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 Coming soon… Lately I’ve heard a lot great things about uv, an extremely fast Python package and project manager, written in Rust. After the volume of praise I see about uv reached a critical level I decided to take a look at the docs myself and give it a try.

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.

Last year I wrote a post describing an R package I put together that fetches recent bioRxiv preprints from a given subject and summarizes them in a couple of sentences using a local LLM running through Ollama: That tool has a limitation in that it’s using the bioRxiv RSS feed to pull recent paper titles and abstracts, and the RSS feeds currently only provide the 30 most recent preprints in each subject area.

We’re 6 weeks into the new year and I’m still catching up on papers from my late 2024 backlog.

I’ve been blogging about genetics, statistics, computational biology, data science, and science in general for over 15 years. I published my first blog post on Getting Genetics Done in 2009, and started this blog last year after taking a few years off. Science blogging has significantly contributed to my personal and professional growth as a scientist. Writing takes time and effort — time I could have spent elsewhere.

I'm still catching up on papers from my late 2024 backlog. This week’s recap highlights a browser application for visualizing pathogen dispersal, a DNA language model evaluation benchmark on regulatory DNA, regularized ensemble polygenic risk prediction with GWAS summary statistics, multimodal analysis of RNA-seq data for complex trait genetics, and a deep dive on blastp’s E-value.

Something a little different for this week’s recap. I’ve been thinking a lot lately about the practice of data science education in this era of widely available (and really good!) LLMs for code. Commentary at the top based on my own data science teaching experience, with a deep dive into a few recent papers below.

The majority of developers use LLMs to help write code, present company included. When I’m working in languages I know well, they're fantastic at handling the grunt work: generating boilerplate, suggesting completions, and writing tedious tests and documentation.