RIsearch2: suffix array-based large-scale prediction of RNA–RNA interactions and siRNA off-targets

Alkan, Ferhat; Wenzel, Anne; Palasca, Oana; Kerpedjiev, Peter; Rudebeck, Anders Frost; Stadler, Peter F.; Hofacker, Ivo L.; Gorodkin, Jan

doi:10.1093/nar/gkw1325

Cited by 69 publications

(89 citation statements)

References 61 publications

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“…Next, we compared the accuracy and runtime of PREPH to those of other programs such as IntaRNA2.0 [41], RIsearch2 [42], RNAplex [43], DuplexFold [44] and bifold [44]. As a benchmark for the time efficiency, we used a set of 1000 pairs of randomly chosen conserved intronic sequences from the human genome (see Methods).…”

Section: Benchmarkmentioning

confidence: 99%

Conserved long-range base pairings are associated with pre-mRNA processing of human genes

Kalmykova

Kalinina

Denisov

et al. 2020

Preprint

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The ability of nucleic acids to form double-stranded structures is essential for all living systems on Earth. While DNA employs it for genome replication, RNA molecules fold into complicated secondary and tertiary structures. Current knowledge on functional RNA structures in human protein-coding genes is focused on locally-occurring base pairs. However, chemical crosslinking and proximity ligation experiments have demonstrated that long-range RNA structures are highly abundant. Here, we present the most complete to-date catalog of conserved long-range RNA structures in the human transcriptome, which consists of 1.1 million pairs of conserved complementary regions (PCCRs). PCCRs tend to occur within introns proximally to splice sites, suppress intervening exons, circumscribe circular RNAs, and exert an obstructive effect on cryptic and inactive splice sites. The double-stranded structure of PCCRs is supported by a significant decrease of icSHAPE nucleotide accessibility, high abundance of A-to-I RNA editing sites, and frequent nearby occurrence of forked eCLIP peaks. Introns with PCCRs show a distinct splicing pattern in response to RNA Pol II slowdown suggesting that splicing is widely affected by co-transcriptional RNA folding. Additionally, transcript starts and ends are strongly enriched in regions between complementary parts of PCCRs, leading to an intriguing hypothesis that RNA folding coupled with splicing could mediate co-transcriptional suppression of premature cleavage and polyadenylation events. PCCR detection procedure is highly sensitive with respect to bona fide validated RNA structures at the expense of having a high false positive rate, which cannot be reduced without loss of sensitivity.The catalog of PCCRs is visualized through a UCSC Genome Browser track hub to facilitate further genome research on long-range RNA structures.Double-stranded structure is a key feature of nucleic acids that enables replicating the genomic information and underlies fundamental cellular processes [1,2]. Many RNAs adopt functional secondary structures, and mRNAs are no exception although their main role is to encode proteins [3,4,5,6]. In eukaryotes, RNA structure affects gene expression through modulating all steps of pre-mRNA processing including splicing [7], cleavage and polyadenylation [8], and RNA editing [9]. The loss of functional RNA structure has been increasingly reported as implicated in hereditary disease and cancer [10,11,12,13].Recent progress in high-throughput sequencing techniques enabled several experimental strategies to determine RNA structure in vivo [14,15,16]. Chemical RNA structure probing can reveal which bases are single-or double-stranded, but it cannot determine which nucleotides form base pairs [17,18,19,20,21].In order to identify the interacting partners, RNA structure probing has to be combined with de novo RNA structure prediction [22], but due to a number of technical limitations such methods cannot account for base pairings that are far apart [23]. Photo-inducible RNA crosslinking a...

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Section: Benchmarkmentioning

confidence: 99%

Conserved long-range base pairings are associated with pre-mRNA processing of human genes

Kalmykova

Kalinina

Denisov

et al. 2020

Preprint

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“…It is possible to use RNA-RNA interaction prediction software for transcriptome-wide, lncRNA-RNA interaction prediction, through the use of existing tools such as IntaRNA (on a supercomputer (Terai et al, 2016)) or by new pipelines such as RISearch2 (Alkan et al, 2017). All these approaches need to apply the following steps (not necessarily in order):…”

Section: Introductionmentioning

confidence: 99%

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

Gawronski

Uhl

Zhang

et al. 2018

Preprint

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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:

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“…While tools like IntaRNA [6,7] or TargetRNA(2) [16,17] require 7 base pairs, other approaches as RIsearch2 [8], RIblast [11] or sTarPicker [12] are less restrictive and require only 6, 5 or at least 5 (with additional constraints), respectively. Similar constraints are also applied in the context of eukaryotic microRNAs [8,18]. Figure 1 summarizes the results for various seed lengths using IntaRNA.…”

Section: Seed Constraint -Length Of the Seedmentioning

confidence: 99%

“…Accessibility-based approaches can be split into two classes based on the applied accessibility model. The site-based approaches, like RNAup [5], IntaRNA [6,7] or RIsearch2 [8], compute and use explicit unpaired probabilities for the interacting subregions. While this is exact, the precomputation time and space consumption grows with the maximal length of considered interactions.…”

Section: Introductionmentioning

confidence: 99%

The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment

et al. 2020

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Background: Seed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to estimate the influence of individual restrictions on the prediction results. Results: Here, we present a systematic assessment of the impact of established and new constraints on sRNA target prediction both on a qualitative as well as computational level. This is done exemplarily based on the performance of IntaRNA, one of the most exact sRNA target prediction tools. IntaRNA provides various ways to constrain considered seed interactions, e.g. based on seed length, its accessibility, minimal unpaired probabilities, or energy thresholds, beside analogous constraints for the overall interaction. Thus, our results reveal the impact of individual constraints and their combinations. Conclusions: This provides both a guide for users what is important and recommendations for existing and upcoming sRNA target prediction approaches. We show on a large sRNA target screen benchmark data set that only by altering the parameter set, IntaRNA recovers 30% more verified interactions while becoming 5-times faster. This exemplifies the potential of seed, accessibility and interaction constraints for sRNA target prediction.

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RIsearch2: suffix array-based large-scale prediction of RNA–RNA interactions and siRNA off-targets

Cited by 69 publications

References 61 publications

Conserved long-range base pairings are associated with pre-mRNA processing of human genes

Conserved long-range base pairings are associated with pre-mRNA processing of human genes

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

The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment

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