ArticleComputational and structural biotechnology journal2023
ChEAP: ChIP-exo analysis pipeline and the investigation of
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Compact Origins and Where to Find Them: ORC's Guide to Genome-Wide Licensing.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- ChIP-mini: a low-input ChIP-exo protocol for elucidating DNA-binding protein dynamics in intracellular pathogens.Nucleic acids research · 2025Article
- An Optimized Method for Reconstruction of Transcriptional Regulatory Networks in Bacteria Using ChIP-exo and RNA-seq Datasets.Journal of microbiology (Seoul, Korea) · 2024Article
- Unveiling the novel regulatory roles of RpoD-family sigma factors in Salmonella Typhimurium heat shock response through systems biology approaches.PLoS genetics · 2024Article
- Experimental promoter identification of a foodborne pathogenFrontiers in microbiology · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
Genome-scale studies of the bacterial regulatory network have been leveraged by declining sequencing cost and advances in ChIP (chromatin immunoprecipitation) methods. Of which, ChIP-exo has proven competent with its near-single base-pair resolution. While several algorithms and programs have been developed for different analytical steps in ChIP-exo data processing, there is a lack of effort in incorporating them into a convenient bioinformatics pipeline that is intuitive and publicly available. In this paper, we developed ChIP-exo Analysis Pipeline (ChEAP) that executes the one-step process, starting from trimming and aligning raw sequencing reads to visualization of ChIP-exo results. The pipeline was implemented on the interactive web-based Python development environment - Jupyter Notebook, which is compatible with the Google Colab cloud platform to facilitate the sharing of codes and collaboration among researchers. Additionally, users could exploit the free GPU and CPU resources allocated by Colab to carry out computing tasks regardless of the performance of their local machines. The utility of ChEAP was demonstrated with the ChIP-exo datasets of RpoN sigma factor in
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