Motivation: RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single-and double-stranded regions of RNAs in vivo using predictions of protein interactions. Results: Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single-and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions.
CROSSalive: A web server for predicting the in vivo structure of RNA molecules / Ponti, R. D.; Armaos, A.; Vandelli, A.; Tartaglia, G. G.. - In: BIOINFORMATICS. - ISSN 1367-4803. - 36:3(2020), pp. 940-941. [10.1093/bioinformatics/btz666]
CROSSalive: A web server for predicting the in vivo structure of RNA molecules
Tartaglia G. G.
2020
Abstract
Motivation: RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single-and double-stranded regions of RNAs in vivo using predictions of protein interactions. Results: Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single-and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions.File | Dimensione | Formato | |
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