WTKO-CNN: Deep Learning Reveals Sequence Motifs Distinguishing Wild-Type and Knockout ATAC-seq Peaks
Abstract
Chromatin regulators can alter transcriptional programs by modifying the accessibility of regulatory DNA elements. Understanding how regulatory sequences differ between wild-type (WT) and knockout (KO) conditions is crucial for deciphering transcriptional control. Here, we applied a convolutional neural network, \textbf{WTKO-CNN} with an attention mechanism to classify DNA sequences as WT or KO, achieving high predictive performance. To interpret the model, we generated saliency maps to identify nucleotide positions most influential for the classification decision. From these high-saliency regions, we extracted and clustered k-mers, enabling de novo motif discovery. Sequence logos and consensus motifs derived from the CNN filters revealed biologically meaningful patterns, which are further validated using MEME, TOMTOM, and HOMER against known transcription factor binding sites. Our analysis identified motifs associated with transcription factor families that discriminate WT from KO sequences, demonstrating that CNN-guided saliency mapping is a powerful approach for uncovering functional sequence features.
Related
- Anticipatory Gaze Reveals Individual Differences in Implicit Sequence Learning Before Motor Performance
- Machine learning reveals sequence and genomic context features underlying Alu-specific effects on genome folding
- Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex
- β-Catenin Knockout in the Olfactory Bulb and Hippocampus Reveals Dual Roles in Olfactory Discrimination and Working Memory
- Shotgun metagenomic profiling of the fecal microbiome in Lamp2 knockout mice reveals limited genotype-associated differences under standard housing
BibTeX
@misc{dey2026wtkocnn,
title = {{WTKO-CNN: Deep Learning Reveals Sequence Motifs Distinguishing Wild-Type and Knockout ATAC-seq Peaks}},
author = {Lopamudra Dey},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.24034},
eprint = {2605.24034},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.24034},
}
RIS
TY - UNPB
TI - WTKO-CNN: Deep Learning Reveals Sequence Motifs Distinguishing Wild-Type and Knockout ATAC-seq Peaks
AU - Lopamudra Dey
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2605.24034
UR - https://arxiv.org/abs/2605.24034
AB - Chromatin regulators can alter transcriptional programs by modifying the accessibility of regulatory DNA elements. Understanding how regulatory sequences differ between wild-type (WT) and knockout (KO) conditions is crucial for deciphering transcriptional control. Here, we applied a convolutional neural network, \textbf{WTKO-CNN} with an attention mechanism to classify DNA sequences as WT or KO, achieving high predictive performance. To interpret the model, we generated saliency maps to identify nucleotide positions most influential for the classification decision. From these high-saliency regions, we extracted and clustered k-mers, enabling de novo motif discovery. Sequence logos and consensus motifs derived from the CNN filters revealed biologically meaningful patterns, which are further validated using MEME, TOMTOM, and HOMER against known transcription factor binding sites. Our analysis identified motifs associated with transcription factor families that discriminate WT from KO sequences, demonstrating that CNN-guided saliency mapping is a powerful approach for uncovering functional sequence features.
ER -