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Quantitative Predictions of Protein Interactions with Long Noncoding CORRESPONDENCE Exploiting sequence information, this algorithm integrates local Quantitative predictions of protein properties of protein and RNA structures into an overall binding interactions with long noncoding RNAs propensity (see Supplementary Methods and Fig. 1a). Calibrated on high-throughput data (training, 14 PAR-CLIP or HITS-CLIP experi- To the Editor: Long noncoding RNAs (lncRNAs, which comprise ments; testing, 8 protein arrays), Global Score is the first approach 68% of the human transcriptome with average length of 1,000 nt) to quantitatively predict RBP partners of RNA >1,000 nt (>80% dis- interact with various RNA-binding proteins (RBPs) to mediate crimination between interacting and noninteracting pairs; Fig. 1b, cellular functions1. Identification of lncRNA interactions through Supplementary Fig. 1a, and Supplementary Tables 1–4). Large-scale experimental methods is challenging and can be complemented by analysis of eCLIP assays (>17,000 RBPs–RNAs)3 reveals that Global the use of computational models to predict protein partners. Yet it Score performances significantly increase with the binding strength is currently impractical to calculate RBP–lncRNA interactions for (P values < 10–10; Wilcoxon signed-rank test; Fig. 1c, Supplementary large transcripts2.We introduce Global Score as a means to predict Methods, Supplementary Fig. 2, and Supplementary Table 5). protein interactions with large RNAs (http://service.tartaglialab. Using >600 RBPs available from five proteomic and genomic com/new_submission/globalscore). screens, we investigated Xist interactions (Supplementary Methods, acb RBP 0 lncRNAlncRNA 1.0 CBX5 IGF2BP1 81 Binding site .8 RNF213 60 Rate 0. ve Global Score siti Po 0.4 v ue Tr rue-positive rate T .2 Training set Test set Read counts (log scale) training set 0246 eCLIP 00 testing set 020406080 100 00.2 0.40.6 0.81.0 False-positive rate Predicted interactions (%) d P = 0.0226 e 3 Ptbp1 -6 2 1 P = 5.41 × 10 11 Lbr 1 232 Spen P = 0.213 1 223 HnrnpK 456 231 6 6 HnrnpU Dkc1 A A F H B C D E [0–2126] Ptbp1 [0 - 2126] 1.0 Ptbp1 Pp == 0.00190.0019 [0–1,083] Lbr Lbr 0.8 P = 0.0001 Spen Spen [0–527] P = 0.0033 0.6 HnrnpK [0–18964] HnrnpK P = 0.0219 Global Score 0.4 HnrnpU [0–51] HnrnpU P = 0.2576 0.2 [0–20] Dkc1 Dkc1 0 1 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 1234 Number of overlapping experiments XistXist sequence sequence Figure 1 | Training and testing of the Global Score for prediction of protein interactions with large RNAs. (a) Global Score predicts RBP interactions with large RNAs. (b) Training (green curve, area under the ROC curve (AUC) = 0.84) and testing (black curve, AUC = 0.80) performances. The dashed line indicates performances of a random predictor (AUC = 0.50). (c) Transcript analysis: interaction-prone RBPs (continuous lines) increase from low to high read counts, while low-propensity RBPs (dashed lines) decrease (P values < 10-30; three RNA shown; eCLIP assays). (d) Global Score predictions correlate with the number of experiments reporting Xist interaction with a specific RBP (green line, significance at P value < 0.01). Box plot limits are upper and lower quartiles; center lines represents the median and whiskers indicate minimum and maximum values. (e) Agreement between predicted binding sites (top; numbers and shading represent the ranking of the predictions) and eCLIP experiments (bottom; read counts and significance of the match). Xist tandem repeats are indicated with orange boxes and letters (A, F, H, B, C, D, and E). Nucleotide coordinates are reported at the bottom. NATURE METHODS | VOL.14 NO.1 | JANUARY 2017 | 5 CORRESPONDENCE Supplementary Fig. 1b, and Supplementary Tables 1–3, 6 and Note: Any Supplementary Information and Source Data files are available in the 7)4–8. Global Score predictions correlate with the number of inde- online version of the paper. pendent experiments reporting Xist association with a specific ACKNOWLEDGMENTS protein (Fig. 1d) and identify 38 high-confidence RBPs (empiri- We thank D. Whitworth and K. Havas for critical reading of the manuscript, the cal P value < 0.01) reported in two or more independent experi- European Research Council (RIBOMYLOME_309545), MINECO (BFU2014-55054-P), ments (Supplementary Fig. 3). We used eCLIP to test Xist bind- Centro de Excelencia Severo Ochoa 2013–2017, and the European Molecular Biology Laboratory Grant-50800. ing to the RBPs HnrnpK (Global Score of 0.99), Ptbp1 (0.99), Lbr (0.79), HnrnpU (Saf-A) (0.66), Spen (Sharp) (0.59; Supplementary COMPETING FINANCIAL INTERESTS Methods and Supplementary Figs. 4 and 5), and negative control The authors declare no competing financial interests. Dkc1 (0.01). Global Score prediction correlates with the eCLIP binding profile (Pearson correlation = 0.93; Supplementary Fig. 6): Davide Cirillo1,2, Mario Blanco3, Alexandros Armaos1,2, Spen and HnrnpK, Ptbp1, and Lbr interact respectively with A, B, Andreas Buness4, Philip Avner4, Mitchell Guttman3, and E repeats and adjacent regions, while HnrnpU binds across the Andrea Cerase4 & Gian Gaetano Tartaglia1,2,5 whole transcript, and Dkc1 does not interact with Xist (Fig. 1e and Supplementary Table 8). 1Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and The ability to predict global (overall binding ability; Fig. 1b) and Technology, Barcelona, Spain. 2Universitat Pompeu Fabra (UPF), Barcelona, local (protein and RNA contacts; Fig. 1e) interactions makes Global Spain. 3Division of Biology and Biological Engineering, California Institute of Score the first computational algorithm (Supplementary Fig. 1) to Technology, Pasadena, California. 4EMBL-Monterotondo, Rome, Italy. 5Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain. prioritize lncRNAs partners for experimental validation (Fig. 1c, e-mail: [email protected] or [email protected] Supplementary Fig. 7, and Supplementary Table 9). Data availability. 1. Iyer, M.K. et al. Nat. Genet. 47, 199–208 (2015). 2. Bellucci, M., Agostini, F., Masin, M. & Tartaglia, G.G. Nat. Methods 8, 444– The webserver, documentation, and compiled stand-alone version 445 (2011). of Global Score are available at http://service.tartaglialab.com/new_ 3. Van Nostrand, E.L. et al. Nat. Methods 13, 508–514 (2016). submission/globalscore. Data from HnrnpK, Ptbp1, Lbr, HnrnpU, 4. Chu, C. et al. Cell 161, 404–416 (2015). 5. McHugh, C.A. et al. Nature 521, 232–236 (2015). Sharp, and Dkc1 eCLIP experiments are deposited at the GEO with 6. Minajigi, A. et al. Science 349, aab2276 (2015). codes (respectively) GSM2325771, GSM2299086, GSM2299085, 7. Moindrot, B. et al. Cell Rep. 12, 562–572 (2015). GSM2325772, GSM2299084, and GSM2325770. 8. Monfort, A. et al. Cell Rep. 12, 554–561 (2015). 6 | VOL.14 NO.1 | JANUARY 2017 | NATURE METHODS.
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