SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes.

ABSTRACT: A common way to investigate gene regulatory mechanisms is to identify differentially expressed genes using transcriptomics, find their candidate enhancers using epigenomics, and search for over-represented transcription factor (TF) motifs in these enhancers using bioinformatics tools. A related follow-up task is to model gene expression as a function of enhancer sequences and rank TF motifs by their contribution to such models, thus prioritizing among regulators. We present a new computational tool called SEAMoD that performs the above tasks of motif finding and sequence-to-expression modeling simultaneously. It trains a convolutional neural network model to relate enhancer sequences to differential expression in one or more biological conditions. The model uses TF motifs to interpret the

SUBMITTER: Bhogale S 

PROVIDER: S-EPMC10680628 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature