biorxiv.org/content/10.64898…
Mike Lee retweeted
gbdraw開発記:論文の図を描くPythonスクリプトがブラウザ内実行Webアプリになるまで - Qiita
qiita.com/satoshi_kawato/ite…
久々に文章を書きました。これまでの経緯を振り返るとともに、今つまづいているポイントを記しています。
Mike Lee retweeted
Spatiotemporal Regulation of N2 Fixation and Photosynthesis in Trichodesmium: New Insights From mRNA CARD‐FISH and nanoSIMS enviromicro-journals.onlinel…
Starting today, 10,000 scientists across every field, from math to chemistry to physics and more, can get Claude through our new Claude Team plan for scientists. Standard seats are free, and premium seats with 5x usage limits are $15 per month, an 80% discount, for one year.
Claude is becoming increasingly capable of scientific work, with recent progress on problems from advanced physics calculations to protein design. Alongside that progress, we've been investing in the research community: Claude Science launched in June, and our AI for Science program funds high-impact projects with free credits. Today's expansion builds on both.
Principal investigators (or equivalent) at academic and nonprofit research institutions can sign up, then add the researchers in their group. Over the coming months, we plan to extend the program well beyond the initial 10,000 seats.
Learn more: claude.com/programs/team-pla…
Mike Lee retweeted
Evidence tiers for strain‐resolved long‐read metagenomics onlinelibrary.wiley.com/doi/…
minibwa v0.5+0.6:
* fixed an issue in PE mapQ (inherited from bwa-mem)
* more conservative PE mapQ (learned from dragmap)
* HG002 var calling: FP down but FN up
* memory-mapped index loading
* user-defined insert size distribution
* APIs working for BS-seq
github.com/lh3/minibwa/relea…
Mike Lee retweeted
metaSMASH: Scalable Biosynthetic Gene Cluster Detection for Large Metagenomic Assemblies biorxiv.org/content/10.64898…
Mike Lee retweeted
NCBI Hidden Markov Models (HMM) Release 20.0
18,950 prokaryotic HMMs maintained by @ncbi ncbiinsights.ncbi.nlm.nih.go…
#microbiology #bioinforbioinformatics #genomics
Mike Lee retweeted
Check out this @seattletimes article where @MattParsek & I give a microbiologist's perspective on cleaning your home! Brb, scrubbing my sink... 🫧 seattletimes.com/explore/at-…
Mike Lee retweeted
HIFMB advertised 5 post-doc positions to work on social science / basic science interface, and here is a blog post to tell you why I care about this, and what it looks like:
merenlab.org/blog/HIPP-on-hu…
Please spread the word if you can.
Mike Lee retweeted
Arguably the most boring step in genomics is the first one: normalization. Settled science. Scale + log. Move on.
Except that here's been a huge blind spot in the field. And it matters for AIxBio. A 🧵about what I think may be one of the most important papers I've written. 1/
Mike Lee retweeted
Circular genome comparison is now live in #gbdraw! 🚀 The built-in LOSATN instantly runs pairwise alignments and generates homology rings for your sequences, 100% in your browser! Try it out: gbdraw.app #bioinformatics #genomics #microbiology
Mike Lee retweeted
How do you publish this tree and claim it's a success???!!!
We are releasing Carbon: a crazy fast DNA model
Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days.
Here are the tricks we used:
When modelling DNA sequences a lot of the performance comes down to tokenizing the sequences in a smart way. BPE tokenizer struggle because there are no whitespaces and character (called base in DNA) level tokenizers waste a lot of compute on too many tokens.
Carbon is built with a unique tokenizer: we split sequences in chunks of 6 bases, but during both training and inference we can work with single base resolution. That's similar to having word tokens but resolving them at the character level. All possible thanks to the DNA tokens unique structure.
The architecture combined with the tokenizer makes the model 275x faster than the previous SoTA (Evo2) at this size.
We built an interactive demo so you can explore how the model can generate DNA sequences, investigate the structure of genes, predict the effect of mutations, generate and fold proteins and even reconstruct parts of the tree of life.
huggingface.co/spaces/Huggin…
Mike Lee retweeted
Microbe Decoder uncovers functional traits of microbes in microbiome datasets academic.oup.com/nar/advance…
Mike Lee retweeted
🧬Collinearity Analysis is here in #gbdraw v0.11.0! You can easily detect and visualize synteny and collinearity blocks using LOSATP. You can instantly align the entire diagram based on a shared ortholog!
Try it now: gbdraw.app/
#bioinformatics #genomics #microbiology
Mike Lee retweeted
Welcome @aphillippy to @JohnsHopkins in both @JHUCompSci and @JHUBME, as a new @JHU_BDPs professor starting today! linkedin.com/posts/aphillipp…






















