GaugeFixer: Software for overcoming parameter non-identifiability in models of sequence-function relationships. GaugeFixer addresses gauge freedoms that arise in biophysical models of gene regulation.

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

SQUID: SQUID (Surrogate Quantitative Interpretability for Deepnets) is a Python suite to interpret sequence-based deep learning models for regulatory genomics data with domain-specific surrogate models.

MAVE-NN: A Python package for learning genotype-phenotype maps from multiplex assays of variant effect. MAVE-NN uses neural networks to model complex sequence-function relationships from MPRA and other high-throughput data.

Logomaker: A Python package for creating publication-quality sequence logos. Logomaker provides flexible and customizable visualizations for DNA, RNA, and protein sequence motifs.

SUFTware: A fast and lightweight Python implementation of Bayesian Field Theory algorithms for low-dimensional statistical inference. SUFTware currently supports the one-dimenstional density estimation algorithm DEFT.