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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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Zhou, B., Ji, B., Liu, K., Hu, G., Wang, F., Chen, Q., Ru, Y., Huang, P., Ren, J., Guo, C., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1076 · Cited by 72
Self-citationBeyond CLIP: advances and opportunities to measure RBP–RNA and RNA–RNA interactions
Lin, C., & Miles, W. O. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz295 · Cited by 72
Data Science Issues in Studying Protein–RNA Interactions with CLIP Technologies
Chakrabarti, A. M., Haberman, N., Praznik, A., Luscombe, N. M., & Ule, J. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013525 · Cited by 71
Pervouchine, D. D., Popov, I., Berry, A., Borsari, B., Frankish, A., & Guigó, R. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz193 · Cited by 71
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Tichon, A., Perry, R. B., Stojic, L., & Ulitsky, I. (2018) · Genes & Development · doi:10.1101/gad.309138.117 · Cited by 71
MTSplice predicts effects of genetic variants on tissue-specific splicing
Cheng, J., Çelik, M. H., Kundaje, A. B., & Gagneur, J. (2021) · Genome biology · doi:10.1186/s13059-021-02273-7 · Cited by 68
First Small-Molecule Inhibitors Targeting the RNA-Binding Protein IGF2BP2/IMP2 for Cancer Therapy
Dahlem, C., Abuhaliema, A., Kessler, S. M., Kröhler, T., Zoller, B. G. E., Chanda, S., Wu, Y., Both, S., Müller, F., Lepikhov, K., et al. (2022) · ACS Chemical Biology · doi:10.1021/acschembio.1c00833 · Cited by 70
The potential of long noncoding RNA therapies
Mercer, T. R., Munro, T. P., & Mattick, J. S. (2022) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.01.008 · Cited by 72
Prediction of RNA–protein interactions using a nucleotide language model
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Villanueva, E., Smith, T. S., Pizzinga, M., Elzek, M., Queiroz, R. M. L., Harvey, R. F., Breckels, L. M., Crook, O. M., Monti, M., Dezi, V., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02101-9 · Cited by 78
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Graindorge, A., Pinheiro, I., Nawrocka, A., Mallory, A. C., Tsvetkov, P., Gil, N., Carolis, C., Buchholz, F., Ulitsky, I., Heard, É., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13235-w · Cited by 68
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CCT3- LINC00326 axis regulates hepatocarcinogenic lipid metabolism
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Alfonso-Gonzalez, C., & Hilgers, V. (2024) · Trends in Cell Biology · doi:10.1016/j.tcb.2024.02.010 · Cited by 67
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Maier, K. C., Gressel, S., Cramer, P., & Schwalb, B. (2020) · Genome Research · doi:10.1101/gr.257857.119 · Cited by 64
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Sidibé, H., Khalfallah, Y., Xiao, S., Gómez, N., Fakim, H., Tank, E. M. H., Tomasso, G. D., Bareke, E., Aulas, A., McKeever, P. M., et al. (2021) · Brain · doi:10.1093/brain/awab217 · Cited by 75
Rao, S. J., Hoskins, I., Tonn, T., Garcia, P. D., Özadam, H., Cenik, E. S., & Cenik, C. (2021) · RNA · doi:10.1261/rna.078661.120 · Cited by 63
Witkowski, M. T., Lee, S., Wang, E., Lee, A. K., Talbot, A., Ma, C., Tsopoulidis, N., Brumbaugh, J., Zhao, Y., Roberts, K. G., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01314-y · Cited by 62
RNA G-quadruplex structures control ribosomal protein production
Varshney, D., Cuesta, S. M., Herdy, B., Abdullah, U. B., Tannahill, D., & Balasubramanian, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-01847-6 · Cited by 56
Cortés-López, M., Schulz, L., Enculescu, M., Paret, C., Spiekermann, B., Quesnel-Vallières, M., Torres-Diz, M., Unic, S., Busch, A., Orekhova, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31818-y · Cited by 65
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Pereira‐Castro, I., & Moreira, A. (2021) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1653 · Cited by 60
Krach, F., Stemick, J., Boerstler, T., Weiss, A., Lingos, I., Reischl, S., Meixner, H., Ploetz, S., Farrell, M., Hehr, U., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34419-x · Cited by 61
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Ries, R. J., Pickering, B. F., Poh, H. X., Namkoong, S., & Jaffrey, S. R. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01089-2 · Cited by 62
Daniels, N. J., Hershberger, C. E., Gu, X., Schueger, C., DiPasquale, W. M., Brick, J., Saunthararajah, Y., Maciejewski, J. P., & Padgett, R. A. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108989 · Cited by 58
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Nabeel‐Shah, S., Pu, S., Burns, J. D., Braunschweig, U., Ahmed, N., Burke, G. L., Lee, H., Radovani, E., Zhong, G., Tang, H., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.08.037 · Cited by 61
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Ross, C. J., Rom, A., Spinrad, A., Gelbard-Solodkin, D., Degani, N., & Ulitsky, I. (2021) · Genome biology · doi:10.1186/s13059-020-02247-1 · Cited by 57
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Self-citationXu, Z., Sziraki, A., Lee, J., Zhou, W., & Cao, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01948-9 · Cited by 52
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Huang, S., Luo, K., Jiang, L., Zhang, X., Lv, Y., & Li, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-02642-z · Cited by 57
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Choi, Y., Um, B., Na, Y., Kim, J., Kim, V. N., & Kim, V. N. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.03.012 · Cited by 60
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Jin, W., Brannan, K. W., Kapeli, K., Park, S. S., Tan, H. Q., Gosztyla, M. L., Mujumdar, M., Ahdout, J., Henroid, B., Rothamel, K., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.06.019 · Cited by 52
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Zeng, Y., Fair, B. J., Zeng, H., Krishnamohan, A., Hou, Y., Hall, J. M., Ruthenburg, A. J., Li, Y., & Staley, J. P. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.11.004 · Cited by 52
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Xie, L., Zhang, Q., Mao, J., Zhang, J., & Li, L. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.680713 · Cited by 50
Liu, Q., Zhang, K., Kang, Y., Li, Y., Deng, P., Li, Y., Tian, Y., Sun, Q., Tang, Y., Xu, K., et al. (2022) · Science Advances · doi:10.1126/sciadv.add6391 · Cited by 48
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Grinev, V. V., Barneh, F., Ilyushonak, I. M., Nakjang, S., Smink, J., van Oort, A., Clough, R., Seyani, M., McNeill, H. V., Reza, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20848-z · Cited by 49
Babarinde, I. A., Ma, G., Li, Y., Deng, B., Luo, Z., Liu, H., Abdul, M. M., Ward, C., Chen, M., Fu, X., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab710 · Cited by 49
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Boyle, E. A., Her, H.-L., Mueller, J. R., Naritomi, J. T., Nguyen, G. G., & Yeo, G. W.-M. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100317 · Cited by 49
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Gosztyla, M. L., Zhan, L., Olson, S., Wei, X., Naritomi, J. T., Nguyen, G. G., Street, L. A., Goda, G. A., Cavazos, F. F., Schmok, J. C., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.08.010 · Cited by 51
Self-citationGuillemin, A., Kumar, A., Wencker, M., & Ricci, E. P. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2021.796012 · Cited by 48
Non-canonical functions of spliceosome components in cancer progression
Ivanova, O. M., Anufrieva, K. S., Kazakova, A. N., Malyants, I. K., Shnaider, P. V., Lukina, M. M., & Shender, V. О. (2023) · Cell Death and Disease · doi:10.1038/s41419-022-05470-9 · Cited by 45
IGF2BP1, a Conserved Regulator of RNA Turnover in Cancer
Glaß, M., Misiak, D., Bley, N., Müller, S., Hagemann, S., Busch, B., Rausch, A., & Hüttelmaier, S. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.632219 · Cited by 43
Delivery of CRISPR-Cas9 system for screening and editing RNA binding proteins in cancer
Yan, J., Kang, D. D., Turnbull, G., & Dong, Y. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.114042 · Cited by 45
Mikl, M., Eletto, D., Nijim, M., Lee, M., Lafzi, A., Mhamedi, F., David, O., Sain, S. B., Handler, K., & Moor, A. E. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac806 · Cited by 41
Li, Q., Yang, H., Stroup, E. K., Wang, H., & Ji, Z. (2022) · Genome Research · doi:10.1101/gr.276139.121 · Cited by 46
Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP
Manakov, S. A., Shishkin, A. A., Yee, B. A., Shen, K. A., Cox, D. C., Park, S. S., Foster, H. M., Chapman, K. B., Yeo, G. W., & Van Nostrand, E. L. (2022) · doi:10.1101/2022.02.13.480296 · Cited by 43
Self-citationWen, Y., Ma, X., Wang, X., Wang, F., Dong, J., Wu, Y., Lv, C., Liu, K., Zhang, Y., Zhang, Z., et al. (2021) · Theranostics · doi:10.7150/thno.66819 · Cited by 43
Self-citationAn atlas of posttranslational modifications on RNA binding proteins
England, W., Wang, J., Chen, S., Baldi, P., Flynn, R. A., & Spitale, R. C. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac243 · Cited by 43
Isoform-resolved transcriptome of the human preimplantation embryo
Torre, D., Francoeur, N. J., Kalma, Y., Carmel, I. G., Melo, B. S., Deikus, G. Y., Allette, K., Flohr, R., Fridrikh, M., Vlachos, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42558-y · Cited by 43
Hess, N., & Joseph, J. A. (2025) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2024.12.008 · Cited by 41
Circular RNA discovery with emerging sequencing and deep learning technologies
Zhang, J., & Zhao, F. (2025) · Nature Genetics · doi:10.1038/s41588-025-02157-7 · Cited by 44
Alkallas, R., Lajoie, M., Moldoveanu, D., Hoang, K. V., Lefrançois, P., Lingrand, M., Ahanfeshar-Adams, M., Watters, K. A., Spatz, A., Zippin, J. H., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0077-8 · Cited by 43
Alternative Splicing of Pre-mRNA in the Control of Immune Activity
Su, Z., & Huang, D. (2021) · Genes · doi:10.3390/genes12040574 · Cited by 44
Dvir, S., Argoetti, A., Lesnik, C., Roytblat, M., Shriki, K., Amit, M., Hashimshony, T., & Mandel‐Gutfreund, Y. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109198 · Cited by 39
The FASTK family proteins fine-tune mitochondrial RNA processing
Ohkubo, A., Haute, L. V., Rudler, D. L., Stentenbach, M., Steiner, F. A., Rackham, O., Minczuk, M. A., Filipovska, A., & Martinou, J. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009873 · Cited by 42
Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning
Yang, Y., Li, G. L., Pang, K., Cao, W., Zhang, Z., & Li, X. (2024) · Advanced Science · doi:10.1002/advs.202407013 · Cited by 42
From computational models of the splicing code to regulatory mechanisms and therapeutic implications
Capitanchik, C., Wilkins, O. G., Wagner, N., Gagneur, J., & Ule, J. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00774-2 · Cited by 45
Translational remodeling by RNA ‐binding proteins and noncoding RNAs
Ho, J. J. D., Man, J. H. S., Schatz, J. H., & Marsden, P. A. (2021) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1647 · Cited by 45
Emerging Roles of RNA-Binding Proteins in Neurodevelopment
Parra, A. S., & Johnston, C. A. (2022) · Journal of Developmental Biology · doi:10.3390/jdb10020023 · Cited by 40
Profiling of RNA-binding protein binding sites by in situ reverse transcription-based sequencing
Xiao, Y., Chen, Y.-M., Zou, Z., Ye, C., Dou, X., Wu, J., Liu, C., Liu, S., Yan, H., Wang, P., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02146-w · Cited by 39
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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