RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited; in particular, identification of RNA motifs that bind proteins has long been challenging, especially when such motifs depend on both sequence and structure. Moreover, although RNA binding proteins (RBPs) often contain more than one binding domain, algorithms capable of identifying more than one binding motif simultaneously have not been developed. In this paper we present a novel pipeline to determine binding peaks in crosslinking immunoprecipitation (CLIP) data, to discover multiple possible RNA sequence/structure motifs among them, and to experimentally validate such motifs. At the core is a new semi-automatic algorithm SARNAclust, the first unsupervised method to identify and deconvolve multiple sequence/structure motifs simultaneously. SARNAclust computes similarity between sequence/structure objects using a graph kernel, providing the ability to isolate the impact of specific features through the bulge graph formalism. Application of SARNAclust to synthetic data shows its capability of clustering 5 motifs at once with a V-measure value of over 0. 95, while GraphClust achieves only a V-measure of 0. 083 and RNAcontext cannot detect any of the motifs. When applied to existing eCLIP sets, SARNAclust finds known motifs for SLBP and HNRNPC and novel motifs for several other RBPs such as AGGF1, AKAP8L and ILF3. We demonstrate an experimental validation protocol, a targeted Bind-n-Seq-like high-throughput sequencing approach that relies on RNA inverse folding for oligo pool design, that can validate the components within the SLBP motif. Finally, we use this protocol to experimentally interrogate the SARNAclust motif predictions for protein ILF3. Our results support a newly identified partially double-stranded UUUUUGAGA motif similar to that known for the splicing factor HNRNPC.
RNA-protein binding is a fundamental biological interaction vital to the diverse functions of RNA, including key roles in RNA splicing, translation, localization and stability [1–4]. However, the sequence features that determine affinity to RNA-binding proteins (RBPs) are unknown for most RBPs, including the vast majority of the hundreds of RBPs in the human proteome. Moreover, even for RBPs with known binding motifs, existing sequence motifs are only weakly predictive of which RNA regions will be bound. Deciphering these RNA binding features is crucial for mechanistic understanding of RNA-protein binding and understanding how RNA regulation impacts human health. RNA-protein interactions are known to play a role in a wide variety of diseases including muscular dystrophy, fragile X syndrome, mental retardation, Prader-Willi syndrome, retinitis pigmentosa, spinal muscular atrophy, and cancer [1–5]. Short single motifs are usually used to describe RNA-protein binding elements, e. g. as compiled in the RBPDB experimental database [6], but such motifs have often had poor predictive power. As an example, Hogan et al identified transcripts bound to 40 yeast RBPs and then searched their UTR regions for overrepresented sequences [7]. They were able to find statistically significant motifs for only 21 RBPs, and in many cases previously known motifs could not be found. This issue of poor predictive power for single motifs has continued even with finer resolution assays such as CLIP-seq, which can localize binding sites to within a few nucleotides [8,9]. For example, in CLIP-seq for LIN28–RNA interaction sites in human somatic and embryonic stem cells [10], the most overrepresented sequence motif (GGAGA) was found in less than 13% of the sites. A possible explanation for this problem is that proteins have the potential to interact with multiple sequence motifs. For instance, it is known that Gemin5, a peripheral protein of the survival of motor neuron (SMN) complex in metazoan organisms [11–13], is responsible for recognition of the Sm site of snRNA [14,15]. This recognition is mediated by a WD40 repeat domain located in the N-terminus [16–18], yet there is also a bi-partite non-canonical RNA-binding domain at the C-terminus which modulates IRES-dependent translation [19,20]. However, computational methods to distinguish multiple motifs simultaneously have not been developed. Existing computational approaches for RNA motif detection, which have been geared toward single motif discrimination, have had moderate success. RNA motif analysis has often been carried out by repurposing DNA motif finder tools such as MEME [21], PhyloGibbs [22] or cERMIT [23], but these methods cannot take into account RNA secondary structure. Many known RBPs do bind to single stranded RNA (ssRNA), but it remains unclear how much secondary structure impacts binding. Some methods have incorporated aspects of RNA structure, e. g. by biasing for single stranded regions [24,25] or searching over a limited set of structural contexts (paired, loop, unstructured, miscellaneous) [26–28]. However, the predictive power of these methods remains low, likely because of the limited number of considered contexts compared to the diversity of possible RNA structures. For example, Kazan et al. tested their algorithm on 9 RBP-interaction sets and found an average AUC value of only 0. 64 [28]. Approaches that consider structural contexts using machine learning algorithms such as Support Vector Machines [29], Hidden Markov Models [30,31] or Deep Learning [32–34] have been developed. Some have improved cross-validation AUC values to 0. 8 to 0. 9 [34–36], though common caveats to current approaches are that they rely on immunoprecipitation training sets with uncertain specificity, that they have not been developed to handle multiple motifs, or that they have abstracted structural constraints rather than considering exact RNA structures. Recently, Maticzka and colleagues developed the graph kernel-based GraphProt to handle sequence and structure together and applied it to learn motifs from CLIP-seq data [37], finding motifs that were predictive of binding for the protein PTB. However, this approach has not been tested for RBPs that bind to double stranded RNAs, and it is unknown whether the effectiveness would depend on the types of structures to which individual proteins bind. Moreover, GraphProt reports at most one motif and classifies the remaining data as noise. A more general approach would be to use clustering to allow for multiple possible motifs. A related method is GraphClust [38], which uses a sequence/structure graph kernel to cluster RNAs, and a recent extension called RNAscClust [39] incorporates orthologous sequence conservation to improve the RNA folding estimates into the clustering process. However, these methods are tailored to cluster non-coding RNAs, and it is unknown if they would be effective for the clustering of CLIP-seq sites. Here, we propose a method, SARNAclust (Semi-Automatic RNA clustering), to cluster, as opposed to classify, RNA motifs that bind to a given RBP from CLIP-seq data. To our knowledge, this is the first approach to attempt to cluster CLIP-seq peaks in order to discover potentially multiple RNA motifs that bind to a given RBP. The most related approach we know of is AptaTrace [40], which uses clustering to identify multiple possible RNA motifs from HT-SELEX experiments. However, AptaTrace is not optimized for CLIP-seq data since it relies on k-mer context information during evolution of a sequence pool over multiple SELEX rounds, while CLIP-seq provides a static snapshot. Another recent method, RNAcompete-S [41] clusters multiple components that contribute to a single binding motif, but is not designed to handle distinct motifs arising from separate binding domains. Here we present a novel pipeline to address these problems in RNA-protein motif identification. We first describe our pipeline, which consists of 3 steps (peak discovery, motif discovery and motif experimental validation), with particular attention to the novel computational motif discovery algorithm SARNAclust. Next we benchmark SARNAclust on synthetic data and validate our experimental protocol on a known double-stranded RNA motif. We then show SARNAclust motif predictions for a set of RBPs with eCLIP data and experimentally validate the motif of one such RBP. Finally, we discuss the results and implications of this pipeline in the Discussion section.
We present a mixed computational/experimental pipeline to derive RNA motifs that bind to a given RBP based on immunoprecipitation data. The motivation for our pipeline is two-fold: first, to discover motifs where both sequence and structure are necessary; and second, to enable identification of more than one motif per RBP through optimized clustering over the CLIP peaks. Our complete software pipeline includes source code to process data files from a CLIP experiment (see Methods), to calculate secondary structure of the peaks using RNAfold, to cluster peaks according to sequence only, and to cluster peaks according to sequence/structure using SARNAclust. In addition, we provide a protocol for experimental validation of candidate motifs, including in silico design of instances of the motif using RNAiFold [42,43]. Fig 1 shows the flowchart of our pipeline. S1 Fig shows the flowchart of the peak analysis. A key element of SARNAclust is the graph transformation that allows for the calculation of a similarity value between pairs of sequence/structures. These similarity values provide the input for the clustering of CLIP peaks. Flexible parameters in SARNAclust allow it to be used as a guidance system to identify well-supported motifs and test their key features. To test the effectiveness of SARNAclust, we generated 100 sequences for each of the 5 synthetic motifs in Table 1. We then combined these 500 sequences with 1000 random sequences to act as noise and then tested the ability of SARNAclust to sort these into separate clusters. Each synthetic motif corresponds to a hypothetical RNA motif that would bind a protein binding domain. The 5 motifs represent: a special structure with no sequence conservation (special_structure) or a conserved sequence within a certain structural context in a hairpin loop (GAGA_in_Hairpin), in a bulge (AUG_in_Bulge), in an external loop (pyrimidine_tract) or in a double stranded region (GGUCG_in_left_stem). Sequences for each motif were generated using RNAdualPF [48], which samples from the low energy ensemble of sequences compatible with the given structure and with the corresponding sequence constraints (see Table 1). The 1000 random sequences were generated uniformly randomly (i. e. sampling each nucleotide with 0. 25 probability) with lengths distributed the same as the lengths of the synthetic motifs. All motif and random sequences can be found in S1 Data. Clustering of these 1500 sequences indicated that SARNAclust was able to distinguish multiple clusters corresponding to the original motif groups. As a clustering method we used DBSCAN from the sklearn package, surveying over possible values for the threshold parameter that specifies the minimal similarity for two data points to be in the same cluster, and with graph kernel options R = 2 and D = 2. This threshold is a dissimilarity threshold—at a threshold of x, 2 sequence/structures cannot be in the same cluster if their similarity measure is less than 1-x. Fig 3 shows the different V-measure (a measure of clustering quality, see Methods) values for each graph transformation and each threshold value. S2 Data shows other measures of quality of clustering (see Methods) assessed by comparing the true cluster label for each sequence versus the one yielded by the clustering algorithm. We observed that SARNAclust was able to recover each category of motif, though optimization of the choice of graph transformation enhanced detection of each motif class. As can be seen, use of each graph transformation yielded the corresponding motif at low to mid threshold values but false positives increased as the threshold parameter increased. For instance, option 4 finds the GAGA_in_hairpin motif easily at low thresholds. Although option 6 performs well, it benefits from the fact that most motifs do not have external loops and may not be as general as other options. For most motif instances option 6 is only able to use the bulge graph features to discriminate motif instances from one another. The GraphProt-like options (1 and 2) perform well at high threshold values, but cannot successfully cluster at low thresholds. This is due to the excess number of features specified in this graph transformation, making it difficult to cluster instances unless they are nearly identical. Option 2 contains fewer features than option 1 and thus performs better. Options 10 and 11 are simplified GraphProt-like versions and achieve the best results with v-measure values of over 0. 95. Option 9 achieves high quality values as well, and for a large range of threshold, especially with respect to FMS. For comparison, we also applied GraphClust to the same set of synthetic motifs. We note that we did not choose the GraphClust extension RNAscClust here because the folded structures are pre-determined for these synthetic designed sequences, and therefore the folding improvements of RNAscClust do not offer any advantages. We used the default GraphClust parameters except graph kernel R and D, which were set to the values we used in SARNAclust, and the minimum length of sequence was set so that all the 1500 sequences would be considered. GraphClust returns by default 5 clusters, which we would expect to correspond to the 5 synthetic motifs. GraphClust returns the seed and extended sequences for each instance, and we calculated several clustering quality measures for each (Adjusted Rand Index (ARI), Adjusted Mutual Information (AMI), Homogeneity Score (HS), Completeness Score (CS), V-measure score (VMS) and Fowlkes-Mallows score (FMS) ). SARNAclust outperformed GraphClust as shown in Table 2. Note that the GraphClust v-measure values in both scenarios are under 0. 1, while SARNAclust with option 11 achieves better results for almost all thresholds, including >0. 95 at threshold 0. 5. This indicates that the difference between clustering CLIP peaks and RNAs are substantial enough that the SARNAclust provides superior performance over GraphClust. This is likely due to the large combinatorial complexity of GraphClust’s parameter space. We also compared whether a classification-based approach to motif detection could identify the synthetic motifs as well as SARNAclust. To handle multiple motifs, we used classification to identify the best motif iteratively, at each step removing the sequences containing the prior best motif. For this comparison we chose RNAcontext [28], which uses classification to identify one motif at a time based on sequence and structure. Remarkably, the first iteration of RNAcontext could not find any of the synthetic motifs (S3 Fig). In fact, the sequence motif (of length 11) returned has a very low information content of 5 bits (when a completely fixed sequence of length 11 would have an information content of 22 bits). This is likely because the RNAcontext approach is not well-suited to the benchmark set as it contains multiple signals from distinct overrepresented motifs as well as noise. Interference among the motifs apparently causes RNAcontext to be unable to report any single motif with high confidence. As part of our pipeline for identifying binding motifs, we developed a targeted RNA Bind-N-Seq (RBNS) protocol [49] to experimentally test motif predictions. We first tested this protocol on Stem Loop Binding Domain Protein (SLBP), which binds a known motif [50] found in the 3’UTR of histone mRNAs. In the RNA Bind-N-Seq protocol, randomly generated 40-mers are tested for their efficiency in binding a protein. Because any RNA molecule of length 40 can form over 200 trillion secondary structures, it is not possible to fully sample this space. Therefore, we performed RBNS measurements of SLBP-RNA binding with several thousand designed sequences to ascertain the validity of our experimental validation approach. To do this, we first used RNAiFold [43] (See Methods) to design four different types of sequences as illustrated in S1 Table (153,4106,4107 and 4106 sequences of each respectively). These sequences were chosen to test whether binding requires sequence conservation in the loop region or the stem region of the motif, and also whether relocation of the loop sequence to a different structural context (i. e. a bulge) could still lead to protein-RNA binding. We then performed RBNS in duplicate using purified GST-SBP-SLBP (Glutathione-S-transferase Streptavidin-Binding Peptide SLBP) to pull down the designed RNA sequences [49]. As a nonspecific binding control, we also performed RBNS with the same RNA against purified GST-SBP. Each protein was expressed in E. coli and affinity purified (S4 Fig), and pulled down RNA was reverse transcribed with a primer containing a 10 nt random sequence to enable collapsing of PCR duplicates during data analysis. The resulting cDNA was then PCR amplified to attach Illumina sequencing primers and indices. Only the consensus motif [50] exhibited a clear shift from the control, indicating that the motif definition is specific and that all the variant versions of the motif have decreased binding. Fig 4A shows the difference between GST-SBP RBNS and GST-SBP-SLBP RBNS, quantified by the shift in percentage of reads of each type. Only the consensus motif has a significant enrichment with respect to the control (t-test p-val = 0. 00147). To assess p-values of individual sequences, we used DEseq [51] to compare the read counts (S3 Data) of sequences in the pool to the controls. This analysis showed that only sequences from the consensus motif bind to SLBP significantly. Moreover, all but 7 of these consensus sequences are significantly overrepresented in the SLBP bound pool (Adjusted p-val > 0. 01). Furthermore, S5 Fig shows the sequence logos for all the consensus sequences that bind or do not bind significantly, respectively. The logos indicate that long stretches of U’s near the apical region of the hairpin loop compromise binding affinity, which is to be expected since they are energetically unfavorable and therefore prone to render the hairpin unstable. To further validate these results and the validity of our RBNS-like experimental protocol, we performed several gel shift experiments (Fig 4B). We incubated 6 RNA probes selected from the RBNS data with purified GST-SBP-SLBP. S3 Data shows the 6 selected sequences highlighted in red. These include 2 from the consensus binding group, one with strong binding affinity in the RBNS assay (consensus A) and one with no significant binding affinity (consensus B). There are also 4 extra sequences from the remaining types where the RBNS binding signal was not significant. As expected, only the consensus A sequence shows binding to SLBP, confirming our conclusions from the p-value analysis and validating the RBNS protocol. Given these validations of the computational and experimental pipeline, we then applied SARNAclust to predict motifs from real immunoprecipitation data. First, we verified that SARNAclust could find the motif for SLBP. In order to do so, we downloaded SLBP eCLIP [52] data from the ENCODE project (www. encodeproject. org). After applying our peak discovery pipeline we were left with only 49 peaks, most of them (i. e. 35) found indeed in histone genes. After calculating the secondary structure of each peak using RNAfold, we ran SARNAclust. Fig 5 shows the motif found for options 1,2 and 10 at threshold 0. 6, as well as option 7 at threshold 0. 5. These are the most suited options since they account for sequence in double stranded regions, which is important for SLBP. Options 7 and 10 yield less specific clustering, meaning that they need higher thresholds (i. e. the clusters contain more sequences) to find the motif, consistent with their being coarser representations of the sequence/structure. As can be seen, the motif found is very similar to the canonical motif [50]. No other clusters were found, showing SARNAclust is effective even if only one motif exists. We then used SARNAclust to predict motifs for several RBPs from the ENCODE project, which is generating RNA crosslinking immunoprecipitation assays that are expected to eventually cover >200 known human RNA Binding Proteins using eCLIP [52]. We downloaded a set of 20 RBPs from ENCODE eCLIP experiments at www. encodeproject. org, each with 2 replicates and a control. We selected these RBPs due to the fact that they contain either double stranded RNA binding domains or unknown RNA binding domains. We discarded all helicases since they are known to promiscuously bind to double-stranded RNAs with no clear motif. We identified novel motifs with SARNAclust using the same graph kernel parameters as in the synthetic data section (R = 2, D = 2) and the same DBSCAN algorithm for clustering. We used options 9,10,11 for their overall performance on the synthetic data, along with option 2 for its similarity to GraphClust, at the best performing thresholds (0. 3–0. 55). Table 3 shows the list of RBPs chosen for this study, along with their RNA binding domains and the number of peaks found by our peak discovery pipeline. Note that none of these RBPs had previously known motifs in the two most relevant motif databases: RBPDB [6] and ATtRACT [53]; and only one RBP (EIF4G) has a motif described in the two recent publications on ENCODE eCLIP [54] and RBNS [55]. We found motifs for several proteins, with results dependent on the choice of options (see S2 Table). However, analysis of the data under the GraphProt algorithm (equivalent to SARNAclust with option 2) was unable to find any clusters for all but 2 RBPs. Similarly, RNAcontext yielded motif predictions with low Area Under the Receiver Operating Curve values, ranging from 0. 111 (NKRF) to 0. 546 (AKAP8L), and most motifs had low sequence complexity and information content. In contrast, SARNAclust with options 9,10 and 11 was able to find clusters almost for all RBPs (all of them for option 9, all but 1 for option 11 and all but 5 for option 10). We focused on clusters with sequence conservation signal in addition to structural information, as those without sequence information would be more difficult to interpret and experimentally validate. This is also why we removed helicases, as we would expect those to lack sequence conservation. Nevertheless, we were able to find several interesting cases, as described below. SARNAclust results were consistent with k-mer analysis but provided additional structural context. K-mer overrepresentation results [3] for k = 4,5, 6,7, 8,9 on these RBPs are shown in S3 Table. Most of the k-mers found were either GU repeats or a stretch of guanines GGGG. SARNAclust was able to find these GU repeat motifs as well simultaneously with structure. However, SARNAclust did not reveal other candidate motifs for those proteins, suggesting that those RBPs with repetitive motifs bind to double stranded structures indiscriminately. Because of the strong predictive motif for ILF3 from SARNAclust, as evidenced by its clusters and signal for double-strandedness, we next used the RBNS approach to validate these motifs. ILF3 is known to be involved in many processes such as transcription, translation, regulation of cell cycle or viral replication [57]. However splicing has only recently been reported as a possible function [58], and confirmation of this HNRNPC-like motif would shed light on a potentially novel function. We therefore used RBNS to test the binding of the predicted motifs with ILF3 and whether it requires a specific RNA structure. Using RNAiFold we designed sequences for 4 different perturbations of the motif as shown in S4 Table (19,2680,2680, and 2680 sequences of each motif class respectively). For each motif class, we attempted to generate a few thousand sequences. However, only 19 designed sequences were obtained for the UUUUUGAGA-unpaired motif class due to the fact that unpaired structures tend to have higher free energies than paired structures (see Methods). Similarly as for SLBP, we performed RBNS with purified GST-SBP-ILF3 using an RNA pool based on the motifs in S4 Table. Fig 7 shows the shift in percentage of reads of each type and its difference between GST-SBP-1, GST-SBP-2 non-specific binding controls and ILF3-1, ILF3-2 samples. Only the motif UUUUUGAGA-paired exhibited a significant positive shift from the control (p-val<0. 005 using the t-test), supporting the novel motif. We also used DEseq to analyze differential representation of sequences in the ILF3 bound and unbound pools (S4 Data). Only sequences from the UUUUUGAGA-paired motif showed significant binding to ILF3, confirming and specifying the computationally discovered motif. 1551 out of 2680 sequences showed increased binding (T-test, p-adj<0. 05), while only 6 sequences showed decreased binding. These targeted RBNS results suggest that among the motifs tested, that the UUUUUGAGA-paired shows the strongest binding. In comparison, we observed no significant enrichment or depletion in binding from any of the 19 sequences representing the UUUUUGAGA-unpaired motif. The paired and unpaired sets of sequences each spanned a range of base compositions. The only systematic difference in the two sets was that all of the unpaired motif sequences contained a poly-A sequence that bound to the UUUUU region while preventing pairing of the GAGA region. This is likely a structural constraint, though we cannot rule out that the polyA sequence could also have a sequence-dependent effect on binding. Because the sequences from all motif groups were incubated with the protein at the same time, we note that the lack of enrichment of other motifs is a comparative effect impacted by the stronger binding of UUUUUGAGA-paired motif sequences. Another factor is that RNA Bind-n-Seq does not capture indirect binding interactions mediated through multiprotein complexes, which may be relevant for some of the other motifs. A special case is the GU repeat motif predicted by SARNAclust for ILF3 (GU-repeats motif in S2 Table), which did not show enriched binding. GU-rich motifs were predicted for many other ENCODE RBPs as well ([54]and S3 Table), and we speculate that the presence of such sequences in CLIP data may be due to experimental noise. These results indicate that ILF3 binds to a UUUUUGAGA motif with most nucleotides in double-stranded regions. They also suggest a relationship between ILF3 and HNRNPC, which has been reported to have a similar motif, though that motif was reported to be single stranded. To explore this further, we analyzed the overlap of ILF3 and HNRNPC peaks in ENCODE eCLIP data. Overall, ILF3 has 822 peaks that fall in anti-sense Alus. 322 of these peaks are shared with HNRNPC peaks, and 146 of those (45%) contain the UUUUUGAGA sequence. Conversely, ILF3 has 285 peaks in anti-sense Alus covering a UUUUUGAGA sequence, and 146 of such peaks (51%) are shared with HNRNPC. Thus from both perspectives a substantial fraction of sites overlap between ILF3 peaks, HNRNPC peaks, and UUUUUGAGA sequences within anti-sense Alus. Our motif analysis predicts 67 of the 146 common sites (46%) to have the paired version of the UUUUUGAGA motif, suggesting there may be some flexibility in RNA structure at the overlapping sites.
SARNAclust is a novel computational method that can effectively process and analyze data from CLIP experiments in order to predict RNA motifs likely to bind individual proteins. A key novelty of SARNAclust is that it can assess RNA binding motifs at the level of the complete RNA structure, rather than only taking into account abstractions of structural context. The SARNAclust approach of clustering rather than classifying distinguishes it from prior methods, allowing it to identify motifs even without training data. This is an important aspect for CLIP-seq, for which the specificity of experimental measurements is not well understood due to diverse effects such as multiple binding modalities and sources of noise. Application of SARNAclust and the new RBNS validation approach allowed us to experimentally verify ILF3 binding to a newly predicted UUUUUGAGA motif. The signal for this was distinct from repetitive GU or CU motifs, supporting the idea that those repetitive sequences are not true binding sequences. More broadly, SARNAclust allowed us to investigate the relative importance of structure, which has been challenging for RNA-protein interactions, and we found that structure significantly affected the RBNS results for both SLBP and ILF3. Structural changes to each of several components of the SLBP motif reduced binding, and the new motif for ILF3 exhibited a bias for double-strandedness. Although identification of RBPs that bind to multiple motifs will require further investigation, the multi-domain structure of many RBPs suggest this is a likely possibility. The combination of SARNAclust and our target RBNS validation already allows us to separate multiple distinct signals from noise, making it suited to this ongoing challenge. In contrast, other methods have more difficulty in resolving multiple signals simultaneously. In addition to our results on synthetic motifs, we found that when we selected 1000 ILF3 peaks at random and inputted them to RNAcontext, we found no similar motif to the ones output by SARNAclust (S6 Fig). The similarity of the new ILF3 motif to that for HNRNPC is intriguing, as it was shown in [56] that HNRNPC competes with another protein U2AF2 for binding of 3’ splice sites to regulate the inclusion/exclusion of exons. They concluded that HNRNPC prevents inclusion of cryptic exons while U2AF2 promotes it, with RBP binding often occurring in antisense Alu elements. Based on this competition, we would expect U2AF2 to have a similar binding site to HNRNPC. However, the predicted motif for HNRNPC is much more similar to that for ILF3 than it is to the predicted U2AF2 motif (Table 3). We speculate that ILF3 might compete with either HNRNPC or U2AF2 for binding of similar regions. In this paper we have introduced a new pipeline with a powerful clustering algorithm SARNAclust for analyzing CLIP data in order to cluster CLIP peaks into different binding motifs. We have verified the effectiveness of SARNAclust on synthetic data and used RNA Bind-n-Seq to experimentally validate predictions for new and known motif predictions from ENCODE data. These studies included surveying over different biophysical models and clustering thresholds to identify those likely to work best for real datasets (i. e. options 9,10,11 at clustering thresholds 0. 3–0. 55). We have also shown the utility of our RBNS approach by validating its results using gel shift experiments. Still we are we are cognizant of the fact that different RBPs will vary in binding affinity and modality, particularly those with different types of RNA recognition motifs, and further studies will be needed to confirm the generality of these methods for all RBPs. In the future and as more eCLIP data sets for double-stranded binding RBPs become available, we expect SARNAclust will be a valuable tool to discover new motifs, to probe the combinatorial interactions of RNA-binding proteins, and to elucidate their functional importance.
ENCODE data (www. encodeproject. org) correspond to a set of CLIP experiments described as enhanced CLIP (eCLIP), which modifies the iCLIP method to include improvements in library preparation of RNA fragments. See [52] for details. All data were downloaded through the ENCODE Project website. For proteins with more than one experimental cell type, we used the data from the K562 female cell line. Probes were in vitro transcribed and biotinylated using the Pierce RNA 3’ end biotinylation kit. 1 nM of biotinylated probe was incubated with or without 320 nM GST-SBP-SLBP in binding buffer consisting of 10 mM HEPES (pH 7. 3), 20 mM KCl, 1 mM MgCl2,20 mM DTT, 5% glycerol. The incubation period was 30 minutes, followed by gel electrophoresis on a native TBE 4% polyacrylamide gel and transfer to a nylon membrane, all at 4°C. Membranes were processed using the ThermoFisher Scientific Chemiluminescent Nucleic Acid Detection Module Kit. Images were captured on a Kodak ImageStation 4000MM Pro.
RNA-protein binding is critical to gene regulation, and aberrant RNA-protein interactions play a role in a wide variety of diseases. However, molecular understanding of these interactions remains limited because of the difficulty of ascertaining the motifs that bind each protein. To address this challenge, we have developed a novel algorithm, SARNAclust, to computationally identify combined structure/sequence motifs from immunoprecipitation data. SARNAclust can deconvolve multiple motifs simultaneously and determine the importance of specific features through a graph kernel and bulge graph formalism. We have verified SARNAclust to be effective on synthetic motif data and also tested it on ENCODE eCLIP datasets, identifying known motifs and novel predictions. We have experimentally validated SARNAclust for two proteins, SLBP and ILF3, using RNA Bind-n-Seq measurements. Applying SARNAclust to ENCODE data provides new evidence for previously unknown regulatory interactions, notably splicing co-regulation by ILF3 and the splicing factor hnRNPC.
rna-binding proteins
applied mathematics
simulation and modeling
algorithms
mathematics
clustering algorithms
immunoprecipitation
sequence motif analysis
research and analysis methods
sequence analysis
rna structure
sequence alignment
bioinformatics
proteins
molecular biology
precipitation techniques
biochemistry
rna
rna folding
double stranded rna
nucleic acids
database and informatics methods
biology and life sciences
physical sciences
macromolecular structure analysis
SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data