siDRM: an effective and generally applicable online siRNA design tool

Small interfering RNAs (siRNAs) have become an indispensable tool for the investigation of gene functions. Most existing siRNA design tools were trained on datasets assembled from confined origins, incompatible with the diverse siRNA laboratory practice to which these tools will ultimately be applied. We have performed an updated analysis using the disjunctive rule merging (DRM) approach on a large and diverse dataset compiled from siRecords, and implemented the resulting rule sets in siDRM, a new online siRNA design tool. siDRM also implements a few high-sensitivity rule sets and fast rule sets, links to siRecords, and uses several filters to check unwanted detrimental effects, including innate immune responses, cell toxic effects and off-target activities in selecting siRNAs. A performance comparison using an independent dataset indicated that siDRM outperforms 19 existing siRNA design tools in identifying effective siRNAs.     

siRNA selection criteria--statistical analyses of applicability and significance

RNA interference is a powerful tool for gene silencing, which is mediated by introducing siRNA. In the present study, statistical analyses of published siRNA selection criteria, the interpretation of some criteria and systematic searching for new criteria have been carried out for CGB siRNA and siRecords databases. The results of the analyses are as follows: (i) Our study supports the two-state model of the RNA-induced silencing complex (RISC). (ii) Stable 5'-S ends of a siRNA sequence, higher stability of the whole siRNA, and low breaking energy of siRNA duplex occurs in effective siRNA sequences. Also low internal stability of the 5'-AS terminus is preferred. (iii) Secondary structure can be successfully used as an RNAi selection criterion. (iv) Several published sequence criteria have been confirmed and also new criteria have been developed. (v) Also a Target Patterns criterion, which is comparable or better than the best known criteria, has been created.     

Designing of highly effective complementary and mismatch siRNAs for silencing a gene

In past, numerous methods have been developed for predicting efficacy of short interfering RNA (siRNA). However these methods have been developed for predicting efficacy of fully complementary siRNA against a gene. Best of author's knowledge no method has been developed for predicting efficacy of mismatch siRNA against a gene. In this study, a systematic attempt has been made to identify highly effective complementary as well as mismatch siRNAs for silencing a gene.Support vector machine (SVM) based models have been developed for predicting efficacy of siRNAs using composition, binary and hybrid pattern siRNAs. We achieved maximum correlation 0.67 between predicted and actual efficacy of siRNAs using hybrid model. All models were trained and tested on a dataset of 2182 siRNAs and performance was evaluated using five-fold cross validation techniques. The performance of our method desiRm is comparable to other well-known methods. In this study, first time attempt has been made to design mutant siRNAs (mismatch siRNAs). In this approach we mutated a given siRNA on all possible sites/positions with all possible nucleotides. Efficacy of each mutated siRNA is predicted using our method desiRm. It is well known from literature that mismatches between siRNA and target affects the silencing efficacy. Thus we have incorporated the rules derived from base mismatches experimental data to find out over all efficacy of mutated or mismatch siRNAs. Finally we developed a webserver, desiRm (http://www.imtech.res.in/raghava/desirm/) for designing highly effective siRNA for silencing a gene. This tool will be helpful to design siRNA to degrade disease isoform of heterozygous single nucleotide polymorphism gene without depleting the wild type protein.     

Improved and automated prediction of effective siRNA

Short interfering RNAs are used in functional genomics studies to knockdown a single gene in a reversible manner. The results of siRNA experiments are highly dependent on the choice of siRNA sequence. In order to evaluate siRNA design rules, we collected a database of 398 siRNAs of known efficacy from 92 genes. We used this database to evaluate previously proposed rules from smaller datasets, and to find a new set of rules that are optimal for the entire database. We also trained a regression tree with full cross-validation. It was however difficult to obtain the same precision as methods previously tested on small datasets from one or two genes. We show that those methods are overfitting as they work poorly on independent validation datasets from multiple genes. Our new design rules can predict siRNAs with efficacy >/= 50% in 91% of cases, and with efficacy >/=90% in 52% of cases, which is more than a twofold improvement over random selection. Software for designing siRNAs is available online via a web server at or as a standalone version for high-throughput applications.     

Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming

MOTIVATION: Both small interfering RNAs (siRNAs) and antisense oligonucleotides can selectively block gene expression. Although the two methods rely on different cellular mechanisms, these methods share the common property that not all oligonucleotides (oligos) are equally effective. That is, if mRNA target sites are picked at random, many of the antisense or siRNA oligos will not be effective. Algorithms that can reliably predict the efficacy of candidate oligos can greatly reduce the cost of knockdown experiments, but previous attempts to predict the efficacy of antisense oligos have had limited success. Machine learning has not previously been used to predict siRNA efficacy. RESULTS: We develop a genetic programming based prediction system that shows promising results on both antisense and siRNA efficacy prediction. We train and evaluate our system on a previously published database of antisense efficacies and our own database of siRNA efficacies collected from the literature. The best models gave an overall correlation between predicted and observed efficacy of 0.46 on both antisense and siRNA data. As a comparison, the best correlations of support vector machine classifiers trained on the same data were 0.40 and 0.30, respectively.     

Prediction of siRNA knockdown efficiency using artificial neural network models

Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.     

Efficient prediction of siRNAs with siRNArules 1.0: an open-source JAVA approach to siRNA algorithms

RNAi interference and siRNA have become useful tools for investigation of gene function. However, the discovery that not all siRNA are equally efficient made necessary screens or design algorithms to obtain high activity siRNA candidates. Several algorithms have been published, but they remain inefficient, obscure, or commercially restricted. This article describes an open-source JAVA program that is surprisingly efficient at predicting active siRNAs (Pearson correlation coefficient r = 0.52, n = 526 siRNAs). Furthermore, this version 1.0 sets the stage for further improvement of the free code by the open-source community (http://sourceforge.net/).     

Designing highly active siRNAs for therapeutic applications

The discovery of RNA interference (RNAi) generated considerable interest in developing short interfering RNAs (siRNAs) for understanding basic biology and as the active agents in a new variety of therapeutics. Early studies showed that selecting an active siRNA was not as straightforward as simply picking a sequence on the target mRNA and synthesizing the siRNA complementary to that sequence. As interest in applying RNAi has increased, the methods for identifying active siRNA sequences have evolved from focusing on the simplicity of synthesis and purification, to identifying preferred target sequences and secondary structures, to predicting the thermodynamic stability of the siRNA. As more specific details of the RNAi mechanism have been defined, these have been incorporated into more complex siRNA selection algorithms, increasing the reliability of selecting active siRNAs against a single target. Ultimately, design of the best siRNA therapeutics will require design of the siRNA itself, in addition to design of the vehicle and other components necessary for it to function in vivo. In this minireview, we summarize the evolution of siRNA selection techniques with a particular focus on one issue of current importance to the field, how best to identify those siRNA sequences likely to have high activity. Approaches to designing active siRNAs through chemical and structural modifications will also be highlighted. As the understanding of how to control the activity and specificity of siRNAs improves, the potential utility of siRNAs as human therapeutics will concomitantly grow.     

C911: A Bench-Level Control for Sequence Specific siRNA Off-Target Effects

Small interfering RNAs (siRNAs) have become a ubiquitous experimental tool for down-regulating mRNAs. Unfortunately, off-target effects are a significant source of false positives in siRNA experiments and an effective control for them has not previously been identified. We introduce two methods of mismatched siRNA design for negative controls based on changing bases in the middle of the siRNA to their complement bases. To test these controls, a test set of 20 highly active siRNAs (10 true positives and 10 false positives) was identified from a genome-wide screen performed in a cell-line expressing a simple, constitutively expressed luciferase reporter. Three controls were then synthesized for each of these 20 siRNAs, the first two using the proposed mismatch design methods and the third being a simple random permutation of the sequence (scrambled siRNA). When tested in the original assay, the scrambled siRNAs showed significantly reduced activity in comparison to the original siRNAs, regardless of whether they had been identified as true or false positives, indicating that they have little utility as experimental controls. In contrast, one of the proposed mismatch design methods, dubbed C911 because bases 9 through 11 of the siRNA are replaced with their complement, was able to completely distinguish between the two groups. False positives due to off-target effects maintained most of their activity when the C911 mismatch control was tested, whereas true positives whose phenotype was due to on-target effects lost most or all of their activity when the C911 mismatch was tested. The ability of control siRNAs to distinguish between true and false positives, if widely adopted, could reduce erroneous results being reported in the literature and save research dollars spent on expensive follow-up experiments.     

Differential effects of viral silencing suppressors on siRNA and miRNA loading support the existence of two distinct cellular pools of ARGONAUTE1

Plant viruses encode RNA silencing suppressors (VSRs) to counteract the antiviral RNA silencing response. Based on in-vitro studies, several VSRs were proposed to suppress silencing through direct binding of short-interfering RNAs (siRNAs). Because their expression also frequently hinders endogenous miRNA-mediated regulation and stabilizes labile miRNA* strands, VSRs have been assumed to prevent both siRNA and miRNA loading into their common effector protein, AGO1, through sequestration of small RNA (sRNA) duplexes in vivo. These assumptions, however, have not been formally tested experimentally. Here, we present a systematic in planta analysis comparing the effects of four distinct VSRs in Arabidopsis. While all of the VSRs tested compromised loading of siRNAs into AGO1, only P19 was found to concurrently prevent miRNA loading, consistent with a VSR strategy primarily based on sRNA sequestration. By contrast, we provide multiple lines of evidence that the action of the other VSRs tested is unlikely to entail siRNA sequestration, indicating that in-vitro binding assays and in-vivo miRNA* stabilization are not reliable indicator of VSR action. The contrasted effects of VSRs on siRNA versus miRNA loading into AGO1 also imply the existence of two distinct pools of cellular AGO1 that are specifically loaded by each class of sRNAs. These findings have important implications for our current understanding of RNA silencing and of its suppression in plants.     

Competition for RISC binding predicts in vitro potency of siRNA

Short interfering RNAs (siRNA) guide degradation of target RNA by the RNA-induced silencing complex (RISC). The use of siRNA in animals is limited partially due to the short half-life of siRNAs in tissues. Chemically modified siRNAs are necessary that maintain mRNA degradation activity, but are more stable to nucleases. In this study, we utilized alternating 2′-O-methyl and 2′-deoxy-2′-fluoro (OMe/F) chemically modified siRNA targeting PTEN and Eg5. OMe/F-modified siRNA consistently reduced mRNA and protein levels with equal or greater potency and efficacy than unmodified siRNA. We showed that modified siRNAs use the RISC mechanism and lead to cleavage of target mRNA at the same position as unmodified siRNA. We further demonstrated that siRNAs can compete with each other, where highly potent siRNAs can compete with less potent siRNAs, thus limiting the ability of siRNAs with lower potency to mediate mRNA degradation. In contrast, a siRNA with low potency cannot compete with a highly efficient siRNA. We established a correlation between siRNA potency and ability to compete with other siRNAs. Thus, siRNAs that are more potent inhibitors for mRNA destruction have the potential to out-compete less potent siRNAs indicating that the amount of a cellular component, perhaps RISC, limits siRNA activity.     

Comprehensive evaluation of canonical versus Dicer-substrate siRNA in vitro and in vivo

Since the discovery of RNA interference (RNAi), researchers have identified a variety of small interfering RNA (siRNA) structures that demonstrate the ability to silence gene expression through the classical RISC-mediated mechanism. One such structure, termed "Dicer-substrate siRNA" (dsiRNA), was proposed to have enhanced potency via RISC-mediated gene silencing, although a comprehensive comparison of canonical siRNAs and dsiRNAs remains to be described. The present study evaluates the in vitro and in vivo activities of siRNAs and dsiRNAs targeting Phosphatase and Tensin Homolog (PTEN) and Factor VII (FVII). More than 250 compounds representing both siRNA and dsiRNA structures were evaluated for silencing efficacy. Lead compounds were assessed for duration of silencing and other key parameters such as cytokine induction. We identified highly active compounds from both canonical siRNAs and 25/27 dsiRNAs. Lead compounds were comparable in potency both in vitro and in vivo as well as duration of silencing in vivo. Duplexes from both structural classes tolerated 2'-OMe chemical modifications well with respect to target silencing, although some modified dsiRNAs demonstrated reduced activity. On the other hand, dsiRNAs were more immunostimulatory as compared with the shorter siRNAs, both in vitro and in vivo. Because the dsiRNA structure does not confer any appreciable benefits in vitro or in vivo while demonstrating specific liabilities, further studies are required to support their applications in RNAi therapeutics.     

Simple gene silencing using the trans‐acting siRNA pathway

In plants, particular micro‐RNAs (miRNAs) induce the production of a class of small interfering RNAs (siRNA) called trans‐acting siRNA (ta‐siRNA) that lead to gene silencing. A single miRNA target is sufficient for the production of ta‐siRNAs, which target can be incorporated into a vector to induce the production of siRNAs, and ultimately gene silencing. The term miRNA‐induced gene silencing (MIGS) has been used to describe such vector systems in Arabidopsis. Several ta‐siRNA loci have been identified in soybean, but, prior to this work, few of the inducing miRNAs have been experimentally validated, much less used to silence genes. Nine ta‐siRNA loci and their respective miRNA targets were identified, and the abundance of the inducing miRNAs varies dramatically in different tissues. The miRNA targets were experimentally verified by silencing a transgenic GFP gene and two endogenous genes in hairy roots and transgenic plants. Small RNAs were produced in patterns consistent with the utilization of the ta‐siRNA pathway. A side‐by‐side experiment demonstrated that MIGS is as effective at inducing gene silencing as traditional hairpin vectors in soybean hairy roots. Soybean plants transformed with MIGS vectors produced siRNAs and silencing was observed in the T1 generation. These results complement previous reports in Arabidopsis by demonstrating that MIGS is an efficient way to produce siRNAs and induce gene silencing in other species, as shown with soybean. The miRNA targets identified here are simple to incorporate into silencing vectors and offer an effective and efficient alternative to other gene silencing strategies.     

The impact of target site accessibility on the design of effective siRNAs

Small-interfering RNAs (siRNAs) assemble into RISC, the RNA-induced silencing complex, which cleaves complementary mRNAs. Despite their fluctuating efficacy, siRNAs are widely used to assess gene function. Although this limitation could be ascribed, in part, to variations in the assembly and activation of RISC, downstream events in the RNA interference (RNAi) pathway, such as target site accessibility, have so far not been investigated extensively. In this study we present a comprehensive analysis of target RNA structure effects on RNAi by computing the accessibility of the target site for interaction with the siRNA. Based on our observations, we developed a novel siRNA design tool, RNAxs, by combining known siRNA functionality criteria with target site accessibility. We calibrated our method on two data sets comprising 573 siRNAs for 38 genes, and tested it on an independent set of 360 siRNAs targeting four additional genes. Overall, RNAxs proves to be a robust siRNA selection tool that substantially improves the prediction of highly efficient siRNAs.     

Functional comparison of single- and double-stranded siRNAs in mammalian cells

The concept of small interfering RNA (siRNA) has been extended to include not only short double-stranded RNA of 19-25bp, but also single-stranded antisense RNA of the same length, since such single-stranded antisense siRNAs were recently found to be able to inhibit gene expression as well. We made comprehensive comparison of double- and single-stranded siRNA functions in RNA interference (RNAi), targeting multiple sites and different mRNAs, measuring RNAi effects at different time-points and in different cell lines, and examining response curves. Duplex siRNAs were found to be more potent than single-stranded antisense siRNAs. This was verified by the observation that single-stranded antisense siRNAs, which were inefficient in some cases when used alone, could be rescued from inefficiency by sequentially transfecting with the sense siRNAs. This result suggests that the structural character of siRNA molecules might be a more important determinant of siRNA efficiency than the cellular persistence of them.