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A large-scale binding and functional map of human RNA-binding proteins
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Abstract
Many proteins regulate the expression of genes by binding to specific regions encoded in the genome 1 . Here we introduce a new data set of RNA elements in the human genome that are recognized by RNA-binding proteins (RBPs), generated as part of the Encyclopedia of DNA Elements (ENCODE) project phase III. This class of regulatory elements functions only when transcribed into RNA, as they serve as the binding sites for RBPs that control post-transcriptional processes such as splicing, cleavage and polyadenylation, and the editing, localization, stability and translation of mRNAs. We describe the mapping and characterization of RNA elements recognized by a large collection of human RBPs in K562 and HepG2 cells. Integrative analyses using five assays identify RBP binding sites on RNA and chromatin in vivo, the in vitro binding preferences of RBPs, the function of RBP binding sites and the subcellular localization of RBPs, producing 1,223 replicated data sets for 356 RBPs. We describe the spectrum of RBP binding throughout the transcriptome and the connections between these interactions and various aspects of RNA biology, including RNA stability, splicing regulation and RNA localization. These data expand the catalogue of functional elements encoded in the human genome by the addition of a large set of elements that function at the RNA level by interacting with RBPs.
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“Previous studies have shown that linear sequence motifs alone are often insufficient to fully capture the binding specificities of RBPs, and other contextual features such as RNA secondary structure and surrounding base composition contribute to binding specificity (Dominguez et al. 2018; Nostrand et al. 2020).”
From ESRP2–microRNA-122 axis promotes the postnatal onset of liver polyploidization and maturation (2025) · cites this paper as Nostrand et al. 2020
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“Similar UG-containing sequence motifs for ESRP proteins have been identified in vitro by SELEX-seq (Dittmar et al. 2012) and RNA bind-n-seq (Nostrand et al. 2020), confirming the accuracy of our in vivo ESRP2-binding site determination with FLAG-ESRP2 eCLIP.”
From ESRP2–microRNA-122 axis promotes the postnatal onset of liver polyploidization and maturation (2025) · cites this paper as Nostrand et al. 2020
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
EMBOSS: the European molecular biology open software suite.
Rice, P., Longden, I., & Bleasby, A. (2000) · Trends in Genetics · doi:10.1016/s0168-9525(00)02024-2 · Cited by 9,466
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Cheng, L. C., Zheng, D., Zhang, Q., Güvenek, A., Cheng, H., & Tian, B. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109407 · Cited by 27
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Sasse, A., Laverty, K. U., Hughes, T. R., & Morris, Q. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.08.001 · Cited by 27
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Theil, K., Herzog, M., & Rajewsky, N. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.02.094 · Cited by 27
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Ronchetti, D., Traini, V., Silvestris, I., Fabbiano, G., Passamonti, F., Bolli, N., & Taìana, E. (2024) · Cancer Gene Therapy · doi:10.1038/s41417-024-00763-x · Cited by 29
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Vandelli, A., Cid-Samper, F., Torrent, M., de Groot, N. S., & Tartaglia, G. G. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167159 · Cited by 26
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Ma, Z., Chen, H., Xia, Z., You, J., Han, C., Wang, S., Xia, W., Bai, Y., Liu, T., Xu, L., et al. (2023) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-023-02723-z · Cited by 25
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Peng, X., Wang, X., Guo, Y., Ge, Z., Li, F., Gao, X., & Song, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac215 · Cited by 24
Self-citation
Metadata
- Authors
- Van Nostrand, E. L., Freese, P., Pratt, G. A., Wang, X., Wei, X., Xiao, R., Blue, S. M., Chen, J.-Y., Cody, N. A. L., Dominguez, D., Olson, S., Sundararaman, B., Zhan, L., Bazile, C., Bouvrette, L. P. B., Bergalet, J., Duff, M. O., Garcia, K. E., Gelboin-Burkhart, C., Hochman, M., Lambert, N. J., Li, H., McGurk, M. P., Nguyen, T. B., Palden, T., Rabano, I., Sathe, S., Stanton, R., Su, A., Wang, R., Yee, B. A., Zhou, B., Louie, A. L., Aigner, S., Fu, X.-D., Lécuyer, E., Burge, C. B., Graveley, B. R., & Yeo, G. W.
- Affiliations
- Massachusetts Institute of Technology (US)
- Montreal Clinical Research Institute (CA)
- UConn Health (US)
- University of California San Diego (US)
- Corresponding author
- Fu, Xiang-Dong
- Burge, Christopher B.
- Graveley, Brenton R.
- Yeo, Gene W.
- ORCID
- Van Nostrand, Eric L.
- Wang, Xiaofeng
- Xiao, Rui
- Blue, Steven M.
- Chen, Jia-Yu
- Olson, Sara
- Sundararaman, Balaji
- Zhan, Lijun
- Bazile, Cassandra
- Bouvrette, Louis Philip Benoit
- Duff, Michael O.
- Hochman, Myles
- McGurk, Michael P.
- Nguyen, Thai B.
- Rabano, Ines
- Sathe, Shashank
- Wang, Ruth
- Yee, Brian A.
- Zhou, Bing
- Aigner, Stefan
- Fu, Xiang-Dong
- Burge, Christopher B.
- Graveley, Brenton R.
- Yeo, Gene W.
- Venue
- Nature
- Published
- 2020-07-29
- Volume
- 583
- Issue
- 7818
- Pages
- 711-719
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7410833
- OpenAlex
- W3046003662
- Cited by
- 1,317
- References
- 94
- Citations by year
- 2026: 193 · 2025: 232 · 2024: 248 · 2023: 170 · 2022: 204 · 2021: 153 · 2020: 55
- FWCI
- 50.08
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-01-05)
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation
- Damon Runyon Cancer Research Foundation (DRG-2172-13)
- National Institutes of Health (HG009530)
- National Human Genome Research Institute (K99HG009530, R00HG009530, R01HG004659, HG009530, U41HG009889, U54HG007005)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- RNA-binding proteins, RBP binding sites, post-transcriptional regulation, splicing regulation, RNA stability, RNA localization, cleavage and polyadenylation, RNA editing, K562 cells, HepG2 cells, human transcriptome
- MeSH terms
- Base Sequence, Binding Sites, Cell Line, Chromatin, Female, Humans, Male, Protein Binding, RNA, Messenger, Substrate Specificity, RNA-Binding Proteins, Alternative Splicing, Databases, Genetic, Intracellular Space, Gene Knockdown Techniques, Transcriptome
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