ArticleNucleic acids research2014
MACE: model based analysis of ChIP-exo.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
48 citing papers in PubMed, 91 citations in OpenAlex.
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- Elucidating the CodY regulon inmSystems · 2023Article
- The Escherichia coli Fur pan-regulon has few conserved but many unique regulatory targets.Nucleic acids research · 2023Article
- ChEAP: ChIP-exo analysis pipeline and the investigation ofComputational and structural biotechnology journal · 2023Article
- Evidence that direct inhibition of transcription factor binding is the prevailing mode of gene and repeat repression by DNA methylation.Nature genetics · 2022Article
- proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms.Nucleic acids research · 2022Article
- Unveiling Forkhead-mediated regulation of yeast cell cycle and metabolic networks.Computational and structural biotechnology journal · 2022Article
- Unraveling the functions of uncharacterized transcription factors in Escherichia coli using ChIP-exo.Nucleic acids research · 2021Article
- Promoter-proximal elongation regulates transcription in archaea.Nature communications · 2021Article
- Fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution.Genome research · 2021Article
- Molecular and computational approaches to map regulatory elements in 3D chromatin structure.Epigenetics & chromatin · 2021Review
- PM4NGS, a project management framework for next-generation sequencing data analysis.GigaScience · 2021Article
- Elucidation of Regulatory Modes for Five Two-Component Systems in Escherichia coli Reveals Novel Relationships.mSystems · 2020Article
- Comparative analysis of ChIP-exo peak-callers: impact of data quality, read duplication and binding subtypes.BMC bioinformatics · 2020Article
- Hdac3, Setdb1, and Kap1 mark H3K9me3/H3K14ac bivalent regions in young and aged liver.Aging cell · 2020Article
- The Escherichia coli transcriptome mostly consists of independently regulated modules.Nature communications · 2019Article
- ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast.Nucleic acids research · 2019Article
- Glucocorticoid Receptor-Tethered Mineralocorticoid Receptors Increase Glucocorticoid-Induced Transcriptional Responses.Endocrinology · 2019Article
- Characterizing protein-DNA binding event subtypes in ChIP-exo data.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
16 authors at 6 institutions in 3 countries.
Funding
Abstract
Understanding the role of a given transcription factor (TF) in regulating gene expression requires precise mapping of its binding sites in the genome. Chromatin immunoprecipitation-exo, an emerging technique using λ exonuclease to digest TF unbound DNA after ChIP, is designed to reveal transcription factor binding site (TFBS) boundaries with near-single nucleotide resolution. Although ChIP-exo promises deeper insights into transcription regulation, no dedicated bioinformatics tool exists to leverage its advantages. Most ChIP-seq and ChIP-chip analytic methods are not tailored for ChIP-exo, and thus cannot take full advantage of high-resolution ChIP-exo data. Here we describe a novel analysis framework, termed MACE (model-based analysis of ChIP-exo) dedicated to ChIP-exo data analysis. The MACE workflow consists of four steps: (i) sequencing data normalization and bias correction; (ii) signal consolidation and noise reduction; (iii) single-nucleotide resolution border peak detection using the Chebyshev Inequality and (iv) border matching using the Gale-Shapley stable matching algorithm. When applied to published human CTCF, yeast Reb1 and our own mouse ONECUT1/HNF6 ChIP-exo data, MACE is able to define TFBSs with high sensitivity, specificity and spatial resolution, as evidenced by multiple criteria including motif enrichment, sequence conservation, direct sequence pileup, nucleosome positioning and open chromatin states. In addition, we show that the fundamental advance of MACE is the identification of two boundaries of a TFBS with high resolution, whereas other methods only report a single location of the same event. The two boundaries help elucidate the in vivo binding structure of a given TF, e.g. whether the TF may bind as dimers or in a complex with other co-factors.
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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.