ArticleNucleic acids research2017
Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improved linking of motifs to their TFs using domain information.Bioinformatics (Oxford, England) · 2020Pooled it
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- Genome-Wide Identification and Expression Analysis of the LbDof Transcription Factor Family Genes inPlants (Basel, Switzerland) · 2025Article
- A simple urine test by 3D-plus-3D immunoassay guides preciseBioengineering & translational medicine · 2023Article
- Gene expression profiling of sepsis-associated acute kidney injury.Experimental and therapeutic medicine · 2020Article
- Article
- Effect of promoter, promoter mutation and enhancer on transgene expression mediated by episomal vectors in transfected HEK293, Chang liver and primary cells.Bioengineered · 2019Article
- A k-mer grammar analysis to uncover maize regulatory architecture.BMC plant biology · 2019Article
- The spatial binding model of the pioneer factor Oct4 with its target genes during cell reprogramming.Computational and structural biotechnology journal · 2019Article
- Integrated analysis of motif activity and gene expression changes of transcription factors.Genome research · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Transcription factors (TFs) play crucial roles in regulating gene expression through interactions with specific DNA sequences. Recently, the sequence motif of almost 400 human TFs have been identified using high-throughput SELEX sequencing. However, there remain a large number of TFs (∼800) with no high-throughput-derived binding motifs. Computational methods capable of associating known motifs to such TFs will avoid tremendous experimental efforts and enable deeper understanding of transcriptional regulatory functions. We present a method to associate known motifs to TFs (MATLAB code is available in Supplementary Materials). Our method is based on a probabilistic framework that not only exploits DNA-binding domains and specificities, but also integrates open chromatin, gene expression and genomic data to accurately infer monomeric and homodimeric binding motifs. Our analysis resulted in the assignment of motifs to 200 TFs with no SELEX-derived motifs, roughly a 50% increase compared to the existing coverage.
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Registered trials
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