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TFTG: A comprehensive database for human transcription factors and their targets

Authors :
Xinyuan Zhou
Liwei Zhou
Fengcui Qian
Jiaxin Chen
Yuexin Zhang
Zhengmin Yu
Jian Zhang
Yongsan Yang
Yanyu Li
Chao Song
Yuezhu Wang
Desi Shang
Longlong Dong
Jiang Zhu
Chunquan Li
Qiuyu Wang
Source :
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 1877-1885 (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

Details

Language :
English
ISSN :
20010370
Volume :
23
Issue :
1877-1885
Database :
Directory of Open Access Journals
Journal :
Computational and Structural Biotechnology Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.bba1ce15c5c34469894748eb52e542f9
Document Type :
article
Full Text :
https://doi.org/10.1016/j.csbj.2024.04.036