The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse
Open accessAbstract
Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named 'RegNetwork', of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org.
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Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
MicroRNAs: small RNAs with a big role in gene regulation
He, L., & Hannon, G. J. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1379 · Cited by 7,118
Prediction of Mammalian MicroRNA Targets
Lewis, B. P., Shih, I.-H., Jones-Rhoades, M. W., Bartel, D. P., & Burge, C. B. (2003) · Cell · doi:10.1016/s0092-8674(03)01018-3 · Cited by 5,199
Combinatorial microRNA target predictions
Krek, A., Grün, D., Poy, M. N., Wolf, R., Rosenberg, L., Epstein, E. J., MacMenamin, P., da Piedade, I., Gunsalus, K. C., Stoffel, M., et al. (2005) · Nature Genetics · doi:10.1038/ng1536 · Cited by 4,603
Pruitt, K. D., Tatusova, T., & Maglott, D. R. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl842 · Cited by 4,481
Cited by
NicheNet: Modeling intercellular communication by linking ligands to target genes
Browaeys, R., Saelens, W., & Saeys, Y. (2020) · Nature Methods · doi:10.1038/s41592-019-0667-5 · Cited by 2,192
Benchmark and integration of resources for the estimation of human transcription factor activities
Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D., & Saez-Rodriguez, J. (2019) · Genome Research · doi:10.1101/gr.240663.118 · Cited by 1,122
Spatially organized multicellular immune hubs in human colorectal cancer
Pelka, K., Hofree, M., Chen, J. H., Sarkizova, S., Pirl, J. D., Jorgji, V., Bejnood, A., Dionne, D., Ge, W. H., Xu, K. H., et al. (2021) · Cell · doi:10.1016/j.cell.2021.08.003 · Cited by 874
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792
A network-biology perspective of microRNA function and dysfunction in cancer
Bracken, C. P., Scott, H. S., & Goodall, G. J. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.134 · Cited by 668
Müller-Dott, S., Tsirvouli, E., Vazquez, M., Ramirez Flores, R. O., Badia-i-Mompel, P., Fallegger, R., Türei, D., Lægreid, A., & Saez-Rodriguez, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad841 · Cited by 380
Yang, X., Liu, G., Feng, G., Bu, D., Wang, P., Jiang, J., Chen, S., Yang, Q., Zhang, Y., Man, Z., et al. (2023) · doi:10.1101/2023.09.26.559542 · Cited by 148
Self-citationYang, X., Liu, G., Feng, G., Bu, D., Wang, P., Jiang, J., Chen, S., Yang, Q., Miao, H., Zhang, Y., et al. (2024) · Cell Research · doi:10.1038/s41422-024-01034-y · Cited by 148
Self-citationBiophysically Motivated Regulatory Network Inference: Progress and Prospects
Äijö, T., & Bonneau, R. (2016) · Human Heredity · doi:10.1159/000446614 · Cited by 30
Jiang, J., Chen, S., Tsou, T., McGinnis, C. S., Khazaei, T., Zhu, Q., Park, J. H., Strazhnik, I.-M., Vielmetter, J., Gong, Y., et al. (2023) · doi:10.1101/2023.04.19.537364 · Cited by 21
Kamal, A., Arnold, C., Claringbould, A., Moussa, R., Servaas, N. H., Kholmatov, M., Daga, N., Nogina, D., Mueller-Dott, S., Reyes-Palomares, A., et al. (2021) · doi:10.1101/2021.12.18.473290 · Cited by 12
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Xue, A., Fan, J., Dong, O. A., Huang, H. L., Chen, L., Allen, P. C., Spenceley, E., Sagi-Zsigmond, E., Bowen, B., Henry, A., et al. (2026) · Nature · doi:10.1038/s41586-026-11173-4 · Cited by 3
Metadata
- Authors
- Liu, Z.-P., Wu, C., Miao, H., & Wu, H.
- Venue
- Database
- Publisher
- Oxford University Press (OUP)
- Published
- 2015
- Volume
- 2015
- Pages
- bav095
- ISSN
- 1758-0463 (ISSN-L)
- OpenAlex
- W2175370850
- Cited by
- 555
- References
- 54
- FWCI
- 8.36
- Type
- journal article
- Language
- en
- Open access
- gold
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