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. 2013 Jul;41(Web Server issue):W180-6.
doi: 10.1093/nar/gkt463. Epub 2013 Jun 10.

RBPmotif: a web server for the discovery of sequence and structure preferences of RNA-binding proteins

Hilal Kazan  1 , Quaid Morris
Affiliations

RBPmotif: a web server for the discovery of sequence and structure preferences of RNA-binding proteins

Hilal Kazan et al. Nucleic Acids Res. 2013 Jul.

Abstract

RBPmotif web server (http://www.rnamotif.org) implements tools to identify binding preferences of RNA-binding proteins (RBPs). Given a set of sequences that are known to be bound and unbound by the RBP of interest, RBPmotif provides two types of analysis: (i) de novo motif finding when there is no a priori knowledge on RBP's binding preferences and (ii) analysis of structure preferences when there is a previously identified sequence motif for the RBP. De novo motif finding is performed with the previously published RNAcontext algorithm that learns discriminative motif models to identify both sequence and structure preferences. The results of this analysis include the inferred binding preferences of the RBP and the added predictive value of incorporating structure preferences. Second type of analysis investigates whether the instances of the previously identified sequence motif are enriched in a particular structure context in bound sequences, relative to its instances in unbound sequences. On completion, the results page shows the comparison of structure contexts of the motif instances between bound and unbound sequences and an assessment of statistical significance of detected preferences. In summary, RBPmotif web server enables the concurrent analysis of sequence and structure preferences of RBPs through a user-friendly interface.

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Figures

Figure 1.
Figure 1.
Calculation of secondary structure profiles. In this toy example, the RNA sequence is assumed to fold into five equally probable secondary structures. These structures can be represented by annotating each base according to the secondary structure element (e.g. paired, hairpin loop) that it participates in. The distribution of annotation for each base can then be calculated by recording the proportion of times that the base appears in the particular structure context. An example calculation is shown for bases in positions 14–17.
Figure 2.
Figure 2.
Result page of the first type of analysis that involves de novo motif finding with RNAcontext. (a) Inferred sequence preferences are converted into a PFM and plotted as a motif logos using EnoLOGOS (14). (b) Structure parameters are scaled so that the most preferred context gets a value of 1. Resulting relative structure preferences are shown as a bar graph. (c) To assess the added predictive value of inferred structure preferences, the AU-ROCs of the full RNAcontext model and a simpler version of it that only includes the sequence preferences on held-out data are compared for each cross-validation run. AU-ROCs and their associated P-values are displayed as a table. (d) The top 5 RBPs with most similar binding motifs [identified with TomTom (13)] to the predicted sequence motif (shown in a) are displayed as a table. The columns of this table show the name of the RBP, name of the gene, P-value, q-value, local id and link to the original database entry, respectively.
Figure 3.
Figure 3.
Result page of the second type of analysis. (a)The bar graph compares the mean profile values of motif instances between bound and unbound sequences. The standard errors of the mean are also shown as error bars. (b) The table shows the results of Wilcoxon’s rank sum test to compare the distribution of structure profiles of motif instances among bound and unbound sequences. The significance threshold for P-values is 0.05 after Bonferroni multiple testing correction.
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