This week’s recap highlights FLAMES for prioritizing genes at trait-associated GWAS hits, integrating protein language models and an automatic biofoundry for enhanced protein evolution, benchmarking DNA sequence models for causal regulatory variant prediction, and the doubletrouble R/Bioconductor package for identifying and classifying gene and genome duplications.

R 4.5.0 was released last week, and Bioconductor 3.21 came a few days later. You can read the R release notes here and the Bioconductor 3.21 announcement here.

This week’s recap highlights Evo2 for variant effect analysis and genome design, a preprint showing that pretraining doesn’t necessarily increase performance on genomic foundation models, a new R package ggalign for making complex biological data visualizations with ggplot2, and an ancestral reconstruction method for ancient DNA. I also highlight a few reviews in biodiversity genomics.

In a previous post I demonstrated how to set up a local LLM that you can run through either a command line interface (Ollama) or a graphical user interface (Open WebUI and others), and quickly demonstrated how to “chat with your documents” with a local model using LMStudio. In that previous post I simply attached a few documents to a one-off chat.

This week’s recap highlights ESCARGOT, an AI agent for biomedical knowledge graphs and reasoning, CASTER for direct species tree inference from whole-genome alignments, the scGPT-spatial foundation model for spatial transcriptomics, the BioChatter platform for biomedical research applications with LLMs, moscot for mapping cells through time and space, and two reviews: one on epigenetic clocks and another on structural variation in the human

This is part 2 of a series on uv. Other posts in this series: uv, part 1: running scripts and tools This post uv, part 3: Python in R with reticulate Coming soon… Last year I wrote a post on creating a Python command line application with Click using a cookiecutter template, building with setuptools and publishing with twine.

Here we are most of the way through March and I’m just getting around to my first “weekly” recap. It’s been a busy month — I’m writing a few papers of my own which I’ll share here when they’re published, and I took some much needed R&R in Portugal where I traded my stack of research papers for some escapist sci-fi. But I’m back now and making my way through a deep backlog.

Anyone reading this newsletter has surely used the frontier models like ChatGPT, Claude, and Gemini. I’ve written a few posts about using local models but haven’t really talked much about the tools I use to directly interact with these models. Those previous posts interact with local models using tools like ellmer in R or my own biorecap package which interacts with a locally running Ollama server.

This is part 1 of a series on uv. Other posts in this series: This post uv, part 2: building and publishing packages uv, part 3: Python in R with reticulate Coming soon… Lately I’ve heard a lot great things about uv, an extremely fast Python package and project manager, written in Rust.

This week’s recap highlights Verkko2 for T2T genome assembly, the GPN-MSA DNA language model trained on multispecies alignments for variant effect prediction that outperforms other methods like CADD, ESM-1b, phyloP, phastCons, nucleotide transformer, and HyenaDNA, fast orthology inference with FastOMA, a foundation model of transcription across cell types, and engineering CRISPR-Cas PAM sites using deep learning.

It’s been a few weeks since I wrote a recap about what I’m reading. It’s been difficult watching helplessly as the institutions and financial infrastructure underpinning my profession are being systematically and irreversibly dismantled, with brilliant scientists I know personally having their careers destroyed and lives upturned.