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PreprintCited by 1Genomics and Chromatin DynamicsGene Regulatory Network AnalysisBioinformatics and Genomic Networks

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

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Bhogale, Shounak, Seward, Chris, Stubbs, Lisa, Sinha, Saurabh·2023·bioRxiv (Cold Spring Harbor Laboratory)
DOI 10.1101/2023.11.09.565900PMC PMC10680628
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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 sequences, learning these motifs and their relative importance to each biological condition from data. It also utilizes epigenomic information in the form of activity scores of putative enhancers and automatically searches for the most promising enhancer for each gene. Compared to existing neural network models of non-coding sequences, SEAMoD uses far fewer parameters, requires far less training data, and emphasizes biological interpretability. We used SEAMoD to understand regulatory mechanisms underlying the differentiation of neural stem cell (NSC) derived from mouse forebrain. We profiled gene expression and histone modifications in NSC and three differentiated cell types and used SEAMoD to model differential expression of nearly 12,000 genes with an accuracy of 81%, in the process identifying the Olig2, E2f family TFs, Foxo3, and Tcf4 as key transcriptional regulators of the differentiation process.

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Authors
Bhogale, S., Seward, C., Stubbs, L., & Sinha, S.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • Georgia Institute of Technology (US)
Corresponding author
Bhogale, Shounak
ORCID
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2023-11-13
DOI
10.1101/2023.11.09.565900
PMC
PMC10680628
OpenAlex
W4388620463
Cited by
1
References
7
Citations by year
2026: 1
Type
posted content
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • National Science Council
  • National Institutes of Health
  • National Institute of Mental Health (R01MH114600)
  • National Institute of General Medical Sciences (R35GM131819)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
cis-regulatory elements, enhancer identification, transcription factor binding motifs, differentially expressed genes, interpretable neural networks, convolutional neural networks, histone modifications, neural stem cell differentiation, forebrain, Olig2, E2F transcription factors, FOXO3, TCF4, transcriptional regulation

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