Excited to share our work, now out in Nature Genetics, led by Sneha Mitra on using accessibility in genomic tiles (instead of called peaks) in multiome data to (1) perform gene expression prediction and (2) pinpoint functionally important tiles underlying disease variants.

Excited to share this work by Saori Sakaue and others on linking peaks to genes from 10x multiome data.

Excited to share our work introducing EPInformer🧬, a scalable and lightweight deep learning framework to predict gene expression by integrating promoter-enhancer sequences with epigenomic signals and chromatin contacts. 📜biorxiv.org/content/10.110… (1/11)

We are thrilled to share that our first paper from my new lab, Spateo (github.com/aristoteleo/sp…) for spatiotemporal modeling of molecular holograms, is now online in Cell: cell.com/cell/fulltext/…. Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal

Preprint on "Improving spliced alignment by modeling splice sites with deep learning". It describes minisplice for modeling splice signals. Minimap2 and miniprot now optionally use the predicted scores to improve spliced alignment. arxiv.org/abs/2506.12986