Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework

Nucleic Acids Research
Jinyu YangQin Ma

Abstract

The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-regulatory motif prediction using deep neural networks and the binomial distribution model. DESSO outperformed existing tools, including DeepBind, in predicting motifs in 690 human ENCODE ChIP-sequencing datasets. Furthermore, the deep-learning framework of DESSO expanded motif discovery beyond the state-of-the-art by allowing the identification of known and new protein-protein-DNA tethering interactions in human transcription factors (TFs). Specifically, 61 putative tethering interactions were identified among the 100 TFs expressed in the K562 cell line. In this work, the power of DESSO was further expanded by integrating the detection of DNA shape features. We found that shape information has strong predictive power for TF-DNA binding and provides new putative shape motif information for human TFs. Thus, DESSO improves in the identification and structural analysis of TF binding sites, by integrating the complexities of DNA binding into a deep-learning framework.

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Citations

May 7, 2020·Frontiers in Bioengineering and Biotechnology·Rao Zeng, Minghong Liao
Oct 3, 2020·Briefings in Bioinformatics·Ying HeDe-Shuang Huang
Oct 17, 2020·Molecular & Cellular Proteomics : MCP·Guido van Mierlo, Michiel Vermeulen
Feb 9, 2021·Molecular & Cellular Proteomics : MCP·Guido van Mierlo, Michiel Vermeulen
Oct 5, 2021·Briefings in Bioinformatics·Shuangquan ZhangYan Wang

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Datasets Mentioned

BETA
K562

Methods Mentioned

BETA
ChIP-seq
Footprinting

Software Mentioned

MEME
TFClass
GSEA
WebLogo
ChIPMunk
KMAC
gkm
TRANSFAC
SMiLE
TensorFlow

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