JLTS

Tanvi Sinha is an experimental cardiovascular biologist at UCSF. The computational work behind this project — single-cell genomics and training deep-learning models — is what she would normally collaborate on with a computational specialist like her teammate Jan Lebert. In this project we tested whether Claude Science can be that specialist. Heart-valve disease is common and largely untreatable short of surgery, and the transcription-factor logic that gives valve endothelial cells their identity is a route into understanding it. Using Claude Science, we mined public mouse and zebrafish single-cell RNA and ATAC atlases, reprocessed a mouse multiome from raw data to call 940 valve-EC enhancers, and trained a ChromBPNet model that predicts valve chromatin accessibility directly from DNA sequence. Interpreted with no motifs supplied, the model recovered a conserved ETS + AP-1 + KLF regulatory grammar centered on the mechano-sensitive gene Klf2, cross-validated across mouse and two zebrafish datasets. We also used it to package the whole pipeline as a reusable skill and build interactive demos, including a live in-silico mutagenesis browser.