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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
STRING v9.1: protein-protein interaction networks, with increased coverage and integration
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398
miRBase: tools for microRNA genomics
Griffiths-Jones, S., Saini, H. K., van Dongen, S., & Enright, A. J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm952 · Cited by 4,382
Network motifs in the transcriptional regulation network of Escherichia coli
Shen-Orr, S. S., Milo, R., Mangan, S., & Alon, U. (2002) · Nature Genetics · doi:10.1038/ng881 · Cited by 2,952
The microRNA.org resource: targets and expression
Betel, D., Wilson, M., Gabow, A., Marks, D. S., & Sander, C. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm995 · Cited by 2,503
ChIP–seq: advantages and challenges of a maturing technology
Park, P. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2641 · Cited by 2,217
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
The UCSC Genome Browser database: update 2011
Fujita, P. A., Rhead, B., Zweig, A. S., Hinrichs, A. S., Karolchik, D., Cline, M. S., Goldman, M., Barber, G. P., Clawson, H., Coelho, A., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq963 · Cited by 1,811
Pruitt, K. D. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki025 · Cited by 1,660
The evolution of gene regulation by transcription factors and microRNAs
Chen, K., & Rajewsky, N. (2007) · Nature Reviews Genetics · doi:10.1038/nrg1990 · Cited by 1,547
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
miRecords: an integrated resource for microRNA-target interactions
Xiao, F., Zuo, Z., Cai, G., Kang, S., Gao, X., & Li, T. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn851 · Cited by 1,438
miRTarBase: a database curates experimentally validated microRNA–target interactions
Hsu, S.-D., Lin, F.-M., Wu, W.-Y., Liang, C., Huang, W.-C., Chan, W.-L., Tsai, W.-T., Chen, G.-Z., Lee, C.-J., Chiu, C.-M., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1107 · Cited by 1,389
Connecting microRNA Genes to the Core Transcriptional Regulatory Circuitry of Embryonic Stem Cells
Marson, A., Levine, S. S., Cole, M. F., Frampton, G. M., Brambrink, T., Johnstone, S., Guenther, M. G., Johnston, W. K., Wernig, M., Newman, J., et al. (2008) · Cell · doi:10.1016/j.cell.2008.07.020 · Cited by 1,370
Scale-free networks in cell biology
Albert, R. (2005) · Journal of Cell Science · doi:10.1242/jcs.02714 · Cited by 1,215
The IntAct molecular interaction database in 2012
Kerrien, S., Aranda, B., Breuza, L., Bridge, A., Broackes-Carter, F., Chen, C., Duesbury, M., Dumousseau, M., Feuermann, M., Hinz, U., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1088 · Cited by 1,150
The BioGRID interaction database: 2013 update
Chatr-aryamontri, A., Breitkreutz, B.-J., Heinicke, S., Boucher, L., Winter, A., Stark, C., Nixon, J., Ramage, L., Kolas, N., O’Donnell, L., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1158 · Cited by 1,017
Flicek, P., Amode, M. R., Barrell, D., Beal, K., Brent, S., Carvalho-Silva, D., Clapham, P., Coates, G., Fairley, S., Fitzgerald, S., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr991 · Cited by 823
Ideker, T., & Krogan, N. J. (2012) · Molecular Systems Biology · doi:10.1038/msb.2011.99 · Cited by 792
Revealing strengths and weaknesses of methods for gene network inference
Marbach, D., Prill, R. J., Schaffter, T., Mattiussi, C., Floreano, D., & Stolovitzky, G. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913357107 · Cited by 752
The BioPAX community standard for pathway data sharing
Demir, E., Cary, M. P., Paley, S., Fukuda, K., Lemer, C., Vastrik, I., Wu, G., D'Eustachio, P., Schaefer, C., Luciano, J., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1666 · Cited by 745 · cited 1×
“The users can employ some tools such as Sig2BioPax (43) to convert the regulations into the BioPAX Level 3 format (44).”
MethodFANMOD: a tool for fast network motif detection
Wernicke, S., & Rasche, F. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl038 · Cited by 745
Ongoing and future developments at the Universal Protein Resource
The UniProt Consortium (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1020 · Cited by 729
TarBase: A comprehensive database of experimentally supported animal microRNA targets
SETHUPATHY, P., CORDA, B., & HATZIGEORGIOU, A. G. (2006) · RNA · doi:10.1261/rna.2239606 · Cited by 717
An atlas of combinatorial transcriptional regulation in mouse and man
Ravasi, T., Suzuki, H., Cannistraci, C. V., Katayama, S., Bajic, V. B., Tan, K., Akalin, A., Schmeier, S., Kanamori-Katayama, M., Bertin, N., et al. (2010) · Cell · doi:10.1016/j.cell.2010.01.044 · Cited by 715
Bryne, J. C., Valen, E., Tang, M.-H. E., Marstrand, T., Winther, O., da Piedade, I., Krogh, A., Lenhard, B., & Sandelin, A. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm955 · Cited by 692
Reverse engineering gene networks using singular value decomposition and robust regression
Yeung, M. K. S., Tegnér, J., & Collins, J. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.092576199 · Cited by 667
Human protein reference database--2006 update
Mishra, G. R. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj141 · Cited by 601
Benson, D. A., Karsch-Mizrachi, I., Clark, K., Lipman, D. J., Ostell, J., & Sayers, E. W. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1202 · Cited by 589
DIANA-microT web server: elucidating microRNA functions through target prediction
Maragkakis, M., Reczko, M., Simossis, V. A., Alexiou, P., Papadopoulos, G. L., Dalamagas, T., Giannopoulos, G., Goumas, G., Koukis, E., Kourtis, K., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp292 · Cited by 562
TransmiR: a transcription factor–microRNA regulation database
Wang, J., Lu, M., Qiu, C., & Cui, Q. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp803 · Cited by 404
TRED: a transcriptional regulatory element database, new entries and other development
Jiang, C., Xuan, Z., Zhao, F., & Zhang, M. Q. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl1041 · Cited by 329
Conde, L., Vaquerizas, J. M., Ferrer-Costa, C., de la Cruz, X., Orozco, M., & Dopazo, J. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki476 · Cited by 305
Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power
Ideker, T., Dutkowski, J., & Hood, L. (2011) · Cell · doi:10.1016/j.cell.2011.03.007 · Cited by 205
Zhao, F. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki004 · Cited by 200
RegulonDB: a database on transcriptional regulation in Escherichia coli
Huerta, A. (1998) · Nucleic Acids Research · doi:10.1093/nar/26.1.55 · Cited by 197
MOODS: fast search for position weight matrix matches in DNA sequences
Korhonen, J., Martinmäki, P., Pizzi, C., Rastas, P., & Ukkonen, E. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp554 · Cited by 190
Greenfield, A., Hafemeister, C., & Bonneau, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt099 · Cited by 144
Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data
Liu, Z.-P. (2015) · Current Genomics · doi:10.2174/1389202915666141110210634 · Cited by 80
Self-citationImproved microarray methods for profiling the yeast knockout strain collection
Yuan, D. (2005) · Nucleic Acids Research · doi:10.1093/nar/gni105 · Cited by 65
Literature-based priors for gene regulatory networks
Steele, E., Tucker, A., Hoen, P. A. C. ’., & Schuemie, M. J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp277 · Cited by 39
Liu, Z., Wang, Y., Zhang, X.-S., Xia, W., & Chen, L. (2011) · Molecular BioSystems · doi:10.1039/c0mb00325e · Cited by 35
Self-citationLiu, Z., Wu, H., Zhu, J., & Miao, H. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-336 · Cited by 35
Self-citationLiu, Z., Zhang, W., Horimoto, K., & Chen, L. (2013) · IET Systems Biology · doi:10.1049/iet-syb.2012.0062 · Cited by 21
Self-citationSig2BioPAX: Java tool for converting flat files to BioPAX Level 3 format
Webb, R. L., & Ma’ayan, A. (2011) · Source Code for Biology and Medicine · doi:10.1186/1751-0473-6-5 · Cited by 8
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
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
