ArticleNucleic acids research2019
Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 75 citations in OpenAlex.
- ShapeME: A Tool and Web Front-end for De Novo Discovery of Structural Motifs Underpinning Protein-DNA Interactions.Journal of molecular biology · 2026Article
- PTF-Vāc: An explainable and generative deep co-learning encoder-decoder system for ab initio discovery of plant transcription factor binding sites.Plant communications · 2025Article
- Enhancer reprogramming: critical roles in cancer and promising therapeutic strategies.Cell death discovery · 2025Review
- ShapeME: A tool and web front-end for de novo discovery of structural motifs underpinning protein-DNA interactions.bioRxiv : the preprint server for biology · 2025Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.Briefings in bioinformatics · 2024Article
- Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.Briefings in bioinformatics · 2024Article
- Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers.Nucleic acids research · 2024Article
- PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.Briefings in bioinformatics · 2024Article
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.Nucleic acids research · 2024Article
- Predicting Transcription Factor Binding Sites with Deep Learning.International journal of molecular sciences · 2024Article
- MMGAT: a graph attention network framework for ATAC-seq motifs finding.BMC bioinformatics · 2024Article
- GNNMF: a multi-view graph neural network for ATAC-seq motif finding.BMC genomics · 2024Article
- A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data.Patterns (New York, N.Y.) · 2024Article
- Less-is-more: selecting transcription factor binding regions informative for motif inference.Nucleic acids research · 2024Article
- Predicting DNA structure using a deep learning method.Nature communications · 2024Article
- JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.Nucleic acids research · 2024Article
- Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph.Frontiers in genetics · 2024Article
- CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 2 countries.
Funding
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.