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

Tianqi Yang, Ricardo Henao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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-centric TF binding activity from paired-end ATAC-seq data. TAMC does not require bias correction during signal processing. By leveraging a one-dimensional convolutional neural network (1D-CNN) model, TAMC make predictions based on both footprint and non-footprint features at binding sites for each TF and outperforms existing footprinting tools in TFBS prediction particularly for ATAC-seq data with limited sequencing depth.
Original languageEnglish (US)
JournalPLoS Computational Biology
Volume18
Issue number9
DOIs
StatePublished - Sep 1 2022
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15

ASJC Scopus subject areas

  • Ecology
  • Cellular and Molecular Neuroscience
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Modeling and Simulation
  • Computational Theory and Mathematics
  • Molecular Biology

Fingerprint

Dive into the research topics of 'TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile'. Together they form a unique fingerprint.

Cite this