The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Modeling the specificity of protein‐DNA interactions
Open accessAbstract
The specificity of protein‐DNA interactions is most commonly modeled using position weight matrices (PWMs). First introduced in 1982, they have been adapted to many new types of data and many different approaches have been developed to determine the parameters of the PWM. New high‐throughput technologies provide a large amount of data rapidly and offer an unprecedented opportunity to determine accurately the specificities of many transcription factors (TFs). But taking full advantage of the new data requires advanced algorithms that take into account the biophysical processes involved in generating the data. The new large datasets can also aid in determining when the PWM model is inadequate and must be extended to provide accurate predictions of binding sites. This article provides a general mathematical description of a PWM and how it is used to score potential binding sites, a brief history of the approaches that have been developed and the types of data that are used with an emphasis on algorithms that we have developed for analyzing high‐throughput datasets from several new technologies. It also describes extensions that can be added when the simple PWM model is inadequate and further enhancements that may be necessary. It briefly describes some applications of PWMs in the discovery and modeling of in vivo regulatory networks.
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Systematic Localization of Common Disease-Associated Variation in Regulatory DNA
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Global mapping of protein-DNA interactions in vivo by digital genomic footprinting
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Circuitry and Dynamics of Human Transcription Factor Regulatory Networks
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Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument
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Cited by
JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles
Castro-Mondragon, J. A., Riudavets-Puig, R., Rauluseviciute, I., Berhanu Lemma, R., Turchi, L., Blanc-Mathieu, R., Lucas, J., Boddie, P., Khan, A., Manosalva Pérez, N., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1113 · Cited by 1,935
Mathelier, 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
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
Measuring quantitative effects of methylation on transcription factor–DNA binding affinity
Zuo, Z., Roy, B., Chang, Y. K., Granas, D., & Stormo, G. D. (2017) · Science Advances · doi:10.1126/sciadv.aao1799 · Cited by 86
Self-citationGeometric deep learning of protein–DNA binding specificity
Mitra, R., Li, J., Sagendorf, J. M., Jiang, Y., Cohen, A. S., Chiu, T.-P., Glasscock, C. J., & Rohs, R. (2024) · Nature Methods · doi:10.1038/s41592-024-02372-w · Cited by 75
Metadata
- Authors
- Stormo, G. D.
- Venue
- Quantitative Biology
- Publisher
- Wiley
- Published
- 2013-06
- Volume
- 1
- Issue
- 2
- Pages
- 115-130
- ISSN
- 2095-4689 (ISSN-L)
- 2095-4697
- OpenAlex
- W2102473929
- Cited by
- 221
- References
- 9
- FWCI
- 5.78
- Type
- journal article
- Language
- en
- Open access
- diamond
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