Jacob Schreiber
@jmschreiber91.bsky.social did:plc:577pvu4vezznlk2mpxkz4wsm
Studying genomics, machine learning, and fruit. My code is like our genomes -- most of it is junk. Assistant Professor UMass Chan Previously IMP Vienna, Stanford Genetics, UW CSE.
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Jacob Schreiber (@jmschreiber91.bsky.social) es un usuario de Bluesky que se unió el 17 noviembre 2023. Tiene 6.667 seguidores, sigue a 1.408 cuentas y ha publicado 824 posts.
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Interestingly, tissue-specific accessible regions alone gave performance approaching that of models fine-tuned on VISTA. This suggests a practical route to enhancer design in tissues where experimentally validated enhancer datasets are scarce.
Ver postWhat if no in vivo–validated enhancer dataset is available? We benchmarked inferred enhancer labels based on: • distal accessible regions without CTCF • H3K4me1/H3K27ac-overlapping accessible regions • tissue-specific accessible regions
Ver postIn vivo validation was the critical test: ❤️ heart: 5/5 🦵 limb: 5/5 🧠 midbrain: 5/5
Ver postTo move from prediction to design, we optimized sequences de novo for both accessibility and tissue-specific enhancer activity using Ledidi, a model-guided, gradient-based sequence design method: www.biorxiv.org/content/10.1...
Ver postThe models also captured interpretable regulatory logic, including tissue-specific TF motifs and motif combinations/grammar. This suggests that part of the mammalian cis-regulatory code is sufficiently learnable to support predictive sequence design.
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