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Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data.

Wu Y, Shi B, Ding X, Liu T, Hu X, Yip KY, Yang ZR, Mathews DH, Lu ZJ - Nucleic Acids Res. (2015)

Bottom Line: We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures).For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs.However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.

View Article: PubMed Central - PubMed

Affiliation: MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.

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Performance of RME, RNAstructure-Fold and SeqFold with perfect restraints. (A) Sensitivity and (B) PPV were calculated for the RNA secondary structure predictions. The values were averaged from five-fold cross-validation over a large RNA secondary structure database. Error bars represent the standard deviation. Performances with (restrained) or without restraints (control) are shown side by side.
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Figure 2: Performance of RME, RNAstructure-Fold and SeqFold with perfect restraints. (A) Sensitivity and (B) PPV were calculated for the RNA secondary structure predictions. The values were averaged from five-fold cross-validation over a large RNA secondary structure database. Error bars represent the standard deviation. Performances with (restrained) or without restraints (control) are shown side by side.

Mentions: The average performance scores for the RNA secondary structure predictions from the five-fold cross-validation are shown in Figure 2 (details in Supplementary Table S7). RME with perfect restraints significantly improved the accuracy of RNA secondary structure prediction in comparison with RME-control without restraints added. We compared the mean and standard deviation of the average MCC from five-fold cross validation. The average MCC was increased from (62.9 ± 0.6)% (sample size = 5) for RME-control to (93.7 ± 0.2)% (sample size = 5) for RME (P < 0.05, one-tailed Wilcoxon signed-rank test). RNAstructure-Fold also performed well with perfect restraints: the average MCC was increased from (62.0 ± 0.5)% (sample size = 5) to (93.4 ± 0.2)% (sample size = 5). Although the addition of perfect restraints to SeqFold (average MCC, (68.4 ± 0.7)%, sample size = 5) produced great improvement in comparison with SeqFold-control (average MCC, (61.6 ± 0.7)%, sample size = 5), SeqFold did not perform as well as RME or RNAstucture-Fold, because SeqFold cannot guarantee the correct structure is sampled, even when the restraint data is perfect.


Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data.

Wu Y, Shi B, Ding X, Liu T, Hu X, Yip KY, Yang ZR, Mathews DH, Lu ZJ - Nucleic Acids Res. (2015)

Performance of RME, RNAstructure-Fold and SeqFold with perfect restraints. (A) Sensitivity and (B) PPV were calculated for the RNA secondary structure predictions. The values were averaged from five-fold cross-validation over a large RNA secondary structure database. Error bars represent the standard deviation. Performances with (restrained) or without restraints (control) are shown side by side.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4551937&req=5

Figure 2: Performance of RME, RNAstructure-Fold and SeqFold with perfect restraints. (A) Sensitivity and (B) PPV were calculated for the RNA secondary structure predictions. The values were averaged from five-fold cross-validation over a large RNA secondary structure database. Error bars represent the standard deviation. Performances with (restrained) or without restraints (control) are shown side by side.
Mentions: The average performance scores for the RNA secondary structure predictions from the five-fold cross-validation are shown in Figure 2 (details in Supplementary Table S7). RME with perfect restraints significantly improved the accuracy of RNA secondary structure prediction in comparison with RME-control without restraints added. We compared the mean and standard deviation of the average MCC from five-fold cross validation. The average MCC was increased from (62.9 ± 0.6)% (sample size = 5) for RME-control to (93.7 ± 0.2)% (sample size = 5) for RME (P < 0.05, one-tailed Wilcoxon signed-rank test). RNAstructure-Fold also performed well with perfect restraints: the average MCC was increased from (62.0 ± 0.5)% (sample size = 5) to (93.4 ± 0.2)% (sample size = 5). Although the addition of perfect restraints to SeqFold (average MCC, (68.4 ± 0.7)%, sample size = 5) produced great improvement in comparison with SeqFold-control (average MCC, (61.6 ± 0.7)%, sample size = 5), SeqFold did not perform as well as RME or RNAstucture-Fold, because SeqFold cannot guarantee the correct structure is sampled, even when the restraint data is perfect.

Bottom Line: We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures).For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs.However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.

View Article: PubMed Central - PubMed

Affiliation: MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.

Show MeSH
Related in: MedlinePlus