Megan L. Taylor

The paper develops STEAM, an evolution-augmented deep learning model, to predict mammalian enhancer landscapes across species using comparative genomics and single-cell data.

This study examines HIV escape from two bNAbs in vivo, identifying recurrent mutations and genetic background dependencies in viral resistance.

The paper designs and characterizes diverse genomic parts for precise genome editing and molecular recording in mammalian cells.

12.4 million single-nucleus transcriptomes from 83 staged mouse embryos (E8 to P0) yield 190 cell types, a zygote-to-pup cell-type tree, and rapid postnatal transcriptional shifts.