SEAM Python Suite (2025)

SEAM (Systematic Explanation of Attribution-based Mechanisms) is a Python suite to interpret sequence-based deep learning models for regulatory genomics data. SEAM systematically mutates sequences and clusters their attribution maps to reveal diverse regulatory mechanisms and genomic background effects that shape DNN behavior in local regions of sequence space. I developed this method and software in the Koo lab and Kinney lab at Cold Spring Harbor Laboratory.

For installation instructions, tutorials, and documentation, please refer to the SEAM website, https://seam-nn.readthedocs.io/. For an extended discussion of this approach and its applications, please refer to our manuscript.