The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Ribosome profiling reveals resemblance between long non-coding RNAs and 5′ leaders of coding RNAs
Open accessAbstract
Large-scale genomics and computational approaches have identified thousands of putative long non-coding RNAs (lncRNAs). It has been controversial, however, as to what fraction of these RNAs is truly non-coding. Here, we combine ribosome profiling with a machine-learning approach to validate lncRNAs during zebrafish development in a high throughput manner. We find that dozens of proposed lncRNAs are protein-coding contaminants and that many lncRNAs have ribosome profiles that resemble the 5′ leaders of coding RNAs. Analysis of ribosome profiling data from embryonic stem cells reveals similar properties for mammalian lncRNAs. These results clarify the annotation of developmental lncRNAs and suggest a potential role for translation in lncRNA regulation. In addition, our computational pipeline and ribosome profiling data provide a powerful resource for the identification of translated open reading frames during zebrafish development.
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Cites
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1219 · Cited by 33,055
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
Landscape of transcription in human cells
Djebali, S., Davis, C. A., Merkel, A., Dobin, A., Lassmann, T., Mortazavi, A., Tanzer, A., Lagarde, J., Lin, W., Schlesinger, F., et al. (2012) · Nature · doi:10.1038/nature11233 · Cited by 5,187
Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals
Guttman, M., Amit, I., Garber, M., French, C., Lin, M. F., Feldser, D., Huarte, M., Zuk, O., Carey, B. W., Cassady, J. P., et al. (2009) · Nature · doi:10.1038/nature07672 · Cited by 4,098
Self-citationGenome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S., & Weissman, J. S. (2009) · Science · doi:10.1126/science.1168978 · Cited by 3,853
Ingolia, N. T., Lareau, L. F., & Weissman, J. S. (2011) · Cell · doi:10.1016/j.cell.2011.10.002 · Cited by 2,187
Guttman, M., Garber, M., Levin, J. Z., Donaghey, J., Robinson, J., Adiconis, X., Fan, L., Koziol, M. J., Gnirke, A., Nusbaum, C., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1633 · Cited by 1,253
Self-citationCalvo, S. E., Pagliarini, D. J., & Mootha, V. K. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810916106 · Cited by 914
Ribosome Profiling Shows That miR-430 Reduces Translation Before Causing mRNA Decay in Zebrafish
Bazzini, A. A., Lee, M. T., & Giraldez, A. J. (2012) · Science · doi:10.1126/science.1215704 · Cited by 709 · cited 1×
“Our dataset increases the depth of previous ribosome profiling datasets in zebrafish by an order of magnitude (Bazzini et al., 2012) D E V E L O P M E N T identification of potential protein isoforms, furthering ongoing efforts to refine zebrafish genome annotation (Kettleborough et al., 2013).”
MethodHigh-Resolution View of the Yeast Meiotic Program Revealed by Ribosome Profiling
Brar, G. A., Yassour, M., Friedman, N., Regev, A., Ingolia, N. T., & Weissman, J. S. (2012) · Science · doi:10.1126/science.1215110 · Cited by 533
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LNCcation: lncRNA localization and function
Bridges, M. C., Daulagala, A. C., & Kourtidis, A. (2021) · Journal of Cell Biology · doi:10.1083/jcb.202009045 · Cited by 1,556
Genetic compensation: A phenomenon in search of mechanisms
El-Brolosy, M. A., & Stainier, D. Y. R. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006780 · Cited by 829
Ribosome profiling: new views of translation, from single codons to genome scale
Ingolia, N. T. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3645 · Cited by 632
Long non-coding RNAs as a source of new peptides
Ruiz-Orera, J., Messeguer, X., Subirana, J. A., & Alba, M. M. (2014) · eLife · doi:10.7554/elife.03523 · Cited by 543
Ribosome Footprint Profiling of Translation throughout the Genome
Ingolia, N. T. (2016) · Cell · doi:10.1016/j.cell.2016.02.066 · Cited by 409
An update on LNCipedia: a database for annotated human lncRNA sequences
Volders, P.-J., Verheggen, K., Menschaert, G., Vandepoele, K., Martens, L., Vandesompele, J., & Mestdagh, P. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1060 · Cited by 355
Upstream ORFs are prevalent translational repressors in vertebrates
Johnstone, T. G., Bazzini, A. A., & Giraldez, A. J. (2016) · The EMBO Journal · doi:10.15252/embj.201592759 · Cited by 346
Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer
Ouspenskaia, T., Law, T., Clauser, K. R., Klaeger, S., Sarkizova, S., Aguet, F., Li, B., Christian, E., Knisbacher, B. A., Le, P. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01021-3 · Cited by 298
Self-citationExtensive identification and analysis of conserved small ORFs in animals
Mackowiak, S. D., Zauber, H., Bielow, C., Thiel, D., Kutz, K., Calviello, L., Mastrobuoni, G., Rajewsky, N., Kempa, S., Selbach, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0742-x · Cited by 227
High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies
Rabani, M., Raychowdhury, R., Jovanovic, M., Rooney, M., Stumpo, D. J., Pauli, A., Hacohen, N., Schier, A. F., Blackshear, P. J., Friedman, N., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.015 · Cited by 226
Self-citationVerbruggen, S., Ndah, E., Van Criekinge, W., Gessulat, S., Kuster, B., Wilhelm, M., Van Damme, P., & Menschaert, G. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.001218 · Cited by 45
Zebrafish Ski7 tunes RNA levels during the oocyte-to-embryo transition
Cabrera-Quio, L. E., Schleiffer, A., Mechtler, K., & Pauli, A. (2021) · PLOS Genetics · doi:10.1371/journal.pgen.1009390 · Cited by 18
Self-citationThe zebrafish mutant dreammist implicates sodium homeostasis in sleep regulation
(2020) · doi:10.1101/2020.11.18.388736 · Cited by 4
The zebrafish mutant dreammist implicates sodium homeostasis in sleep regulation
Barlow, I. L., Mackay, E., Wheater, E., Goel, A., Lim, S., Zimmerman, S., Woods, I., Prober, D. A., & Rihel, J. (2023) · doi:10.7554/elife.87521.2
Metadata
- Authors
- Chew, G.-L., Pauli, A., Rinn, J. L., Regev, A., Schier, A. F., & Valen, E.
- Venue
- Development
- Publisher
- The Company of Biologists
- Published
- 2013-07-01
- Volume
- 140
- Issue
- 13
- Pages
- 2828-2834
- ISSN
- 0950-1991 (ISSN-L)
- 1477-9129
- OpenAlex
- W2092987085
- Cited by
- 274
- References
- 10
- FWCI
- 12.51
- Type
- journal article
- Language
- en
- Open access
- green
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