ArticleNucleic acids research2011
Inferring transcription factor complexes from ChIP-seq data.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 1 of them a synthesis that pooled it.
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Who cites it
92 citing papers in PubMed, 1 synthesis or guideline pooled it, 150 citations in OpenAlex.
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- Peak Scores Significantly Depend on the Relationships between Contextual Signals in ChIP-Seq Peaks.International journal of molecular sciences · 2024Article
- Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet.Genome biology · 2024Article
- Bioinformatics for wet-lab scientists: practical application in sequencing analysis.BMC genomics · 2023Article
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- Strand asymmetries across genomic processes.Computational and structural biotechnology journal · 2023Review
- Detecting clusters of transcription factors based on a nonhomogeneous poisson process model.BMC bioinformatics · 2022Article
- Developmental genome-wide occupancy analysis of bZIP transcription factor NRL uncovers the role of c-Jun in early differentiation of rod photoreceptors in the mammalian retina.Human molecular genetics · 2022Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) allows researchers to determine the genome-wide binding locations of individual transcription factors (TFs) at high resolution. This information can be interrogated to study various aspects of TF behaviour, including the mechanisms that control TF binding. Physical interaction between TFs comprises one important aspect of TF binding in eukaryotes, mediating tissue-specific gene expression. We have developed an algorithm, spaced motif analysis (SpaMo), which is able to infer physical interactions between the given TF and TFs bound at neighbouring sites at the DNA interface. The algorithm predicts TF interactions in half of the ChIP-seq data sets we test, with the majority of these predictions supported by direct evidence from the literature or evidence of homodimerization. High resolution motif spacing information obtained by this method can facilitate an improved understanding of individual TF complex structures. SpaMo can assist researchers in extracting maximum information relating to binding mechanisms from their TF ChIP-seq data. SpaMo is available for download and interactive use as part of the MEME Suite (http://meme.nbcr.net).
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Registered trials
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.