@article{Gawronski_Uhl_Zhang-MechR_predi_lncRN-2018,
author = {Gawronski, Alexander R. and Uhl, Michael and Zhang, Yajia 
          and Lin, Yen-Yi and Niknafs, Yashar S. and Ramnarine, Varune 
          R. and Malik, Rohit and Feng, Felix and Chinnaiyan, Arul M. 
          and Collins, Colin C. and Sahinalp, S. Cenk and Backofen, 
          Rolf},
title = {{MechRNA}: prediction of {lncRNA} mechanisms from 
         {RNA}-{RNA} and {RNA}-protein interactions},
journal = {Bioinformatics},
year = {2018},
doi = {10.1093/bioinformatics/bty208},
volume = {},
user = {backofen},
pmid = {29617966},
pages = {},
number = {},
issn = {1367-4811},
abstract = {Motivation: Long non-coding RNAs (lncRNAs) are defined as 
            transcripts longer than 200 nucleotides that do not get 
            translated into proteins. Often these transcripts are 
            processed (spliced, capped, polyadenylated) and some are 
            known to have important biological functions. However, most 
            lncRNAs have unknown or poorly understood functions. 
            Nevertheless, because of their potential role in cancer, 
            lncRNAs are receiving a lot of attention, and the need for 
            computational tools to predict their possible mechanisms of 
            action is more than ever. Fundamentally, most of the known 
            lncRNA mechanisms involve RNA-RNA and/or RNA-protein 
            interactions. Through accurate predictions of each kind of 
            interaction and integration of these predictions, it is 
            possible to elucidate potential mechanisms for a given 
            lncRNA. Approach: Here we introduce MechRNA, a pipeline for 
            corroborating RNA-RNA interaction prediction and protein 
            binding prediction for identifying possible lncRNA 
            mechanisms involving specific targets or on a 
            transcriptome-wide scale. The first stage uses a version of 
            IntaRNA2 with added functionality for efficient prediction 
            of RNA-RNA interactions with very long input sequences, 
            allowing for large-scale analysis of lncRNA interactions 
            with little or no loss of optimality. The second stage 
            integrates protein binding information pre-computed by 
            GraphProt, for both the lncRNA and the target. The final 
            stage involves inferring the most likely mechanism for each 
            lncRNA/target pair. This is achieved by generating candidate 
            mechanisms from the predicted interactions, the relative 
            locations of these interactions and correlation data, 
            followed by selection of the most likely mechanistic 
            explanation using a combined p-value. Results: We applied 
            MechRNA on a number of recently identified cancer-related 
            lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied 
            lncRNAs (PCA3 and 7SL). This led to the identification of 
            hundreds of high confidence potential targets for each 
            lncRNA and corresponding mechanisms. These predictions 
            include the known competitive mechanism of 7SL with HuR for 
            binding on the tumor suppressor TP53, as well as mechanisms 
            expanding what is known about PCAT1 and ARLn1 and their 
            targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the 
            mechanism involves competitive binding with HuR, which we 
            confirmed using HuR immunoprecipitation assays. 
            Availability: MechRNA is available for download at 
            https://bitbucket.org/compbio/mechrna. Contact: 
            backofen@informatik.uni-freiburg.de, cenksahi@indiana.edu. 
            Supplementary information: Supplementary data are available 
            at Bioinformatics online.}
}