Excited to share our work from the Battle Lab! It’s on an innovative deep learning model architecture incorporating biological domain knowledge to predict traits from large scale proteomic data: www.medrxiv.org/content/10.1...

Prior knowledge informs graph neural networks to improve phenotype prediction from proteomics

High-throughput proteomics data provides dense individual-level molecular readouts, enabling the development of machine learning models for predicting diverse phenotypes relevant to patient health. Pr...

medrxiv.org

Really excited to share our new PRS method, developed with @aprilkim.bsky.social and @alexisbattle.bsky.social ! Our approach is to use a lot of recently developed functional annotations to better estimate the weights of the SNPs. www.medrxiv.org/content/10.1...

Polygenic prediction of phenotypes with a neural empirical Bayes approach

Polygenic risk scores (PRS) estimate the expected value of a phenotype based on individual genotypes. Although statistical approaches for calculating PRS have advanced considerably in recent years, fe...

medrxiv.org

Check out the amazing work from my colleague and friend!

Arun Das@arun-das.bsky.social · last yr.

Our pre-print on investigating variation in South Asian genomes is now out! Thank you to @mikeschatz.bsky.social, @rajivmccoy.bsky.social and @aabiddanda.bsky.social for all their work on this. 🧵 A thread on the key results and takeaways from our work:

Excited to share our latest work on comparing and visualizing multiple genome assemblies to identify conservation and structural variation in pangenomes with Mumemto! Check out poster 250 at #bog25 if you are here. New preprint coming very soon 👀

If you are here at #bog25 please check out my poster (number 87) tonight! 😁 Showing our work on common variation associated with aneuploidy in human embryos

1/n 🚨Very excited to share our recent work!🚨 To understand gene regulation across diverse environmental conditions and cellular contexts, we treated a broad array of human cell types with three environmental exposures in vitro. www.biorxiv.org/content/10.1...

Uncalled4: a toolkit for nanopore signal alignment, analysis and visualization of DNA and RNA modifications. www.nature.com/articles/s41...

Happy to share our work characterizing functional rare SVs in rare diseases with long-read genome sequencing and transcriptomic outlier data: genome.cshlp.org/content/earl...

Excited to share a preprint for (w/ @benlangmead.bsky.social) our new tool, Mumemto, on biorxiv! Mumemto finds multi-MUMs across pangenomes (i.e. mummer but for pangenomes). It can rapidly visualize synteny, identify misassemblies, and accelerate core genome and multiple alignment, highlighting SVs.

Mumemto: efficient maximal matching across pangenomes

Aligning genomes into common coordinates is central to pangenome analysis and construction, but it is also computationally expensive. Multi-sequence maximal unique matches (multi-MUMs) are guideposts ...

biorxiv.org

Mumemto: efficient maximal matching across pangenomes https://www.biorxiv.org/content/10.1101/2025.01.05.631388v1