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RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules.

Horesh Y, Doniger T, Michaeli S, Unger R - BMC Bioinformatics (2007)

Bottom Line: We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy.These datasets allowed for comparison of the algorithm with other methods.In these tests, RNAspa performed better than four other programs.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel. yair@biomodel.os.biu.ac.il

ABSTRACT

Background: In recent years, RNA molecules that are not translated into proteins (ncRNAs) have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure.

Results: We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE) predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of vertices in a graph. The shortest path in this graph is the basis for structural predictions for the molecule. We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy. We show that this approach allows us to more deeply explore the suboptimal structure space.

Conclusion: The algorithm was tested on three datasets which include several ncRNA families taken from the Rfam database. These datasets allowed for comparison of the algorithm with other methods. In these tests, RNAspa performed better than four other programs.

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The average MCC score of the worst/average/best structures suggested by RNAsubopt compared to RNAspa's MCC score. The average MCC score of the worst/average/best predicted structure in the list of 150 suboptimal structures predicted by RNAsubopt. Note that the performance of RNAspa is much closer to the best score than to the average score.
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Figure 4: The average MCC score of the worst/average/best structures suggested by RNAsubopt compared to RNAspa's MCC score. The average MCC score of the worst/average/best predicted structure in the list of 150 suboptimal structures predicted by RNAsubopt. Note that the performance of RNAspa is much closer to the best score than to the average score.

Mentions: For each sequence, we took the 150 structure predictions suggested by RNAsubopt and calculated the worst, average, and best MCC scores for these structures. We then compared them with the MCC score of the prediction selected by our RNAspa algorithm. The results, averaged over each family, are shown in Figure 4. The results show that the structures suggested by RNAspa are clearly better than the average of the predicted structures, and in most cases are very close to the best possible structure that is present in the set of RNAsubopt predictions. Thus, in a very reasonable running time, our method is capable of extracting from RNAsubopt the most accurate structures.


RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules.

Horesh Y, Doniger T, Michaeli S, Unger R - BMC Bioinformatics (2007)

The average MCC score of the worst/average/best structures suggested by RNAsubopt compared to RNAspa's MCC score. The average MCC score of the worst/average/best predicted structure in the list of 150 suboptimal structures predicted by RNAsubopt. Note that the performance of RNAspa is much closer to the best score than to the average score.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2147038&req=5

Figure 4: The average MCC score of the worst/average/best structures suggested by RNAsubopt compared to RNAspa's MCC score. The average MCC score of the worst/average/best predicted structure in the list of 150 suboptimal structures predicted by RNAsubopt. Note that the performance of RNAspa is much closer to the best score than to the average score.
Mentions: For each sequence, we took the 150 structure predictions suggested by RNAsubopt and calculated the worst, average, and best MCC scores for these structures. We then compared them with the MCC score of the prediction selected by our RNAspa algorithm. The results, averaged over each family, are shown in Figure 4. The results show that the structures suggested by RNAspa are clearly better than the average of the predicted structures, and in most cases are very close to the best possible structure that is present in the set of RNAsubopt predictions. Thus, in a very reasonable running time, our method is capable of extracting from RNAsubopt the most accurate structures.

Bottom Line: We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy.These datasets allowed for comparison of the algorithm with other methods.In these tests, RNAspa performed better than four other programs.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel. yair@biomodel.os.biu.ac.il

ABSTRACT

Background: In recent years, RNA molecules that are not translated into proteins (ncRNAs) have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure.

Results: We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE) predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of vertices in a graph. The shortest path in this graph is the basis for structural predictions for the molecule. We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy. We show that this approach allows us to more deeply explore the suboptimal structure space.

Conclusion: The algorithm was tested on three datasets which include several ncRNA families taken from the Rfam database. These datasets allowed for comparison of the algorithm with other methods. In these tests, RNAspa performed better than four other programs.

Show MeSH
Related in: MedlinePlus