ArticleNucleic acids research2010
On the detection and refinement of transcription factor binding sites using ChIP-Seq data.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- Identification and application of bioparts for plant synthetic biology.Molecules and cells · 2025Review
- Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data.Briefings in bioinformatics · 2025Article
- BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.Bioinformatics (Oxford, England) · 2024Article
- Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.Nucleic acids research · 2023Article
- Validating a re-implementation of an algorithm to integrate transcriptome and ChIP-seq data.PeerJ · 2023Article
- Systematic Evaluation of DNA Sequence Variations onFrontiers in genetics · 2021Article
- Promoter Architecture and Promoter Engineering inMetabolites · 2020Review
- Set cover-based methods for motif selection.Bioinformatics (Oxford, England) · 2020Article
- ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery.Bioinformatics (Oxford, England) · 2019Article
- A Clustering Approach for Motif Discovery in ChIP-Seq Dataset.Entropy (Basel, Switzerland) · 2019Article
- FisherMP: fully parallel algorithm for detecting combinatorial motifs from large ChIP-seq datasets.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019Article
- Systems and Synthetic Biology Approaches to Engineer Fungi for Fine Chemical Production.Frontiers in bioengineering and biotechnology · 2018Review
- Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites.Current genomics · 2017Review
- Inducible super-enhancers are organized based on canonical signal-specific transcription factor binding elements.Nucleic acids research · 2017Article
- Evaluating tools for transcription factor binding site prediction.BMC bioinformatics · 2016Article
- Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.Toxicological sciences : an official journal of the Society of Toxicology · 2016Article
- Estrogen receptor alpha (ERα/ESR1) mediates the p53-independent overexpression of MDM4/MDMX and MDM2 in human breast cancer.Oncotarget · 2016Article
- An Entropy-Based Position Projection Algorithm for Motif Discovery.BioMed research international · 2016Article
- Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells.Frontiers in genetics · 2016Review
- A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets.BioMed research international · 2015Article
4 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein-DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic 'greedy' search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation.
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