The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets
Open accessAbstract
Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF–target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of chromatin immunoprecipitation sequencing (ChIP-seq) provide great opportunities to discover the TF–target regulations across different conditions. In this study, we constructed a database named hTFtarget, which integrated huge human TF target resources (7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs) and epigenetic modification information to predict accurate TF–target regulations. hTFtarget offers the following functions for users to explore TF–target regulations: (1) browse or search general targets of a query TF across datasets; (2) browse TF–target regulations for a query TF in a specific dataset or tissue; (3) search potential TFs for a given target gene or non-coding RNA; (4) investigate co-association between TFs in cell lines; (5) explore potential co-regulations for given target genes or TFs; (6) predict candidate TF binding sites on given DNA sequences; (7) visualize ChIP-seq peaks for different TFs and conditions in a genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploring human TF–target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtarget.
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Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
FIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247
PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants
Jin, J., Tian, F., Yang, D.-C., Meng, Y.-Q., Kong, L., Luo, J., & Gao, G. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw982 · Cited by 2,361
TRANSFAC(R) and its module TRANSCompel(R): transcriptional gene regulation in eukaryotes
Matys, V. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj143 · Cited by 2,166
TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions
Han, H., Cho, J.-W., Lee, S., Yun, A., Kim, H., Bae, D., Yang, S., Kim, C. Y., Lee, M., Kim, E., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1013 · Cited by 1,973
Transcriptional Regulation and Its Misregulation in Disease
Lee, T. I., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.02.014 · Cited by 1,620
Architecture of the human regulatory network derived from ENCODE data
Gerstein, M. B., Kundaje, A., Hariharan, M., Landt, S. G., Yan, K.-K., Cheng, C., Mu, X. J., Khurana, E., Rozowsky, J., Alexander, R., et al. (2012) · Nature · doi:10.1038/nature11245 · Cited by 1,442
GSCALite: a web server for gene set cancer analysis
Liu, C.-J., Hu, F.-F., Xia, M.-X., Han, L., Zhang, Q., & Guo, A.-Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty411 · Cited by 1,149
Self-citationMathelier, A., Fornes, O., Arenillas, D. J., Chen, C.-Y., Denay, G., Lee, J., Shi, W., Shyr, C., Tan, G., Worsley-Hunt, R., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1176 · Cited by 937
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Devos, H., Zoidakis, J., Roubelakis, M. G., Latosinska, A., & Vlahou, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241210004 · Cited by 119
Wang, Y.-F., Zhang, W.-L., Li, Z.-X., Liu, Y., Tan, J., Yin, H.-Z., Zhang, Z.-C., Piao, X.-J., Ruan, M.-H., Dai, Z.-H., et al. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01797-1 · Cited by 69
Xu, Y., Zhang, J., Hu, Y., Li, X., Sun, L., Peng, Y., Sun, Y., Liu, B., Bian, Z., & Rong, Z. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109524 · Cited by 27
ATF6 enables pathogen infection in ticks by inducing stomatin and altering cholesterol dynamics
Vosbigian, K. A., Wright, S. J., Steiert, B. P., Rosche, K. L., Fisk, E. A., Ramirez-Zepp, E., Park, J. M., Shelden, E. A., & Shaw, D. K. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2501045122 · Cited by 6
Metadata
- Authors
- Zhang, Q., Liu, W., Zhang, H.-M., Xie, G.-Y., Miao, Y.-R., Xia, M., & Guo, A.-Y.
- Venue
- Genomics, Proteomics & Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-04-01
- Volume
- 18
- Issue
- 2
- Pages
- 120-128
- ISSN
- 1672-0229 (ISSN-L)
- 2210-3244
- OpenAlex
- W3080500495
- Cited by
- 600
- References
- 33
- FWCI
- 15.01
- Type
- journal article
- Language
- en
- Open access
- gold
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