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Understanding splicing regulation through RNA splicing maps. Trends Genet 27: 89–97. [ PMC free article] [ PubMed] [ Google Scholar] So this is why some row heights are larger than others in this example, due to varying lengths of text in each cell. How to unwrap/overflow text in Google Sheets Ray, D. et al. Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat. Biotechnol. 27, 667–670 (2009). Argonaute CLIP defines a deregulated miR-122-bound transcriptome that correlates with patient survival in human liver cancer. Mol Cell 67: 400–410.e7. [ PMC free article] [ PubMed] [ Google Scholar] Ascano, M., Hafner, M., Cekan, P., Gerstberger, S. & Tuschl, T. Identification of RNA–protein interaction networks using PAR-CLIP. Wiley Interdiscip. Rev. RNA 3, 159–177 (2012).

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NOVA-dependent regulation of cryptic NMD exons controls synaptic protein levels after seizure. eLife 2: e00178. [ PMC free article] [ PubMed] [ Google Scholar] Choder, M. mRNA imprinting: additional level in the regulation of gene expression. Cell. Logist. 1, 37–40 (2011).Miller, M. R., Robinson, K. J., Cleary, M. D. & Doe, C. Q. TU-tagging: cell type-specific RNA isolation from intact complex tissues. Nat. Methods 6, 439–441 (2009). In the image below, you can see that in cell A1, there is a long sentence which does not fit inside the cell. We will select cell A1 for this example. Hafner, M. et al. RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries. RNA 17, 1697–1712 (2011).

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Drewe-Boss, P., Wessels, H.-H. & Ohler, U. omniCLIP: probabilistic identification of protein–RNA interactions from CLIP-seq data. Genome Biol. 19, 183 (2018). Yamaji, M. et al. DND1 maintains germline stem cells via recruitment of the CCR4–NOT complex to target mRNAs. Nature 543, 568–572 (2017). Investigating the RNP composition in higher plants is made difficult by several technical challenges. In contrast to mammalian cell cultures, plant cell cultures cannot be cultivated in monolayers and are of limited use for CLIP techniques; as a result, experiments have mostly been performed in transgenic Arabidopsis plants expressing epitope-tagged RBPs 17, 18. Although the presence of UV-absorbing pigments and secondary metabolites such as chlorophyll and flavonoids can inhibit cross-linking efficiency, UVC-based cross-linking has been successfully applied to whole plants 17, 18. Another obstacle in plants is the rigid cell wall that requires mechanical force and harsh denaturing conditions for efficient cell lysis 137. Moreover, the large amounts of endogenous RNases present in the plant vacuole require the use of RNase inhibitors to prevent extensive RNA degradation during extract preparation (also reported for pancreatic tissue). To ensure a controlled RNase treatment to fragment RNA, RNase treatment is performed after immunoprecipitation of the RNA–protein complexes rather than on the lysate 18. cTag-CLIP well preserves native transcriptome states and has an advantage in profiling sensitive cell types: One distinctive feature of cTag-CLIP is its “built-in” cell type specificity imparted through genetic expression of cell type–restricted Cre in cTag mice ( Fig. 2B). As a result, cTag-CLIP can use the whole tissue as the input material without going through the process of tissue dissociation and cell type purification, which is necessary for common RNA profiling methods such as RNA-seq to achieve cell type specificity. Tissue dissociation and cell type purification disrupts the tissue microenvironment, causes cellular stress, and may alter gene expression ( Cardona et al. 2006; Okaty 2011). Most importantly, cTag-CLIP kept the expression of microglia activation markers and immediate early genes (which respond to cellular stress) low compared with RNA-seq profiling from ex vivo purified microglia. These results show that cTag-CLIP better preserves native transcriptome states of individual cell types and therefore is suitable for accurate in vivo RNA profiling.Even when "Overflow" is selected, if there is data that is present in the cells to the right of the text that is set to "Overflow" the text will be clipped. If you want your text to actually overflow into the cells to the right of their location, then the cells to the right must be empty. Haberman, N. et al. Insights into the design and interpretation of iCLIP experiments. Genome Biol. 18, 7 (2017).

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The RNA modification landscape in human disease. RNA 23: 1754–1769. [ PMC free article] [ PubMed] [ Google Scholar] Reading m 6A in the transcriptome: m 6A-binding proteins. Trends Cell Biol 28: 113–127. [ PMC free article] [ PubMed] [ Google Scholar]Licatalosi, D. D. & Darnell, R. B. RNA processing and its regulation: global insights into biological networks. Nat. Rev. Genet. 11, 75–87 (2010). Numerous other recently developed techniques are capable of performing compartment-specific labelling and analysis of RNA and/or proteins. Some approaches use genetically encoded photosensitizers localized to specific compartments, which mediate the oxidation of proximal guanosines by generating reactive oxygen species after irradiation with visible light 51, 52, 53. Photosensitized guanosines can then be coupled with reactive amino group-containing probes to isolate and quantify localized RNA. Targets of RNA-binding proteins identified by editing The second aspect of CLIP methods in which major variations have been explored involves ligating adapters to the fragmented RNA. By modifying the ligation conditions, it is possible to enable proximity ligation of the two strands of RNA duplexes that are bound by double-strand RNA-binding proteins (dsRBPs). When the two strands get ligated, the resulting sequencing reads are referred to as “hybrid reads,” because they need to be split into two sequences that map to separate loci in the genome. This approach has been exploited to study intermolecular duplexes, such as small nucleolar RNA (snoRNA)–rRNA, messenger RNA–microRNA (mRNA–miRNA) and long noncoding RNA (lncRNA)–mRNA, as well as intramolecular RNA–RNA duplexes bound by dsRBPs ( Chi et al. 2009; Kudla et al. 2011; Imig et al. 2014; Moore et al. 2015; Sugimoto et al. 2015). Further technical details of these protocols, their data analysis, and relationship to other methods to study RNA secondary structure are reviewed in more depth elsewhere ( Sugimoto et al. 2017).

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Tuerk, C. & Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249, 505–510 (1990). As you can see in the image below, the header text in cells A1 and B1 are too long to fit inside the cells, and you cannot see what the columns are labeled as. Rescuing Z + agrin splicing in Nova null mice restores synapse formation and unmasks a physiologic defect in motor neuron firing. Proc Natl Acad Sci 106: 3513–3518. [ PMC free article] [ PubMed] [ Google Scholar] If you want to decide where the lines break (where they go to a new line) in your Google spreadsheet, then you can manually wrap text by inserting a new line inside the cell, exactly where you want it. Badis, G. et al. Diversity and complexity in DNA recognition by transcription factors. Science 324, 1720–1723 (2009).cTag-CLIP achieves desired cell type specificity to provide a powerful means to dissect cell type–specific biology: The ubiquitous expression of PABPC1 presents an ideal test case for the capabilities of cTag-CLIP technology in achieving high cell type specificity. To evaluate the cell type specificity at the anatomical level, we examined the expression patterns of PABP-GFP in brains from four mouse models generated by crossing the cTag-PABP mouse to four Cre drivers with different cell type specificity (excitatory neurons, inhibitory neurons, astrocytes and microglia [ Fig. 2B]). The analyses indeed confirmed the distinct expression patterns of PABP-GFP from the four mouse models and their consistency with the corresponding cell type markers. Next, we evaluated the cell type specificity at the molecular level by analyzing the four resulting RNA profiles from each mouse model generated by cTag-PAPERCLIP. These cTag-PAPERCLIP profiles correlated with independently generated cell type–specific RNA sequencing (RNA-seq) profiles and showed expected segregation of known cell type–specific marker genes by the Cre activities. Overall, these data show that cTag-CLIP indeed accomplishes its goals: (a) specific expression of the tagged RBP in the desired cell populations, and (b) generation of cell type–specific CLIP maps. Importantly, cTag-CLIP data obtained from cTag mouse models are very consistent with data generated in wild-type mouse models through a variety of methods in multiple occasions ( Hwang et al. 2017; Jereb et al. 2018; Saito et al. 2018), showing that the cTag-CLIP approach in general does not interfere with normal RBP biology and hence provides a powerful means for dissecting cell type–specific biology. Fukunaga, T. et al. CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data. Genome Biol. 15, R16 (2014).