ArticlePloS one2014
On the value of intra-motif dependencies of human insulator protein CTCF.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 28 citations in OpenAlex.
- Position Weight Matrix or Acyclic Probabilistic Finite Automaton: Which model to use? A decision rule inferred for the prediction of transcription factor binding sites.Genetics and molecular biology · 2024Article
- Position-specific evolution in transcription factor binding sites, and a fast likelihood calculation for the F81 model.Royal Society open science · 2024Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- A universal framework for detectingGenome research · 2021Article
- Resolving diverse protein-DNA footprints from exonuclease-based ChIP experiments.Bioinformatics (Oxford, England) · 2021Article
- Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases.Scientific reports · 2019Article
- Disentangling transcription factor binding site complexity.Nucleic acids research · 2018Article
- Predicting transcription factor binding using ensemble random forest models.F1000Research · 2018Article
- CircularLogo: A lightweight web application to visualize intra-motif dependencies.BMC bioinformatics · 2017Article
- InMoDe: tools for learning and visualizing intra-motif dependencies of DNA binding sites.Bioinformatics (Oxford, England) · 2017Article
- RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data.Bioinformatics (Oxford, England) · 2016Article
- Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells.Frontiers in genetics · 2016Review
- DiffLogo: a comparative visualization of sequence motifs.BMC bioinformatics · 2015Article
- Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data.BMC bioinformatics · 2015Article
- Varying levels of complexity in transcription factor binding motifs.Nucleic acids research · 2015Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The binding affinity of DNA-binding proteins such as transcription factors is mainly determined by the base composition of the corresponding binding site on the DNA strand. Most proteins do not bind only a single sequence, but rather a set of sequences, which may be modeled by a sequence motif. Algorithms for de novo motif discovery differ in their promoter models, learning approaches, and other aspects, but typically use the statistically simple position weight matrix model for the motif, which assumes statistical independence among all nucleotides. However, there is no clear justification for that assumption, leading to an ongoing debate about the importance of modeling dependencies between nucleotides within binding sites. In the past, modeling statistical dependencies within binding sites has been hampered by the problem of limited data. With the rise of high-throughput technologies such as ChIP-seq, this situation has now changed, making it possible to make use of statistical dependencies effectively. In this work, we investigate the presence of statistical dependencies in binding sites of the human enhancer-blocking insulator protein CTCF by using the recently developed model class of inhomogeneous parsimonious Markov models, which is capable of modeling complex dependencies while avoiding overfitting. These findings lead to a more detailed characterization of the CTCF binding motif, which is only poorly represented by independent nucleotide frequencies at several positions, predominantly at the 3' end.
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