Discovering differential genome sequence activity with interpretable and efficient deep learning

Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail. mit.edu/deepaccess-package/.

Automated summary

Discovering sequence features that guide cells to different fates is crucial for understanding cellular development and disease-related mutations. The Expected Pattern Effect and Differential Expected Pattern Effect methods can interpret genome regulatory sequences to identify cell type-specific or condition-specific patterns. These methods identify relevant transcription factor motifs and spacings that predict cell state-specific chromatin accessibility.