TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile.

ABSTRACT: Determining transcriptional factor binding sites (TFBSs) is critical for understanding the molecular mechanisms regulating gene expression in different biological conditions. Biological assays designed to directly mapping TFBSs require large sample size and intensive resources. As an alternative, ATAC-seq assay is simple to conduct and provides genomic cleavage profiles that contain rich information for imputing TFBSs indirectly. Previous footprint-based tools are inheritably limited by the accuracy of their bias correction algorithms and the efficiency of their feature extraction models. Here we introduce TAMC (Transcriptional factor binding prediction from ATAC-seq profile at Motif-predicted binding sites using Convolutional neural networks), a deep-learning approach for predicting motif

SUBMITTER: Yang T 

PROVIDER: S-EPMC9499209 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature