RNAProbe - a web server for normalization and analysis of RNA structure probing data. You can take a (quick) NAP, while we analyze your data :)

About the server

The RNAProbe is a web server for normalization of RNA chemical probing experiment reactivities [1] [2] [3] [4], prediction and visualization of RNA secondary structure, based on the experiments' result.

The RNAProbe is user-friendly, aimed for structural biologists, with no prior programming/bioinformatics experience. It allows the user to upload data from QuShape analysis for further normalization and visualization of RNA predicted secondary structure. The standard normalization protocol utilized by the server is described in the literature [5]. The normalized reactivity values are used to predict the secondary structure of the studied RNA, and structure is visualized ina variety of forms.

The whole analysis performed by the server takes a few minutes instead of a cumbersome procedure that the experimentalist must perform by-hand. The images generated by the server are publication quality. The user is assured that for each analysis, the normalization and image generation will be performed exactly the same way, reducing human-induced error.

Modes

The server has four main modes:

SHAPE Mode: analysis of SHAPE reactivities with the standard normalization protocol or using user-provided normalized reactivities.
DMS Mode: analysis of DMS reactivities with the standard normalization protocol or using user-provided normalized reactivities.
CMCT Mode: analysis of CMCT reactivities with the standard normalization protocol or using user-provided normalized reactivities.
Prediction Mode: prediction of RNA secondary structure by various predictors; the consensus of predictions is visualized.

SHAPE Mode

In the SHAPE Mode the user provides two files for the analysis: sequence file and QuShape results file (or normalized reactivities). In this mode, the following steps of analysis are being performed:

Normalization of the QuShape reactivities (in case of uploading QuShape results file)
Prediction of the secondary structure, both SHAPE directed and sequence only.
Visualization of the results in various forms.

DMS Mode

In the DMS Mode the user provides two files for the analysis: sequence file and QuShape results file (or normalized reactivities). In this mode, the following steps of analysis are being performed:

Normalization of the QuShape reactivities (in case of uploading QuShape results file)
Prediction of the secondary structure, both SHAPE directed and sequence only.
Visualization of the results in various forms.

CMCT Mode

In the CMCT Mode the user provides two files for the analysis: sequence file and QuShape results file (or normalized reactivities). In this mode, the following steps of analysis are being performed:

Normalization of the QuShape reactivities (in case of uploading QuShape results file)
Prediction of the secondary structure, both SHAPE directed and sequence only.
Visualization of the results in various forms.

Prediction Mode

In the Prediction Mode, the user provides one file for the analysis: sequence file. In this mode, the following steps of analysis are being performed:

Visualization of the results in various forms.

Normalization protocol

After analyzing the probing results with QuShape, one must perform an additional normalization of the nucleotide reactivities. In order to remove outliers, the server follows the protocol described previously for the SHAPE reaction [5]. Briefly, from all reactivity values, 10% of the most reactive peaks are selected, and 20% of them are excluded. Then, from the remaining 8% of most reactive positions, the mean value is calculated, and all intensities are divided by it, yielding the normalized intensities. Finally, all reactivities with values larger than 1 are set to 1, while negative reactivities are set to 0. The whole process results in the normalized probing reactivity ranging from 0 to 1. Additionally, all nucleotides within the probed sequence that are lacking the readout are assigned with the reactivity -1.

Structure prediction methods

The RNAProbe uses three methods for probing-guided secondary structure predictions. For SHAPE-directed predictions, the methods used for obtaining structures are: ViennaRNA RNAfold (RNA probing mode) [6], and RNAstructure ShapeKnots [7]. For DMS and CMCT, probing-directed predictions are obtained using RNAstructure Fold [7].

The server uses several sequence-only structure predictors including ViennaRNA RNAfold [6], RNAstructure Fold and ProbKnot [7], CentroidFold [8], CONTRAFold [9], IPknot [10], Linearfold_V and Linearfold_C [11].

For calculating a consensus structure from a series of predictions we use an in-house method, which identifies base pairs that are present in at least 50% of the input predictions.

Visualization

Structure visualization is performed using VARNA [12]. The predicted structure, informed by the chemical probing results, is visualized using three different approaches: radiate, circular, and linear. The nucleotides are color-coded, based on their normalized reactivities, while nucleotides without data (-1 value) are presented without any color. In the case of DMS and CMCT modes, for convenient visualization of the resulting structures, the user can choose to color only the nucleotides that are susceptible to the given reagent, and to ‘grey-out’ the nucleotides that are not supposed to be modified. In the ‘Prediction mode’, the structures are visualized without any color code.

References

[1] Wilkinson, K.A., Merino, E.J. and Weeks, K.M., 2006. Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution. Nature protocols, 1(3), p.1610.
[2] Stern, S., Moazed, D. and Noller, H.F., 1988. [33] Structural analysis of RNA using chemical and enzymatic probing monitored by primer extension. In Methods in enzymology (Vol. 164, pp. 481-489). Academic Press.
[3] Peattie, D.A. and Gilbert, W., 1980. Chemical probes for higher-order structure in RNA. Proceedings of the National Academy of Sciences, 77(8), pp.4679-4682.
[4] Harris, K.A., Crothers, D.M. and Ullu, E.L.I.S.A.B.E.T.T.A., 1995. In vivo structural analysis of spliced leader RNAs in Trypanosoma brucei and Leptomonas collosoma: a flexible structure that is independent of cap4 methylations. RNA, 1(4), pp.351-362.
[5] Low, J.T. and Weeks, K.M., 2010. SHAPE-directed RNA secondary structure prediction. Methods, 52(2), pp.150-158.
[6] Lorenz, R., Bernhart, S.H., Zu Siederdissen, C.H., Tafer, H., Flamm, C., Stadler, P.F. and Hofacker, I.L., 2011. ViennaRNA Package 2.0. Algorithms for molecular biology, 6(1), p.26.
[7] Reuter, J.S. and Mathews, D.H., 2010. RNAstructure: software for RNA secondary structure prediction and analysis. BMC bioinformatics, 11(1), p.129.
[8] Sato, K., Hamada, M., Asai, K. and Mituyama, T., 2009. CENTROIDFOLD: a web server for RNA secondary structure prediction. Nucleic acids research, 37(suppl_2), pp.W277-W280.
[9] Do, C.B., Woods, D.A. and Batzoglou, S., 2006. CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics, 22(14), pp.e90-e98.
[10] Sato, K., Kato, Y., Hamada, M., Akutsu, T. and Asai, K., 2011. IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming. Bioinformatics, 27(13), pp.i85-i93.
[11] Huang, L., Zhang, H., Deng, D., Zhao, K., Liu, K., Hendrix, D.A. and Mathews, D.H., 2019. LinearFold: linear-time approximate RNA folding by 5'-to-3'dynamic programming and beam search. Bioinformatics, 35(14), pp.i295-i304.
[12] Darty, K., Denise, A. and Ponty, Y., 2009. VARNA: Interactive drawing and editing of the RNA secondary structure. Bioinformatics, 25(15), p.1974.