SEAM: systematic explanation of attribution-based mechanisms for regulatory genomics

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SEAM (Systematic Explanation of Attribution-based for Mechanisms) is a Python suite to use meta-explanations to interpret sequence-based deep learning models for regulatory genomics data. 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 paper:

  • Seitz, E.E., McCandlish, D.M., Kinney, J.B., and Koo P.K. Deciphering the determinants of mechanistic variation in regulatory sequences. bioRxiv (2025). (unpublished)

Installation:

With Anaconda sourced, create a new environment via the command line:

conda create --name seam

Next, activate this environment via conda activate seam, and install the following packages:

pip install seam-nn

Finally, when you are done using the environment, always exit via conda deactivate.

Notes

SEAM has been tested on Mac and Linux operating systems. Typical installation time on a normal computer is less than 1 minute.

If you have any issues installing SEAM, please see:

For issues installing SQUID, the package used for sequence generation and inference, please see:

Older DNNs may require inference via Tensorflow 1.x or related packages in conflict with SEAM defaults. Users will need to run SEAM piecewise within separate environments:

  1. Tensorflow 1.x environment for generating in silico sequence-function-mechanism dataset
  2. Tensorflow 2.x environment for applying SEAM to explain in silico sequence-function-mechanism dataset

Usage:

SEAM provides a simple interface that takes as input a sequence-based deep-learning model (e.g., a DNN), which is used as an oracle to generate an in silico sequence-function-mechanism dataset representing a localized region of sequence space. SEAM uses a meta-explanation framework to interpret the in silico sequence-function-mechanism dataset, deciphering the determinants of mechanistic variation in regulatory sequences.

API figure: To be done.

Examples

Google Colab examples for applying SEAM on previously-published deep learning models are available at the following links:

Expected run time for the "Figure 2. Local library with hierarchical clustering | DeepSTARR" demo (above) is ~3.6 minutes using Google Colab T4 GPU.

Python script examples are provided in the examples/ folder for locally running SEAM and exporting outputs to file. Additional dependencies for these examples may be required and outlined at the top of each script. Examples include:

  • To be done.

Citation:

If this code is useful in your work, please cite our paper.

bibtex TBD

License:

Copyright (C) 2023–2025 Evan Seitz, David McCandlish, Justin Kinney, Peter Koo

The software, code sample and their documentation made available on this website could include technical or other mistakes, inaccuracies or typographical errors. We may make changes to the software or documentation made available on its web site at any time without prior notice. We assume no responsibility for errors or omissions in the software or documentation available from its web site. For further details, please see the LICENSE file.

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  • Released:
Newer release available (0.6.6)

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Avatar for evanseitz from gravatar.com evanseitz

Credits

Author: Evan Seitz

License

MIT License

Requires

Python >=3.7.2

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