Liu, S. et al. Classification and function of RNA-protein interactions. Wiley Interdiscip. Rev. RNA 11, e1601 (2020).
Google Scholar
Li, W. et al. Functional roles of enhancer RNAs for oestrogen-dependent transcriptional activation. Nature 498, 516–520 (2013).
Google Scholar
Yang, F. et al. The lncRNA Firre anchors the inactive X chromosome to the nucleolus by binding CTCF and maintains H3K27me3 methylation. Genome Biol. 16, 52 (2015).
Google Scholar
Yin, Y. et al. U1 snRNP regulates chromatin retention of noncoding RNAs. Nature 580, 147–150 (2020).
Google Scholar
Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).
Google Scholar
Thelen, M. P. & Kye, M. J. The role of RNA binding proteins for local mRNA translation: implications in neurological disorders. Front. Mol. Biosci. 6, 161 (2019).
Google Scholar
Li, W., Deng, X. & Chen, J. RNA-binding proteins in regulating mRNA stability and translation: roles and mechanisms in cancer. Semin. Cancer Biol. 86, 664–677 (2022).
Google Scholar
Pederson, T. A layperson encounter, on the ‘modified’ RNA world. Proc. Natl Acad. Sci. USA 118, e2110706118 (2021).
Google Scholar
Chen, L. L. Towards higher-resolution and in vivo understanding of lncRNA biogenesis and function. Nat Methods 19, 1152–1155 (2022).
Google Scholar
García-Mauriño, S. M. et al. RNA binding protein regulation and cross-talk in the control of AU-rich mRNA fate. Front. Mol. Biosci. 4, 71 (2017).
Google Scholar
Sanchez de Groot, N. et al. RNA structure drives interaction with proteins. Nat. Commun. 10, 3246 (2019).
Google Scholar
Russell, R. RNA misfolding and the action of chaperones. Front. Biosci. 13, 1–20 (2008).
Google Scholar
Witten, J. T. & Ule, J. Understanding splicing regulation through RNA splicing maps. Trends Genet. 27, 89–97 (2011).
Google Scholar
Quinones-Valdez, G. et al. Regulation of RNA editing by RNA-binding proteins in human cells. Commun. Biol. 2, 19 (2019).
Maziuk, B., Ballance, H. I. & Wolozin, B. Dysregulation of RNA binding protein aggregation in neurodegenerative disorders. Front. Mol. Neurosci. 10, 89 (2017).
Google Scholar
Stanley, R. F. & Abdel-Wahab, O. Dysregulation and therapeutic targeting of RNA splicing in cancer. Nat. Cancer 3, 536–546 (2022).
Google Scholar
Enguita, F. J. et al. The interplay between lncRNAs, RNA-binding proteins and viral genome during SARS-CoV-2 infection reveals strong connections with regulatory events involved in RNA metabolism and immune response. Theranostics 12, 3946–3962 (2022).
Google Scholar
Ramanathan, M., Porter, D. F. & Khavari, P. A. Methods to study RNA-protein interactions. Nat. Methods 16, 225–234 (2019).
Google Scholar
Gräwe, C., Stelloo, S., van Hout, F. A. H. & Vermeulen, M. RNA-centric methods: toward the interactome of specific RNA transcripts. Trends Biotechnol. 39, 890–900 (2021).
Google Scholar
Garcia-Moreno, M. et al. System-wide profiling of RNA-binding proteins uncovers key regulators of virus infection. Mol. Cell 74, 196–211 (2019).
Google Scholar
Huang, R., Han, M., Meng, L. & Chen, X. Transcriptome-wide discovery of coding and noncoding RNA-binding proteins. Proc. Natl Acad. Sci. USA 115, E3879–E3887 (2018).
Google Scholar
Bao, X. et al. Capturing the interactome of newly transcribed RNA. Nat. Methods 15, 213–220 (2018).
Google Scholar
McHugh, C. A. & Guttman, M. RAP-MS: a method to identify proteins that interact directly with a specific RNA molecule in cells. Methods Mol. Biol. 1649, 473–488 (2018).
Google Scholar
Matia-González, A. M., Iadevaia, V. & Gerber, A. P. A versatile tandem RNA isolation procedure to capture in vivo formed mRNA-protein complexes. Methods 118–119, 93–100 (2017).
Google Scholar
Zeng, F. et al. A protocol for PAIR: PNA-assisted identification of RNA binding proteins in living cells. Nat. Protoc. 1, 920–927 (2006).
Google Scholar
Tsai, B. P., Wang, X., Huang, L. & Waterman, M. L. Quantitative profiling of in vivo-assembled RNA-protein complexes using a novel integrated proteomic approach. Mol. Cell Proteomics 10, M110.007385 (2011).
Google Scholar
Ramanathan, M. et al. RNA-protein interaction detection in living cells. Nat. Methods 15, 207–212 (2018).
Google Scholar
Tsue, A. F. et al. Oligonucleotide-mediated proximity-interactome mapping (O-MAP): a unified method for RNA-targeted microenvironment-mapping in situ. Nat. Methods 21, 2058–2071 (2024).
Google Scholar
Qin, W., Cho, K. F., Cavanagh, P. E. & Ting, A. Y. Deciphering molecular interactions by proximity labeling. Nat. Methods 18, 133–143 (2021).
Google Scholar
Weissinger, R., Heinold, L., Akram, S., Jansen, R. P. & Hermesh, O. RNA proximity labeling: a new detection tool for RNA-protein interactions. Molecules 26, 2270 (2021).
Google Scholar
Zhang, Z. et al. Capturing RNA-protein interaction via CRUIS. Nucleic Acids Res. 48, e52 (2020).
Google Scholar
Li, Y. et al. CBRPP: a new RNA-centric method to study RNA-protein interactions. RNA Biol. 18, 1608–1621 (2021).
Google Scholar
Gilbert, C. & Svejstrup, J. Q. RNA immunoprecipitation for determining RNA-protein associations in vivo. Curr. Protoc. Mol. Biol. Chapter 27, Unit 27.4 (2006).
Google Scholar
Hafner, M. et al. CLIP and complementary methods. Nat. Rev. Methods Primers 1, 20 (2021).
Google Scholar
Hafner, M. et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141, 129–141 (2010).
Google Scholar
König, J. et al. iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat. Struct. Mol. Biol. 17, 909–915 (2010).
Google Scholar
Licatalosi, D. D. et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456, 464–469 (2008).
Google Scholar
Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods. 13, 508–514 (2016).
Google Scholar
Nawaz, A. et al. Serine 970 of RNA helicase MOV10 is phosphorylated and controls unfolding activity and fate of mRNAs targeted for AGO2-mediated. silencing. J. Biol. Chem. 299, 104577 (2023).
Google Scholar
Weyn-Vanhentenryck, S. M. et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 6, 1139–1152 (2014).
Google Scholar
Zarnegar, B. J. et al. irCLIP platform for efficient characterization of protein-RNA interactions. Nat. Methods 13, 489–492 (2016).
Google Scholar
Hinze, F. et al. Expanding the map of protein-RNA interaction sites via cell fusion followed by PAR-CLIP. RNA Biol. 15, 359–368 (2018).
Google Scholar
Gu, J. et al. GoldCLIP: gel-omitted ligation-dependent CLIP. Genomics Proteomics Bioinformatics 16, 136–143 (2018).
Google Scholar
Porter, D. F. et al. easyCLIP analysis of RNA-protein interactions incorporating absolute quantification. Nat. Commun. 12, 1569 (2021).
Google Scholar
McMahon, A. C. et al. TRIBE: hijacking an RNA-editing enzyme to identify cell-specific targets of RNA-binding proteins. Cell 165, 742–753 (2016).
Google Scholar
Rahman, R., Xu, W., Jin, H. & Rosbash, M. Identification of RNA-binding protein targets with HyperTRIBE. Nat. Protoc. 13, 1829–1849 (2018).
Google Scholar
Seo, K. W. & Kleiner, R. E. Profiling dynamic RNA-protein interactions using small-molecule-induced RNA editing. Nat. Chem. Biol. 19, 1361–1371 (2023).
Google Scholar
Johnson, K. L. et al. Revealing protein-protein interactions at the transcriptome scale by sequencing. Mol. Cell. 81, 4091–4103.e9 (2021).
Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).
Google Scholar
Gene Ontology Consortium & Aleksander, S. A. et al. The Gene Ontology knowledgebase in 2023. Genetics. 224, iyad031 (2023).
Google Scholar
Protter, D. S. W. & Parker, R. Principles and properties of stress granules. Trends Cell Biol. 26, 668–679 (2016).
Google Scholar
Caudron-Herger, M., Jansen, R. E., Wassmer, E. & Diederichs, S. RBP2GO: a comprehensive pan-species database on RNA-binding proteins, their interactions and functions. Nucleic Acids Res. 49, D425–D436 (2021).
Google Scholar
Cook, K. B., Kazan, H., Zuberi, K., Morris, Q. & Hughes, T. R. RBPDB: a database of RNA-binding specificities. Nucleic Acids Res. 39, D301–D308 (2011).
Google Scholar
Giudice, G., Sánchez-Cabo, F., Torroja, C. & Lara-Pezzi, E. ATtRACT-a database of RNA-binding proteins and associated motifs. Database 2016, baw035 (2016).
Google Scholar
Ghosh P., Murugavel P. & Sowdhamini R. hRBPome: a central repository of all known human RNA-binding proteins. Preprint at bioRxiv http://biorxiv.org/lookup/doi/10.1101/269043 (2018).
Perez-Perri, J. I. et al. Discovery of RNA-binding proteins and characterization of their dynamic responses by enhanced RNA interactome capture. Nat. Commun. 9, 4408 (2018).
Google Scholar
Li, J. H., Liu, S., Zhou, H., Qu, L. H. & Yang, J. H. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 42, D92–D97 (2014).
Google Scholar
Mullari, M., Lyon, D., Jensen, L. J. & Nielsen, M. L. Specifying RNA-binding regions in proteins by peptide cross-linking and affinity purification. J. Proteome Res. 16, 2762–2772 (2017).
Google Scholar
Castello, A. et al. Comprehensive identification of RNA-binding domains in human cells. Mol. Cell. 63, 696–710 (2016).
Google Scholar
Kang, J. et al. RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility. Nucleic Acids Res. 50, D326–D332 (2022).
Google Scholar
Masuda, A. et al. CUGBP1 and MBNL1 preferentially bind to 3′ UTRs and facilitate mRNA decay. Sci. Rep. 2, 209 (2012).
Google Scholar
Oberstrass, F. C. et al. Structure of PTB bound to RNA: specific binding and implications for splicing regulation. Science. 309, 2054–2057 (2005).
Google Scholar
Barabási, A. L. Scale-free networks: a decade and beyond. Science 325, 412–413 (2009).
Google Scholar
Van Nostrand, E. L. et al. Author correction: a large-scale binding and functional map of human RNA-binding proteins. Nature 589, E5 (2021).
Google Scholar
Ye, H. et al. The SP1-induced long noncoding RNA, LINC00339, promotes tumorigenesis in colorectal cancer via the miR-378a-3p/MED19 axis. Onco. Targets Ther. 13, 11711–11724 (2020).
Google Scholar
Yuan, Y., Haiying, G., Zhuo, L., Ying, L. & Xin, H. Long non-coding RNA LINC00339 facilitates the tumorigenesis of non-small cell lung cancer by sponging miR-145 through targeting FOXM1. Biomed. Pharmacother. 105, 707–713 (2018).
Google Scholar
Stelzer, G. et al. The GeneCards Suite: from gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinformatics 54, 1.30.1–1.30.33 (2016).
Google Scholar
Zhang, W., Xie, M., Shu, M. D., Steitz, J. A. & DiMaio, D. A proximity-dependent assay for specific RNA-protein interactions in intact cells. RNA 22, 1785–1792 (2016).
Google Scholar
Kattah, N. H., Kattah, M. G. & Utz, P. J. The U1-snRNP complex: structural properties relating to autoimmune pathogenesis in rheumatic diseases. Immunol. Rev. 233, 126–145 (2010).
Google Scholar
Reid, M. A., Dai, Z. & Locasale, J. W. The impact of cellular metabolism on chromatin dynamics and epigenetics. Nat. Cell Biol. 19, 1298–1306 (2017).
Google Scholar
Chen, X. et al. PHGDH expression increases with progression of Alzheimer’s disease pathology and symptoms. Cell Metab. 34, 651–653 (2022).
Google Scholar
Liang, X. H. et al. Induction of autophagy and inhibition of tumorigenesis by beclin 1. Nature 402, 672–676 (1999).
Google Scholar
Tran, S., Fairlie, W. D. & Lee, E. F. BECLIN1: protein structure, function and regulation. Cells 10, 1522 (2021).
Google Scholar
Wortel, I. M. N., van der Meer, L. T., Kilberg, M. S. & van Leeuwen, F. N. Surviving stress: modulation of ATF4-mediated stress responses in normal and malignant cells. Trends Endocrinol. Metab. 28, 794–806 (2017).
Google Scholar
Danzi, M. C. et al. The effect of Jun dimerization on neurite outgrowth and motif binding. Mol Cell Neurosci. 92, 114–127 (2018).
Google Scholar
Zhu, H., Yu, H., Zhou, H., Zhu, W. & Wang, X. Elevated nuclear PHGDH synergistically functions with cMyc to reshape the immune microenvironment of liver cancer. Adv Sci. 10, e2205818 (2023).
Google Scholar
Calandrelli, R. et al. Genome-wide analysis of the interplay between chromatin-associated RNA and 3D genome organization in human cells. Nat Commun. 14, 6519 (2023).
Google Scholar
Soloviev, Z. et al. Structural mass spectrometry decodes domain interaction and dynamics of the full-length Human Histone Deacetylase 2. Biochim Biophys Acta Proteins Proteom. 1870, 140759 (2022).
Google Scholar
Jankowsky, E. & Harris, M. E. Specificity and nonspecificity in RNA-protein interactions. Nat. Rev. Mol. Cell Biol. 16, 533–544 (2015).
Google Scholar
Xiao, R. et al. Pervasive chromatin-RNA binding protein interactions enable RNA-based regulation of transcription. Cell 178, 107–121 (2019).
Google Scholar
Dethoff, E. A. & Weeks, K. M. Effects of refolding on large-scale RNA structure. Biochemistry 58, 3069–3077 (2019).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
Google Scholar
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Google Scholar
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).
Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Google Scholar
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
Google Scholar
Thomas, P. D. et al. PANTHER: making genome-scale phylogenetics accessible to all. Protein Sci. 31, 8–22 (2022).
Google Scholar
Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. ICWSM 3, 361–362 (2009).
Google Scholar
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Google Scholar
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell. 38, 576–589 (2010).
Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Google Scholar
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2013).
Google Scholar
Zhijie Q., Shuanghong, X. & Kara J. Genome-wide mapping of RNA-protein associations via sequencing. Datasets. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE270010 (2025).
Chen, J., Zhao, W., Qi, Z. & Wen, X. Identification of PHGDH protein-assocaited RNAs and their overlap with PRIM-seq derived RNAs through RIP-seq. Datasets. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE270009 (2025).
Qi, Z. PRIMseqTools. Source code. GitHub https://github.com/Zhong-Lab-UCSD/PRIMseqTools.git (2025).
