The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Extensive low-affinity transcriptional interactions in the yeast genome
Open accessAbstract
Major experimental and computational efforts are targeted at the characterization of transcriptional networks on a genomic scale. The ultimate goal of many of these studies is to construct networks associating transcription factors with genes via well-defined binding sites. Weaker regulatory interactions other than those occurring at high-affinity binding sites are largely ignored and are not well understood. Here I show that low-affinity interactions are abundant in vivo and quantifiable from current high-throughput ChIP experiments. I develop algorithms that predict DNA-binding energies from sequences and ChIP data across a wide dynamic range of affinities and use them to reveal widespread functionality of low-affinity transcription factor binding. Evolutionary analysis suggests that binding energies of many transcription factors are conserved even in promoters lacking classical binding sites. Gene expression analysis shows that such promoters can generate significant expression. I estimate that while only a small percentage of the genome is strongly regulated by a typical transcription factor, up to an order of magnitude more may be involved in weaker interactions. Low-affinity transcription factor–DNA interaction may therefore be important both evolutionarily and functionally.
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Citations
Cites
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441
Transcriptional Regulatory Networks in Saccharomyces cerevisiae
Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) · Science · doi:10.1126/science.1075090 · Cited by 2,956
Cawley, S., Bekiranov, S., Ng, H. H., Kapranov, P., Sekinger, E. A., Kampa, D., Piccolboni, A., Sementchenko, V., Cheng, J., Williams, A. J., et al. (2004) · Cell · doi:10.1016/s0092-8674(04)00127-8 · Cited by 1,095
Jia, Y., Rothermel, B., Thornton, J., & Butow, R. A. (1997) · Molecular and Cellular Biology · doi:10.1128/mcb.17.3.1110 · Cited by 219
Cited by
Absence of a simple code: how transcription factors read the genome
Slattery, M., Zhou, T., Yang, L., Dantas Machado, A. C., Gordân, R., & Rohs, R. (2014) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2014.07.002 · Cited by 537
Evaluation of methods for modeling transcription factor sequence specificity
DREAM5 Consortium, Weirauch, M. T., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T. R., Saez-Rodriguez, J., Cokelaer, T., Vedenko, A., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2486 · Cited by 381
Cnidarian Cell Type Diversity and Regulation Revealed by Whole-Organism Single-Cell RNA-Seq
Sebé-Pedrós, A., Saudemont, B., Chomsky, E., Plessier, F., Mailhé, M.-P., Renno, J., Loe-Mie, Y., Lifshitz, A., Mukamel, Z., Schmutz, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.019 · Cited by 330
Self-citationImpulse Control: Temporal Dynamics in Gene Transcription
Yosef, N., & Regev, A. (2011) · Cell · doi:10.1016/j.cell.2011.02.015 · Cited by 258
Protein-DNA binding: complexities and multi-protein codes
Siggers, T., & Gordân, R. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1112 · Cited by 229
In pursuit of design principles of regulatory sequences
Levo, M., & Segal, E. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3684 · Cited by 215
Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument
Nutiu, R., Friedman, R. C., Luo, S., Khrebtukova, I., Silva, D., Li, R., Zhang, L., Schroth, G. P., & Burge, C. B. (2011) · Nature Biotechnology · doi:10.1038/nbt.1882 · Cited by 194
Transcription factor family-specific DNA shape readout revealed by quantitative specificity models
Yang, L., Orenstein, Y., Jolma, A., Yin, Y., Taipale, J., Shamir, R., & Rohs, R. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167238 · Cited by 126
Habib, N., Wapinski, I., Margalit, H., Regev, A., & Friedman, N. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.50 · Cited by 50
Wang, J., Malecka, A., Trøen, G., & Delabie, J. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s7-s12 · Cited by 31
“If stringent criteria are applied, many potentially functional TF binding sites with weak binding (i.e. low binding affinities or low ChIP-seq tag count) may be eliminated [2].”
Background
Metadata
- Authors
- Tanay, A.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2006-06-29
- Volume
- 16
- Issue
- 8
- Pages
- 962-972
- ISSN
- 1088-9051 (ISSN-L)
- OpenAlex
- W2013449615
- Cited by
- 243
- References
- 4
- Type
- posted content
- Language
- en
- Open access
- bronze
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