WTKO-CNN: Deep Learning Reveals Sequence Motifs Distinguishing Wild-Type and Knockout ATAC-seq Peaks

The study introduces WTKO-CNN, a convolutional neural network with an attention mechanism, to classify DNA sequences as wild‑type (WT) or knockout (KO) based on ATAC‑seq data. By generating saliency maps, the authors pinpointed influential nucleotide positions, extracted high‑saliency k‑mers, and performed de novo motif discovery, producing sequence logos and consensus motifs that align with known transcription factor binding sites. Validation with MEME, TOMTOM, and HOMER confirmed that the identified motifs belong to transcription factor families that differentiate WT from KO sequences.

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