Vinícius H. Franceschini-Santos

This study compares ATAC-seq-based estimates of transcription factor activity with multiplexed reporter assays across perturbations, finding overlap but also discrepancies in their sensitivity to different TF types.

The paper investigates how genomic regions called LADs interact with the nuclear lamina, finding that these interactions are multivalent and cooperative, with some subregions having stronger affinity and influencing neighboring sequences.

This study uses sequence-based deep learning models to identify functional promoter mutations in cancer, finding enrichment in known and novel cancer genes.

This paper introduces PARM, a deep learning model trained on MPRA data to predict human promoter activity and identify TF binding preferences, revealing positional and functional differences in regulatory grammar.

The paper shows that disrupting nuclear lamina association promotes ABCB1 gene derepression and taxol resistance in RPE cells.