ArticleFrontiers in genetics2018
Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 11 citations in OpenAlex.
- Rescuing biologically relevant consensus regions across replicated samples.BMC bioinformatics · 2023Article
- Endogenous neural stem cells characterization using omics approaches: Current knowledge in health and disease.Frontiers in cellular neuroscience · 2023Review
- Detecting clusters of transcription factors based on a nonhomogeneous poisson process model.BMC bioinformatics · 2022Article
- Transcriptome Analysis and Intraspecific Variation in Spanish Fir (International journal of molecular sciences · 2022Article
- Differential ATAC-seq and ChIP-seq peak detection using ROTS.NAR genomics and bioinformatics · 2021Article
- Epigenetic regulation of neuronal cell specification inferred with single cell "Omics" data.Computational and structural biotechnology journal · 2020Article
- Recursive Motif Analyses Identify Brain Epigenetic Transcription Regulatory Modules.Computational and structural biotechnology journal · 2019Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP.
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Registered trials
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