Our database enables a global view of the protein–RNA interactome. RNAct currently covers the human, mouse and yeast genomes and contains a total of 5.87 billion pairwise interactions, reflecting nearly 120 years of computation time on the CRG's high-performance computing cluster. It combines experimentally identified interactions (e.g. from ENCODE) with ab initio predictions, enabling full coverage of the RNA-binding proteome. An in-depth description of our protein–RNA interaction prediction algorithm (catRAPID) is available here.
To compute the interaction propensity scores, we used the catRAPID approach (Bellucci et al., Nature Methods 2011) with the fragmentation procedure (Cirillo et al., RNA 2013; Agostini et al., Nucleic Acids Research 2013) and normalised for sequence lengths similarly to a previous work (Agostini et al., Nucleic Acids Research 2013). ViennaRNA (Gruber et al., Nucleic Acids Research 2008) was used internally for RNA secondary structure prediction. For each protein–RNA pair, the fragment with the maximum interaction propensity score is used to assess overall binding ability (Figure 1, Left). We stress that the method was trained on X-ray and NMR data and that its performance on the experimental eCLIP data (212,256 high-confidence interactions observed in all available eCLIP replicates, against a background sampled from slightly over 2 billion protein–RNA pairs) reflects its predictive power (Figure 1, Right). More details on our prediction method are available here.
Why study the protein–RNA interactome?RNA-binding proteins (RBPs) are implicated in a number of physiological and pathological processes, with molecular mechanisms ranging from defects in splicing, localisation and translation to the formation of aggregates (Marchese et al. 2016). Examples include heterogeneous and life-threatening diseases such as amyotrophic lateral sclerosis, spinocerebellar ataxia and retinitis pigmentosa, among others (Markmiller et al. 2018). The RNAct database reports — for the first time — the map of all possible protein–RNA interactions in the human, mouse and yeast genomes.
How many RBPs are there? What are the specific binding partners of each RBP?Around 1,400 human proteins have been experimentally determined to bind RNA (Hentze et al. 2018). This catalogue has quickly expanded recently thanks to biochemical advances. Many proteins contain one or more RNA-binding regions, either in the form of canonical globular domains or of more recently discovered, intrinsically disordered RNA binding regions. Additionally, protein-protein interaction interfaces and even enzyme active sites are sometimes employed for RNA binding. In the RNAct database we provide the interactome of each protein and RNA in the genome, even where experimental evidence of RNA binding activity is not (yet) available. We report all protein–RNA interactions revealed by the ENCODE project for currently 150 proteins (Van Nostrand et al. 2016), and show that the agreement with our predictions is particularly striking (Figure 1, Right), which indicates that our database may be of great value to the scientific community.
Are the predictions accurate enough?Our group developed the catRAPID approach (Bellucci et al. 2011, Cirillo et al. 2017), which is the most accurate ab initio method for prediction of protein–RNA interactions, based on X-ray and NMR structures. catRAPID and its variants have been run more than 130,000 times by external users and provide a score for the association of a protein–RNA pair (details are available here). The agreement between the original catRAPID approach (Bellucci et al. 2011) and the eCLIP experiments (Van Nostrand et al. 2016) is strong (AUC=0.72, see Figure 1, Right), and we stress that our method was trained on selected X-ray and NMR structures, not on recent high-throughput data.
What type of sequences do you use?We run our protein–RNA interaction predictions on mature spliced transcripts as annotated by GENCODE and Ensembl, including 5' and 3' UTRs. Including introns would increase the computation time needed by a large factor. The eCLIP data, however, includes peaks within introns as well as in the 5' and 3' UTRs.
What will be our future directions?We plan to cover the major model organisms, particularly C. elegans, Drosophila melanogaster and Arabidopsis thaliana, in the near future. Our research targets genetic disease risk via variants in the human protein–RNA interactome and is enabled by recent data on experimentally determined protein–RNA interactions (Marchese et al. 2017). We use predictions to explore protein–RNA interactomes in a genome-wide manner, beyond current experimental data.
UpdatesFeel free to follow us on Twitter at @tartaglialab for updates.
ContactPlease feel free to email Ben Lang (firstname.lastname@example.org) and Gian Gaetano Tartaglia (email@example.com) — any questions, ideas, doubts and feedback are very welcome.
How to cite RNActPlease reference Lang, B., Armaos, A., and Tartaglia, G.G. (2019). RNAct: Protein-RNA interaction predictions for model organisms with supporting experimental data. Nucleic Acids Res. 47, D601–D606.
- Agostini, F., Cirillo, D., Bolognesi, B., and Tartaglia, G.G. (2013) X-inactivation: quantitative predictions of protein interactions in the Xist network. Nucleic Acids Res., 41(1):e31.
- Bellucci, M., Agostini, F., Masin, M. and Tartaglia, G.G. (2011) Predicting protein associations with long noncoding RNAs. Nat. Methods, 8, 444–445.
- Cirillo, D., Agostini, F., Klus, P., Marchese, D., Rodriguez, S., Bolognesi, B. and Tartaglia, G.G. (2013) Neurodegenerative diseases: quantitative predictions of protein-RNA interactions. RNA, 19(2), 129–40.
- Cirillo, D., Blanco, M., Armaos, A., Buness, A., Avner, P., Guttman, M., Cerase, A. and Tartaglia, G.G. (2017) Quantitative predictions of protein interactions with long noncoding RNAs. Nat. Methods, 14, 5–6.
- Gruber, A.R., Lorenz, R., Bernhart, S.H., Neuböck, R., and Hofacker, I.L. (2008). The Vienna RNA websuite. Nucleic Acids Res 36, W70–W74.
- Hentze, M.W., Castello, A., Schwarzl, T. and Preiss, T. (2018) A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol., 19, 327–341.
- Marchese, D., de Groot, N.S., Lorenzo Gotor, N., Livi, C.M. and Tartaglia, G.G. (2016) Advances in the characterization of RNA-binding proteins. Wiley Interdiscip. Rev. RNA, 7, 793–810.
- Marchese, D., Botta-Orfila, T., Cirillo, D., Rodriguez, J.A., Livi, C.M., Fernández-Santiago, R., Ezquerra, M., Martí, M.J., Bechara, E., Tartaglia, G.G., et al. (2017) Discovering the 3′ UTR-mediated regulation of alpha-synuclein. Nucleic Acids Res., 45, 12888–12903.
- Markmiller, S., Soltanieh, S., Server, K.L., Mak, R., Jin, W., Fang, M.Y., Luo, E.-C., Krach, F., Yang, D., Sen, A., et al. (2018) Context-Dependent and Disease-Specific Diversity in Protein Interactions within Stress Granules. Cell, 172, 590-604.e13.
- Van Nostrand, E.L., Pratt, G.A., Shishkin, A.A., Gelboin-Burkhart, C., Fang, M.Y., Sundararaman, B., Blue, S.M., Nguyen, T.B., Surka, C., Elkins, K., et al. (2016) Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods, 13, 508–514.
Primary data sources
- APPRIS: Rodriguez, J.M., Rodriguez-Rivas, J., Di Domenico, T., Vázquez, J., Valencia, A., and Tress, M.L. (2018). APPRIS 2017: principal isoforms for multiple gene sets. Nucleic Acids Res 46, D213–D217.
Please see here for an explanation of the APPRIS isoform classes (P1–5, ALT1–2).
- Ensembl: Zerbino, D.R., Achuthan, P., Akanni, W., Amode, M.R., Barrell, D., Bhai, J., Billis, K., Cummins, C., Gall, A., Girón, C.G., et al. (2018). Ensembl 2018. Nucleic Acids Res 46, D754–D761.
Please see here for an explanation of the Transcript Support Level (TSL) classes (TSL 1–5).
- ENCODE: Sloan, C.A., Chan, E.T., Davidson, J.M., Malladi, V.S., Strattan, J.S., Hitz, B.C., Gabdank, I., Narayanan, A.K., Ho, M., Lee, B.T., et al. (2016). ENCODE data at the ENCODE portal. Nucleic Acids Res 44, D726–D732.
- GENCODE: Harrow, J., Frankish, A., Gonzalez, J.M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B.L., Barrell, D., Zadissa, A., Searle, S., et al. (2012). GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 22, 1760–1774.
Please see here for an explanation of the GENCODE "basic" subset of transcripts.
- HGNC: Yates, B., Braschi, B., Gray, K.A., Seal, R.L., Tweedie, S., and Bruford, E.A. (2017). Genenames.org: the HGNC and VGNC resources in 2017. Nucleic Acids Res 45, D619–D625.
- Human eCLIP data: Van Nostrand, E.L., Pratt, G.A., Shishkin, A.A., Gelboin-Burkhart, C., Fang, M.Y., Sundararaman, B., Blue, S.M., Nguyen, T.B., Surka, C., Elkins, K., et al. (2016) Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods, 13, 508–514.
- RNALocate: Zhang, T., Tan, P., Wang, L., Jin, N., Li, Y., Zhang, L., Yang, H., Hu, Z., Zhang, L., Hu, C., et al. (2017). RNALocate: a resource for RNA subcellular localizations. Nucleic Acids Res 45, D135–D138.
- Yeast RIP-Chip data: Mittal, N., Scherrer, T., Gerber, A.P., and Janga, S.C. (2011). Interplay between posttranscriptional and posttranslational interactions of RNA-binding proteins. J. Mol. Biol. 409, 466–479.