ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data
By Ke-ren Zhou, Shun Liu, Wenju Sun, Lingling Zheng, Hui Zhou, Jianhua Yang, Liang-Hu Qu. Nucleic Acids Res., 2016
362 citations recorded by the source corpus.
The abnormal transcriptional regulation of non-coding RNAs (ncRNAs) and protein-coding genes (PCGs) is contributed to various biological processes and linked with human diseases, but the underlying mechanisms remain elusive. In this study, we developed ChIPBase v2.0 (http://rna.sysu.edu.cn/chipbase/) to explore the transcriptional regulatory networks of ncRNAs and PCGs. ChIPBase v2.0 has been expanded with ∼10 200 curated ChIP-seq datasets, which represent about 20 times expansion when comparing to the previous released version. We identified thousands of binding motif matrices and their binding sites from ChIP-seq data of DNA-binding proteins and predicted millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. We constructed ‘Regulator’ module to predict hundreds of TFs and histone modifications that were involved in or affected transcription of ncRNAs and PCGs. Moreover, we built a web-based tool, Co-Expression, to explore the co-expression patterns between DNA-binding proteins and various types of genes by integrating the gene expression profiles of ∼10 000 tumor samples and ∼9100 normal tissues and cell lines. ChIPBase also provides a ChIP-Function tool and a genome browser to predict functions of diverse genes and visualize various ChIP-seq data. This study will greatly expand our understanding of the transcriptional regulations of ncRNAs and PCGs.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/41d8d32039c62b807253520a17515fef9527d01e
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In Cancer Research
In Molecular Biology
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Citations in the corpus
- PlantPAN3.0: a new and updated resource for reconstructing transcriptional regulatory networks from ChIP-seq experiments in plants (2018, methodology): “Although ChIPBase v2.”
- ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments (2017, methodology): “As the quality of ChIP-seq experiments vary significantly (20,21), we incorporated a critical data quality filtering step in our pipeline––not implemented in other databases (chip-atlas http://chip-atlas.org (6,7,18)).”
- hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets (2019, methodology): “[12] Zhou K-R, Liu S, Sun W-J, Zheng L-L, Zhou H, Yang J-H, et al.”
- Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis (2018, background): “Although there are other ChIP-seq databases, including ChIP-Atlas (BioRxiv: https://doi.org/10.1101/262899), ChIPBase (20) and ReMap (21), the Cistrome DB differs from these in terms of sample coverage, comprehensive quality control metrics, data browsing and querying capabilities, and downstream an”
- TransmiR v2.0: an updated transcription factor-microRNA regulation database (2018, background): “For example, ChIPBase (15) provides regulatory relationships between TFs and non-coding RNAs by parsing ChIP-seq data.”