siPRED: Predicting siRNA Efficacy Using Various Characteristic Methods

Pan, Weijie; Chen, Chi-Wei; Chu, Yen-Wei

doi:10.1371/journal.pone.0027602

Cited by 27 publications

(31 citation statements)

References 33 publications

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“…There are many ways to derive algorithms predicting siRNA efficacy, most of which produce outputs with similar predictive power (∼60% on validation datasets) ( 7 ). Here we developed a linear regression-based algorithm that predicted the efficacy of sdRNAs with an accuracy comparable to other models reported previously ( 7 ). The linear regression model used positional base preferences as descriptors and allowed for simple visualization of the major features contributing to functional efficacy.…”

Section: Discussionmentioning

confidence: 99%

“…A variety of mathematical approaches were used for modeling siRNA efficacy. The majority of these algorithms describe datasets with a Pearson correlation coefficient of ∼0.6, and variation between the predictive power of the different models is relatively small ( 7 ). At the same time, many of these algorithms require time-consuming and multiparametric computations.…”

Section: Introductionmentioning

confidence: 99%

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Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs

Shmushkovich

Monopoli

Homsy

et al. 2018

Nucleic Acids Research

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Progress in oligonucleotide chemistry has produced a shift in the nature of siRNA used, from formulated, minimally modified siRNAs, to unformulated, heavily modified siRNA conjugates. The introduction of extensive chemical modifications is essential for conjugate-mediated delivery. Modifications have a significant impact on siRNA efficacy through interference with recognition and processing by RNAi enzymatic machinery, severely restricting the sequence space available for siRNA design. Many algorithms available publicly can successfully predict the activity of non-modified siRNAs, but the efficiency of the algorithms for designing heavily modified siRNAs has never been systematically evaluated experimentally. Here we screened 356 cholesterol-conjugated siRNAs with extensive modifications and developed a linear regression-based algorithm that effectively predicts siRNA activity using two independent datasets. We further demonstrate that predictive determinants for modified and non-modified siRNAs differ substantially. The algorithm developed from the non-modified siRNAs dataset has no predictive power for modified siRNAs and vice versa. In the context of heavily modified siRNAs, the introduction of chemical asymmetry fully eliminates the requirement for thermodynamic bias, the major determinant for non-modified siRNA efficacy. Finally, we demonstrate that in addition to the sequence of the target site, the accessibility of the neighboring 3′ region significantly contributes to siRNA efficacy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs

Shmushkovich

Monopoli

Homsy

et al. 2018

Nucleic Acids Research

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“…to predict the efficiency of siRNAs. Many other workers have also used a combination of features using different machine learning techniques to predict mammalian siRNA efficacy [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses

2013

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BackgroundSelection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed “VIRsiRNApred” a support vector machine (SVM) based method for predicting the efficacy of viral siRNA.MethodsIn the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T1380) and validation (V345) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development.ResultsDuring 10-fold cross validation on T1380 using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V345 independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree.ConclusionVIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/.

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“…18 Additionally, many web servers are existing to predict the efficacy of siRNAs e.g. siPRED 19 BIOPREDsi 20 MysiRNA, 21 desiRm, 22 VIRsiRNApred 23 etc. However, none of these methods predict efficacy for the chemically modified siRNAs (cm-siRNAs).…”

Section: Introcuctionmentioning

confidence: 99%

SMEpred workbench: A web server for predicting efficacy of chemicallymodified siRNAs

et al. 2016

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Chemical modifications have been extensively exploited to circumvent shortcomings in therapeutic applications of small interfering RNAs (siRNAs). However, experimental designing and testing of these siRNAs or chemically modified siRNAs (cm-siRNAs) involves enormous resources. Therefore, in-silico intervention in designing cm-siRNAs would be of utmost importance. We developed SMEpred workbench to predict the efficacy of normal siRNAs as well as cm-siRNAs using 3031 heterogeneous cm-siRNA sequences from siRNAmod database. These include 30 frequently used chemical modifications on different positions of either siRNA strand. Support Vector Machine (SVM) was employed to develop predictive models utilizing various sequence features namely mono-, di-nucleotide composition, binary pattern and their hybrids. We achieved highest Pearson Correlation Coefficient (PCC) of 0.80 during 10-fold cross validation and similar PCC value in independent validation. We have provided the algorithm in the 'SMEpred' pipeline to predict the normal siRNAs from the gene or mRNA sequence. For multiple modifications, we have assembled 'MultiModGen' module to design multiple modifications and further process them to evaluate their predicted efficacies. SMEpred webserver will be useful to scientific community engaged in use of RNAi-based technology as well as for therapeutic development. Web server is available for public use at following URL address: http://bioinfo.imtech.res.in/manojk/smepred.

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siPRED: Predicting siRNA Efficacy Using Various Characteristic Methods

Cited by 27 publications

References 33 publications

Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs

Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs

VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses

SMEpred workbench: A web server for predicting efficacy of chemicallymodified siRNAs

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