The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells
Open accessAbstract
Next-generation sequencing has greatly increased the scope and the resolution of transcriptional regulation study. RNA sequencing (RNA-Seq) and ChIP-Seq experiments are now generating comprehensive data on transcript abundance and on regulator–DNA interactions. We propose an approach for an integrated analysis of these data based on feature extraction of ChIP-Seq signals, principal component analysis, and regression-based component selection. Compared with traditional methods, our approach not only offers higher power in predicting gene expression from ChIP-Seq data but also provides a way to capture cooperation among regulators. In mouse embryonic stem cells (ESCs), we find that a remarkably high proportion of variation in gene expression (65%) can be explained by the binding signals of 12 transcription factors (TFs). Two groups of TFs are identified. Whereas the first group ( E2f1 , Myc , Mycn , and Zfx ) act as activators in general, the second group ( Oct4 , Nanog , Sox2 , Smad1 , Stat3 , Tcfcp2l1 , and Esrrb ) may serve as either activator or repressor depending on the target. The two groups of TFs cooperate tightly to activate genes that are differentially up-regulated in ESCs. In the absence of binding by the first group, the binding of the second group is associated with genes that are repressed in ESCs and derepressed upon early differentiation.
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The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327
Core Transcriptional Regulatory Circuitry in Human Embryonic Stem Cells
Boyer, L. A., Lee, T. I., Cole, M. F., Johnstone, S. E., Levine, S. S., Zucker, J. P., Guenther, M. G., Kumar, R. M., Murray, H. L., Jenner, R. G., et al. (2005) · Cell · doi:10.1016/j.cell.2005.08.020 · Cited by 4,505
Genome-Wide Mapping of in Vivo Protein-DNA Interactions
Johnson, D. S., Mortazavi, A., Myers, R. M., & Wold, B. (2007) · Science · doi:10.1126/science.1141319 · Cited by 2,927
Polycomb complexes repress developmental regulators in murine embryonic stem cells
Boyer, L. A., Plath, K., Zeitlinger, J., Brambrink, T., Medeiros, L. A., Lee, T. I., Levine, S. S., Wernig, M., Tajonar, A., Ray, M. K., et al. (2006) · Nature · doi:10.1038/nature04733 · Cited by 2,514
Chen, X., Xu, H., Yuan, P., Fang, F., Huss, M., Vega, V. B., Wong, E., Orlov, Y. L., Zhang, W., Jiang, J., et al. (2008) · Cell · doi:10.1016/j.cell.2008.04.043 · Cited by 2,501
Control of Developmental Regulators by Polycomb in Human Embryonic Stem Cells
Lee, T. I., Jenner, R. G., Boyer, L. A., Guenther, M. G., Levine, S. S., Kumar, R. M., Chevalier, B., Johnstone, S. E., Cole, M. F., Isono, K.-I., et al. (2006) · Cell · doi:10.1016/j.cell.2006.02.043 · Cited by 2,268
MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126
Singular value decomposition for genome-wide expression data processing and modeling
Alter, O., Brown, P. O., & Botstein, D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.18.10101 · Cited by 1,971
Cited by
A Validated Regulatory Network for Th17 Cell Specification
Ciofani, M., Madar, A., Galan, C., Sellars, M., Mace, K., Pauli, F., Agarwal, A., Huang, W., Parkurst, C. N., Muratet, M., et al. (2012) · Cell · doi:10.1016/j.cell.2012.09.016 · Cited by 1,160
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
Wilson, N. K., Foster, S. D., Wang, X., Knezevic, K., Schütte, J., Kaimakis, P., Chilarska, P. M., Kinston, S., Ouwehand, W. H., Dzierzak, E., et al. (2010) · Cell stem cell · doi:10.1016/j.stem.2010.07.016 · Cited by 728
Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks
Agarwal, V., & Shendure, J. (2018) · doi:10.1101/416685 · Cited by 243
“Among all models examined, those using multiple forms of experimental data, such as TF ChIP, histone ChIP, and Dnase, achieved the best r 2 values of 0.62 (Dong et al., 2012) and 0.70 (McLeay et al., 2012) in human and mouse, respectively.”
BackgroundMethodInfluentialPredicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks
Agarwal, V., & Shendure, J. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107663 · Cited by 243
Metadata
- Authors
- Ouyang, Z., Zhou, Q., & Wong, W. H.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2009-12-22
- Volume
- 106
- Issue
- 51
- Pages
- 21521-21526
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2105924780
- Cited by
- 339
- References
- 39
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- green
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