Fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution.

ABSTRACT: Decoding the cell type-specific transcription factor (TF) binding landscape at single-nucleotide resolution is crucial for understanding the regulatory mechanisms underlying many fundamental biological processes and human diseases. However, limits on time and resources restrict the high-resolution experimental measurements of TF binding profiles of all possible TF-cell type combinations. Previous computational approaches either cannot distinguish the cell context-dependent TF binding profiles across diverse cell types or can only provide a relatively low-resolution prediction. Here we present a novel deep learning approach, Leopard, for predicting TF binding sites at single-nucleotide resolution, achieving the average area under receiver operating characteristic curve (AUROC) of 0.982 and

SUBMITTER: Li H 

PROVIDER: S-EPMC8015851 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature