GNNMF: a multi-view graph neural network for ATAC-seq motif finding.

ABSTRACT:

Background

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) utilizes the Transposase Tn5 to probe open chromatic, which simultaneously reveals multiple transcription factor binding sites (TFBSs) compared to traditional technologies. Deep learning (DL) technology, including convolutional neural networks (CNNs), has successfully found motifs from ATAC-seq data. Due to the limitation of the width of convolutional kernels, the existing models only find motifs with fixed lengths. A Graph neural network (GNN) can work on non-Euclidean data, which has the potential to find ATAC-seq motifs with different lengths. However, the existing GNN models ignored the relationships among ATAC-seq sequences, and their parameter settings should be improved.

Results

In t

SUBMITTER: Zhang S 

PROVIDER: S-EPMC10956247 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature