Google has a new experimental1 tool called Illuminate ( illuminate.google.com ) that takes a link to a preprint2 and creates a podcast discussing the paper. When I tested this with a few preprints, the podcasts it generated are about 6-8 minutes long, featuring a male and female voice discussing the key points of the paper in a conversational style. There are some obvious shortcomings.
This week’s recap highlights a new tool from Wei Shen and Zamin Iqbal for efficient sequence alignment against millions of prokaryotic genomes (LexicMap), a new tool from Heng Li for efficiently constructing and querying a sequence index at scale, an R/Bioconductor package for detecting and correcting DNA contamination in RNA-seq data, a method for dating gene age using synteny, how AlphaFold predictions for some types of conformations are
Llama 3.1 405B is the first open-source LLM on par with frontier models GPT-4o and Claude 3.5 Sonnet. I’ve been running the 70B model locally for a while now using Ollama + Open WebUI, but you’re not going to run the 405B model on your MacBook.
This week’s recap highlights Google/Deepmind’s new AlphaProteo tool for protein design, tools for protein structure alignment and analysis, biases in polygenic risk scores due to overlap and kinship, highly variable gene selection in single cell RNA-seq, and reconstruction of a 4.2 billion year old last universal common ancestor of life on Earth (spoiler alert: CRISPR-Cas is >4B years old!). Others that caught my attention include a new
Update March 2025 : The preprint described in this paper is now peer-reviewed and published in PLoS ONE. VP (Pete) Nagraj is a long time friend, colleague, and collaborator, and is the author of this post. Pete and I have co-authored over a dozen publications, and have taught several graduate courses in data science together.
This week’s recap highlights perspectives in de-extinction and patent law, systematic benchmarking of scATAC-seq methods, a 91 gigabase (!) animal genome, structural variant genotyping with long reads, cell type-specific enhancer prediction, and a perspective piece in AI in biosecurity.
TL;DR I wrote an R package that summarizes recent bioRxiv preprints using a locally running LLM via Ollama+ollamar, and produces a summary HTML report from a parameterized RMarkdown template.
Here are a few papers that caught my attention recently. I summarize a few in the deep dive at the top, then link to a few other papers of note later on. Subscribe to Paired Ends to get periodic summaries like this delivered to your e-mail.
Here are a few papers that caught my attention recently. I summarize a few in the deep dive at the top, then link to a few other papers of note later on. Subscribe to Paired Ends to get periodic summaries like this delivered to your e-mail.
I’ve been using the llama3.1:70b model just released by Meta using Ollama running on my MacBook Pro. Ollama makes it easy to talk to a locally running LLM in the terminal (ollama run llama3.1:70b) or via a familiar GUI with the open-webui Docker container. Here I’ll demonstrate using the ollamar package on CRAN to talk to an LLM running locally on my Mac.
