This is part 3 of a series on uv. Other posts in this series: uv, part 1: running scripts and tools uv, part 2: building and publishing packages This post Coming soon… Python and R I get the same question all the time from up and coming data scientists in training: “should I use Python or R?” My answer is always the same: it’s not Python versus R, it’s python

This week’s recap highlights new methods in genetic epidemiology, mostly centered around genomic data sharing and privacy-preserving methods: a short commentary on genomic data sharing highlighting how new challenges complicate large-scale data sharing practices, a privacy-preserving method for QTL mapping, privacy-preserving methods for federated biobank-scale GWAS analysis, a Nextflow pipeline for polygenic score QC and construction, and new

I get asked a lot how I have the time to read all these papers and articles I post about here. I don’t. Not all of them at least. I listen to many of them. Lately I’ve been using an app called ElevenReader , made by the popular text-to-speech service ElevenLabs. It’s free for iOS and Android.

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 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.