MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data.

ABSTRACT:

Motivation

Transcription factor binding sites (TFBSs) prediction is a crucial step in revealing functions of transcription factors (TFs) from high-throughput sequencing data. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) provides insight on TFBSs and nucleosome positioning by probing open chromatic, which can simultaneously reveal multiple TFBSs compare to traditional technologies. The existing tools based on convolutional neural network (CNN) only find the fixed length of TFBSs from ATAC-seq data. Graph neural network (GNN) can be considered as the extension of CNN, which has great potential in finding multiple TFBSs with different lengths from ATAC-seq data.

Results

We develop a motif predictor called MMGraph based on three-layer GNN and coexisting

SUBMITTER: Zhang S 

PROVIDER: S-EPMC9524997 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature