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MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

Alexander R. Gawronski, View ORCID ProfileMichael Uhl, View ORCID ProfileYajia Zhang, View ORCID ProfileYen-Yi Lin, Yashar S. Niknafs, Varune R. Ramnarine, Rohit Malik, Felix Feng, Arul M. Chinnaiyan, Colin C. Collins, S. Cenk Sahinalp, Rolf Backofen
doi: https://doi.org/10.1101/285965
Alexander R. Gawronski
1Computing Science, Simon Fraser University, Burnaby, Canada
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  • For correspondence: backofen@informatik.uni-freiburg.de
Michael Uhl
3Institut für Informatik, University of Freiburg, Freiburg im Breisgau, Germany
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  • ORCID record for Michael Uhl
Yajia Zhang
6Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
7Department of Computational Medicine and Bioinformatics, Ann Arbor, Michigan 48109, USA
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Yen-Yi Lin
1Computing Science, Simon Fraser University, Burnaby, Canada
2Vancouver Prostate Centre, Vancouver, BC, Canada
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Yashar S. Niknafs
5Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
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Varune R. Ramnarine
2Vancouver Prostate Centre, Vancouver, BC, Canada
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Rohit Malik
5Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
11Bristol-Myers Squibb Co., Princeton, New Jersey 08543, USA
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Felix Feng
5Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
9Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109, USA
10Departments of Radiation Oncology, Urology, and Medicine, UCSF, San Francisco, California 94115, USA
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Arul M. Chinnaiyan
5Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
6Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
7Department of Computational Medicine and Bioinformatics, Ann Arbor, Michigan 48109, USA
8Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michigan 48109, USA
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Colin C. Collins
2Vancouver Prostate Centre, Vancouver, BC, Canada
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S. Cenk Sahinalp
2Vancouver Prostate Centre, Vancouver, BC, Canada
4Department of Computer Science, Indiana University, Bloomington, USA
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  • For correspondence: backofen@informatik.uni-freiburg.de
Rolf Backofen
3Institut für Informatik, University of Freiburg, Freiburg im Breisgau, Germany
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  • For correspondence: backofen@informatik.uni-freiburg.de
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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{at}informatik.uni-freiburg.de, cenksahi{at}indiana.edu

Supplementary information Supplementary data are available at Bioinformatics online.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 20, 2018.
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MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions
Alexander R. Gawronski, Michael Uhl, Yajia Zhang, Yen-Yi Lin, Yashar S. Niknafs, Varune R. Ramnarine, Rohit Malik, Felix Feng, Arul M. Chinnaiyan, Colin C. Collins, S. Cenk Sahinalp, Rolf Backofen
bioRxiv 285965; doi: https://doi.org/10.1101/285965
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MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions
Alexander R. Gawronski, Michael Uhl, Yajia Zhang, Yen-Yi Lin, Yashar S. Niknafs, Varune R. Ramnarine, Rohit Malik, Felix Feng, Arul M. Chinnaiyan, Colin C. Collins, S. Cenk Sahinalp, Rolf Backofen
bioRxiv 285965; doi: https://doi.org/10.1101/285965

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