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

PFRED: A computational platform for siRNA and antisense oligonucleotides design

PFRED a software application for the design, analysis, and visualization of antisense oligonucleotides and siRNA is described. The software provides an intuitive user-interface for scientists to design a library of siRNA or antisense oligonucleotides that target a specific gene of interest. Moreover, the tool facilitates the incorporation of various design criteria that have been shown to be important for stability and potency. PFRED has been made available as an open-source project so the code can be easily modified to address the future needs of the oligonucleotide research community. A compiled version is available for downloading at https://github.com/pfred/pfred-gui/releases/tag/v1.0 as a java Jar file. The source code and the links for downloading the precompiled version can be found at https://github.com/pfred.

RNA Interference and Small RNA Analysis

Small silencing RNAs have provided powerful reverse genetics tools and have opened new areas of research. This introduction describes the use of RNAi to suppress expression of individual genes for loss-of-function analysis. It also summarizes methods for measuring specific and global changes in small RNA expression, as well as methods to inhibit the function of individual endogenous small RNA species such as miRNAs.

Milligram scale production of potent recombinant small interfering RNAs in Escherichia coli

Small interfering RNAs (siRNAs) are invaluable research tools for studying gene functions in mammalian cells. siRNAs are mainly produced by chemical synthesis or by enzymatic digestion of double-stranded RNA (dsRNA) produced in vitro. Recently, bacterial cells, engineered with ectopic plant viral siRNA binding protein p19, have enabled the production of "recombinant" siRNAs (pro-siRNAs). Here, we describe an optimized methodology for the production of milligram amount of highly potent recombinant pro-siRNAs from Escherichia coli cells. We first optimized bacterial culture medium and tested new designs of pro-siRNA production plasmid. Through the exploration of multiple pro-siRNA related factors, including the expression of p19 protein, (dsRNA) generation method, and the level of RNase III, we developed an optimal pro-siRNA production plasmid. Together with a high-cell density fed-batch fermentation method in a bioreactor, we have achieved a yield of ~10 mg purified pro-siRNA per liter of bacterial culture. The pro-siRNAs produced by the optimized method can achieve high efficiency of gene silencing when used at low nanomolar concentrations. This new method enables fast, economical, and renewable production of pure and highly potent bioengineered pro-siRNAs at the milligram level. Our study also provides important insights into the strategies for optimizing the production of RNA products in bacteria, which is an under-explored field.

Computational Tools: RNA Interference in Fungal Therapeutics

There is steady rise in the number of immunocompromised population due to increased use of potent immunosuppression therapies. This is associated with increased risk of acquiring fungal opportunistic infections in immunocompromised patients which account for high morbidity and mortality rates, if left untreated. The conventional antifungal drugs to treat fungal diseases (mycoses) are increasingly becoming inadequate due to observed varied susceptibility of fungi and their recurrent resistance. RNA interference (RNAi), sequence-specific gene silencing, is emerging as a promising new therapeutic approach. This chapter discusses various aspects of RNAi, viz., the fundamental RNAi machinery present in fungi, in silico siRNA features, designing guidelines and tools, siRNA delivery, and validation of gene knockdown for therapeutics against mycoses. Target gene identification is a crucial step in designing of gene-specific siRNA in addition to efficient delivery strategies to bring about effective inhibition of fungi. Subsequently, designed siRNA can be delivered effectively in vitro either by soaking fungi with siRNA or by transforming inverted repeat transgene containing plasmid into fungi, which ultimately generates siRNA(s). Finally, fungal inhibition can be verified at the RNA and protein levels by blotting techniques, fluorescence imaging, and biochemical assays. Despite challenges, several such in vitro studies have spawned optimism around RNAi as a revolutionary new class of therapeutics against mycoses. But, pharmacokinetic parameters need to be evaluated from in vivo studies and clinical trials to recognize RNAi as a novel treatment approach for mycoses.

ASPsiRNA: A Resource of ASP-siRNAs Having Therapeutic Potential for Human Genetic Disorders and Algorithm for Prediction of Their Inhibitory Efficacy

Allele-specific siRNAs (ASP-siRNAs) have emerged as promising therapeutic molecules owing to their selectivity to inhibit the mutant allele or associated single-nucleotide polymorphisms (SNPs) sparing the expression of the wild-type counterpart. Thus, a dedicated bioinformatics platform encompassing updated ASP-siRNAs and an algorithm for the prediction of their inhibitory efficacy will be helpful in tackling currently intractable genetic disorders. In the present study, we have developed the ASPsiRNA resource (http://crdd.osdd.net/servers/aspsirna/) covering three components viz (i) ASPsiDb, (ii) ASPsiPred, and (iii) analysis tools like ASP-siOffTar. ASPsiDb is a manually curated database harboring 4543 (including 422 chemically modified) ASP-siRNAs targeting 78 unique genes involved in 51 different diseases. It furnishes comprehensive information from experimental studies on ASP-siRNAs along with multidimensional genetic and clinical information for numerous mutations. ASPsiPred is a two-layered algorithm to predict efficacy of ASP-siRNAs for fully complementary mutant (Eff^mut) and wild-type allele (Eff^wild) with one mismatch by ASPsiPred^SVM and ASPsiPred^matrix, respectively. In ASPsiPred^SVM, 922 unique ASP-siRNAs with experimentally validated quantitative Eff^mut were used. During 10-fold cross-validation (10nCV) employing various sequence features on the training/testing dataset (T737), the best predictive model achieved a maximum Pearson’s correlation coefficient (PCC) of 0.71. Further, the accuracy of the classifier to predict Eff^mut against novel genes was assessed by leave one target out cross-validation approach (LOTOCV). ASPsiPred^matrix was constructed from rule-based studies describing the effect of single siRNA:mRNA mismatches on the efficacy at 19 different locations of siRNA. Thus, ASPsiRNA encompasses the first database, prediction algorithm, and off-target analysis tool that is expected to accelerate research in the field of RNAi-based therapeutics for human genetic diseases.

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

BACKGROUND

Selection 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.

METHODS

In 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.

RESULTS

During 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.

CONCLUSION

VIRsiRNApred 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/.

Design of siRNA Therapeutics from the Molecular Scale

While protein-based therapeutics is well-established in the market, development of nucleic acid therapeutics has lagged. Short interfering RNAs (siRNAs) represent an exciting new direction for the pharmaceutical industry. These small, chemically synthesized RNAs can knock down the expression of target genes through the use of a native eukaryotic pathway called RNA interference (RNAi). Though siRNAs are routinely used in research studies of eukaryotic biological processes, transitioning the technology to the clinic has proven challenging. Early efforts to design an siRNA therapeutic have demonstrated the difficulties in generating a highly-active siRNA with good specificity and a delivery vehicle that can protect the siRNA as it is transported to a specific tissue. In this review article, we discuss design considerations for siRNA therapeutics, identifying criteria for choosing therapeutic targets, producing highly-active siRNA sequences, and designing an optimized delivery vehicle. Taken together, these design considerations provide logical guidelines for generating novel siRNA therapeutics.

What parameters to consider and which software tools to use for target selection and molecular design of small interfering RNAs

The design of small gene silencing RNAs with a high probability of being efficient still has some elements of an art, especially when the lowest concentration of small molecules needs to be utilized. The design of highly target-specific small interfering RNAs or short hairpin RNAs is even a greater challenging task. Some logical schemes and software tools that can be used for simplifying both tasks are presented here. In addition, sequence motifs and sequence composition biases of small interfering RNAs that have to be avoided because of specificity concerns are also detailed.

Methods for selecting effective siRNA target sequences using a variety of statistical and analytical techniques

Short interfering RNA (siRNA) has been widely used for studying gene function in mammalian cells but varies markedly in its gene silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are only a few consistencies among them. This makes it difficult to select effective siRNA sequences in mammalian genes. This chapter first reviews the recently reported siRNA design guidelines and then proposes new methods for selecting effective siRNA sequences from many possible candidates by using decision tree learning, Bayes' theorem, and average silencing probability on the basis of a large number of known effective siRNAs. These methods differ from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. Evaluation of these methods by applying them to recently reported effective and ineffective siRNA sequences for a number of genes indicates that they would be useful for many other genes. They should, therefore, be of general utility for selecting effective siRNA sequences for mammalian genes. The chapter also describes another method using a hidden Markov model to select the optimal functional siRNAs and discusses the frequencies of combinations of two successive nucleotides as an important characteristic of effective siRNA sequences.

Reconsideration of in silico siRNA design from a perspective of heterogeneous data integration: problems and solutions

The success of RNA interference (RNAi) depends on the interaction between short interference RNAs (siRNAs) and mRNAs. Design of highly efficient and specific siRNAs has become a challenging issue in applications of RNAi. Here, we present a detailed survey on the state-of-the-art siRNAs design, focusing on several key issues with the current in silico RNAi studies, including: (i) inconsistencies among the proposed guidelines for siRNAs design and the incomplete list of siRNAs features, (ii) improper integration of the heterogeneous cross-platform siRNAs data, (iii) inadequate consideration of the binding specificity of the target mRNAs and (iv) reduction in the 'off-target' effect in siRNAs design. With these considerations, the popular in silico siRNAs design rules are reexamined and several inconsistent viewpoints toward siRNAs feature identifications are clarified. In addition, novel computational models for siRNAs design using state-of-art machine learning techniques are discussed, which focus on heterogeneous data integration, joint feature selection and customized siRNAs screening toward highly specific targets. We believe that addressing such issues in siRNA study will provide new clues for further improved design of more efficient and specific siRNAs in RNAi.

Mini-clusters with mean probabilities for identifying effective siRNAs

Background

The distinction between the effective siRNAs and the ineffective ones is in high demand for gene knockout technology. To design effective siRNAs, many approaches have been proposed. Those approaches attempt to classify the siRNAs into effective and ineffective classes but they are difficult to decide the boundary between these two classes.

Findings

Here, we try to split effective and ineffective siRNAs into many smaller subclasses by RMP-MiC(the relative mean probabilities of siRNAs with the mini-clusters algorithm). The relative mean probabilities of siRNAs are the modified arithmetic mean value of three probabilities, which come from three Markov chain of effective siRNAs. The mini-clusters algorithm is a modified version of micro-cluster algorithm.

Conclusions

When the RMP-MiC was applied to the experimental siRNAs, the result shows that all effective siRNAs can be identified correctly, and no more than 9% ineffective siRNAs are misidentified as effective ones. We observed that the efficiency of those misidentified ineffective siRNAs exceed 70%, which is very closed to the used efficiency threshold. From the analysis of the siRNAs data, we suggest that the mini-clusters algorithm with relative mean probabilities can provide new insights to the applications for distinguishing effective siRNAs from ineffective ones.

siPRED: predicting siRNA efficacy using various characteristic methods

Small interfering RNA (siRNA) has been used widely to induce gene silencing in cells. To predict the efficacy of an siRNA with respect to inhibition of its target mRNA, we developed a two layer system, siPRED, which is based on various characteristic methods in the first layer and fusion mechanisms in the second layer. Characteristic methods were constructed by support vector regression from three categories of characteristics, namely sequence, features, and rules. Fusion mechanisms considered combinations of characteristic methods in different categories and were implemented by support vector regression and neural networks to yield integrated methods. In siPRED, the prediction of siRNA efficacy through integrated methods was better than through any method that utilized only a single method. Moreover, the weighting of each characteristic method in the context of integrated methods was established by genetic algorithms so that the effect of each characteristic method could be revealed. Using a validation dataset, siPRED performed better than other predictive systems that used the scoring method, neural networks, or linear regression. Finally, siPRED can be improved to achieve a correlation coefficient of 0.777 when the threshold of the whole stacking energy is ≥-34.6 kcal/mol. siPRED is freely available on the web at http://predictor.nchu.edu.tw/siPRED.

Informatics approach to the rational design of siRNA libraries

This chapter surveys the literature for state-of-the-art methods for the rational design of siRNA libraries. It identifies and presents major milestones in the field of computational modeling of siRNA's gene silencing efficacy. Commonly used features of siRNAs are summarized along with major machine learning techniques employed to build the predictive models. It has also outlined several web-enabled siRNA design tools. To face the challenge of modeling and rational design of chemically modified siRNAs, it also proposes a new cheminformatics approach for the representation and characterization of siRNA molecules. Some preliminary results with this new approach are presented to demonstrate the promising potential of this method for the modeling of siRNA's efficacy. Together with novel delivery technologies and chemical modification techniques, rational siRNA design algorithms will ultimately contribute to chemical biology research and the efficient development of siRNA therapeutics.

Cheminformatics Approach to Gene Silencing: Z Descriptors of Nucleotides and SVM Regression Afford Predictive Models for siRNA Potency

Short interfering RNA mediated gene silencing technology has been through tremendous development over the past decade, and has found broad applications in both basic biomedical research and pharmaceutical development. Critical to the effective use of this technology is the development of reliable algorithms to predict the potency and selectivity of siRNAs under study. Existing algorithms are mostly built upon sequence information of siRNAs and then employ statistical pattern recognition or machine learning techniques to derive rules or models. However, sequence-based features have limited ability to characterize siRNAs, especially chemically modified ones. In this study, we proposed a cheminformatics approach to describe siRNAs. Principal component scores (z1, z2, z3, z4) have been derived for each of the 5 nucleotides (A, U, G, C, T) from the descriptor matrix computed by the MOE program. Descriptors of a given siRNA sequence are simply the concatenation of the z values of its composing nucleotides. Thus, for each of the 2431 siRNA sequences in the Huesken dataset, 76 descriptors were generated for the 19-NT representation, and 84 descriptors were generated for the 21-NT representation of siRNAs. Support Vector Machine regression (SVMR) was employed to develop predictive models. In all cases, the models achieved Pearson correlation coefficient r and R about 0.84 and 0.65 for the training sets and test sets, respectively. A minimum of 25 % of the whole dataset was needed to obtain predictive models that could accurately predict 75 % of the remaining siRNAs. Thus, for the first time, a cheminformatics approach has been developed to successfully model the structure-potency relationship in siRNA-based gene silencing data, which has laid a solid foundation for quantitative modeling of chemically modified siRNAs.

Prediction of siRNA potency using sparse logistic regression

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

A role for human Dicer in pre-RISC loading of siRNAs

RNA interference is a powerful mechanism for sequence-specific inhibition of gene expression. It is widely known that small interfering RNAs (siRNAs) targeting the same region of a target-messenger RNA can have widely different efficacies. In efforts to better understand the siRNA features that influence knockdown efficiency, we analyzed siRNA interactions with a high-molecular weight complex in whole cell extracts prepared from two different cell lines. Using biochemical tools to study the nature of the complex, our results demonstrate that the primary siRNA-binding protein in the whole cell extracts is Dicer. We find that Dicer is capable of discriminating highly functional versus poorly functional siRNAs by recognizing the presence of 2-nt 3′ overhangs and the thermodynamic properties of 2–4 bp on both ends of effective siRNAs. Our results suggest a role for Dicer in pre-selection of effective siRNAs for handoff to Ago2. This initial selection is reflective of the overall silencing potential of an siRNA.

Efficient prediction methods for selecting effective siRNA sequences

Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. Another shortcoming of the previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. This paper first reviewed the recently reported siRNA design guidelines and clarified the problems concerning the guidelines. It then proposed two prediction methods-Radial Basis Function (RBF) network and decision tree learning-and their combined method for selecting effective siRNA target sequences from many possible candidate sequences. They are quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The methods imply high estimation accuracy for selecting candidate siRNA sequences.

Computational siRNA design considering alternative splicing

RNA interference (RNAi) with small interfering RNA (siRNA) has become a powerful tool in functional and medical genomic research through directed post-transcriptional gene silencing. In order to apply RNAi technique to eukaryotic organisms, where frequent alternative splicing results in diversification of mRNAs and finally of proteins, we need spliced mRNA isoform silencing to study the function of individual proteins. AsiDesigner is a web-based siRNA design software system, which provides siRNA design capability to account for alternative splicing in mRNA level gene silencing. It provides numerous novel functions, including designing common siRNAs for the silencing of more than two mRNAs simultaneously, a scoring scheme to evaluate the performance of designed siRNAs by adopting state-of-the-art design factors, stepwise off-target searching with BLAST and FASTA algorithms, as well as checking the folding secondary structure energy of siRNAs. To do this, we developed a novel algorithm to evaluate the common target region where siRNAs can be designed to knockdown a specific mRNA isoform or more than two mRNA isoforms from a target gene simultaneously. The developed algorithm and the AsiDesigner were tested and validated as being very effective throughout widely performed gene silencing experiments. It is expected that AsiDesigner will play an important role in functional genomics, drug discovery, and other molecular biological research. AsiDesigner is freely accessible at http://sysbio.kribb.re.kr/AsiDesigner .

Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs

Background

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.

Principal Findings

Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3×5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.

Conclusions

The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques.

Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency

Motivation: Short interfering RNA (siRNA)-induced RNA interference is an endogenous pathway in sequence-specific gene silencing. The potency of different siRNAs to inhibit a common target varies greatly and features affecting inhibition are of high current interest. The limited success in predicting siRNA potency being reported so far could originate in the small number and the heterogeneity of available datasets in addition to the knowledge-driven, empirical basis on which features thought to be affecting siRNA potency are often chosen. We attempt to overcome these problems by first constructing a meta-dataset of 6483 publicly available siRNAs (targeting mammalian mRNA), the largest to date, and then applying a Bayesian analysis which accommodates feature set uncertainty. A stochastic logistic regression-based algorithm is designed to explore a vast model space of 497 compositional, structural and thermodynamic features, identifying associations with siRNA potency.

Results: Our algorithm reveals a number of features associated with siRNA potency that are, to the best of our knowledge, either under reported in literature, such as anti-sense 5′ −3′ motif ‘UCU’, or not reported at all, such as the anti-sense 5′ -3′ motif ‘ACGA’. These findings should aid in improving future siRNA potency predictions and might offer further insights into the working of the RNA-induced silencing complex (RISC).

Contact:cholmes@stats.ox.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Methods for selecting effective siRNA sequences by using statistical and clustering techniques

Short interfering RNAs (siRNAs) have been widely used for studying gene functions in mammalian cells but vary markedly in their gene-silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are only a few consistencies among them. This makes it difficult to select effective siRNA sequences targeting mammalian genes. This chapter first reviews the reported siRNA design guidelines and clarifies the problems concerning the current guidelines. It then describes the recently reported new scoring methods for selecting effective siRNA sequences by using statistics and clustering techniques such as the self-organizing map (SOM) and the radial basis function (RBF) network. In the proposed three methods, individual scores are defined as a gene degradation measure based on position-specific statistical significances. The effectiveness of the methods was confirmed by evaluating effective and ineffective siRNAs for recently reported genes and comparison with other reported scoring methods. The sizes (values) of these scores are closely correlated with the degree of gene degradation, and the scores can easily be used for selecting high-potential siRNA candidates. The evaluation results indicate that the proposed new methods are useful for selecting siRNA sequences targeting mammalian mRNA sequences.

siRNA, miRNA, and shRNA: in vivo Applications

RNA interference (RNAi), an accurate and potent gene-silencing method, was first experimentally documented in 1998 in Caenorhabditis elegans by Fire et al., who subsequently were awarded the 2006 Nobel Prize in Physiology/Medicine. Subsequent RNAi studies have demonstrated the clinical potential of synthetic small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs) in dental diseases, eye diseases, cancer, metabolic diseases, neurodegenerative disorders, and other illnesses. siRNAs are generally from 21 to 25 base-pairs (bp) in length and have sequence-homology-driven gene-knockdown capability. RNAi offers researchers an effortless tool for investigating biological systems by selectively silencing genes. Key technical aspects—such as optimization of selectivity, stability, in vivo delivery, efficacy, and safety—need to be investigated before RNAi can become a successful therapeutic strategy. Nevertheless, this area shows a huge potential for the pharmaceutical industry around the globe. Interestingly, recent studies have shown that the small RNA molecules, either indigenously produced as microRNAs (miRNAs) or exogenously administered synthetic dsRNAs, could effectively activate a particular gene in a sequence-specific manner instead of silencing it. This novel, but still uncharacterized, phenomenon has been termed ‘RNA activation’ (RNAa). In this review, we analyze these research findings and discussed the in vivo applications of siRNAs, miRNAs, and shRNAs.

The art and design of genetic screens: RNA interference

The remarkable gene knockdown technique of RNAi has opened exciting new avenues for genetic screens in model organisms and human cells. Here we describe the current state of the art for RNAi screening, and stress the importance of well-designed assays and of analytical approaches for large-scale screening experiments, from high-throughput screens using simplified homogenous assays to microscopy and whole-animal experiments. Like classical genetic screens in the past, the success of large-scale RNAi surveys depends on a careful development of phenotypic assays and their interpretation in a relevant biological context.

AsiDesigner: exon-based siRNA design server considering alternative splicing

RNA interference (RNAi) with small interfering RNA (siRNA) has become a powerful tool in functional and medical genomic research through directed post-transcriptional gene silencing. In order to apply RNAi technique for eukaryotic organisms, where frequent alternative splicing results in diversification of mRNAs and finally of proteins, we need spliced mRNA isoform silencing to study the function of individual proteins. AsiDesigner is a web-based siRNA design software system, which provides siRNA design capability to account for alternative splicing for mRNA level gene silencing. It provides numerous novel functions including the designing of common siRNAs for the silencing of more than two mRNAs simultaneously, a scoring scheme to evaluate the performance of designed siRNAs by adopting currently known key design factors, a stepwise off-target searching with BLAST and FASTA algorithms and checking the folding secondary structure energy of siRNAs. To do this, we developed a novel algorithm to evaluate the common target region, where siRNAs can be designed to knockdown a specific mRNA isoform or more than two mRNA isoforms from a target gene simultaneously. The developed algorithm and the AsiDesigner were tested and validated as very effective throughout widely performed gene silencing experiments. It is expected that AsiDesigner will play an important role in functional genomics, drug discovery and other molecular biological research. AsiDesigner is freely accessible at http://sysbio.kribb.re.kr/AsiDesigner/.

A protocol for designing siRNAs with high functionality and specificity

Effective gene silencing by the RNA interference (RNAi) pathway requires a comprehensive understanding of the elements that influence small interfering RNA (siRNA) functionality and specificity. These include (i) sequence space restrictions that define the boundaries of siRNA targeting, (ii) structural and sequence features required for efficient siRNA performance, (iii) mechanisms that underlie nonspecific gene modulation and (iv) additional features specific to the intended use (i.e., inclusion of native sugar or base chemical modifications for increased stability or specificity, vector design, etc.). Attention to each of these factors enhances siRNA performance and heightens overall confidence in the output of RNAi-mediated functional genomic studies. Here, we provide a detailed protocol explaining the methodologies used for manual and web-based design of siRNAs.

RFRCDB-siRNA: improved design of siRNAs by random forest regression model coupled with database searching

Although the observations concerning the factors which influence the siRNA efficacy give clues to the mechanism of RNAi, the quantitative prediction of the siRNA efficacy is still a challenge task. In this paper, we introduced a novel non-linear regression method: random forest regression (RFR), to quantitatively estimate siRNAs efficacy values. Compared with an alternative machine learning regression algorithm, support vector machine regression (SVR) and four other score-based algorithms [A. Reynolds, D. Leake, Q. Boese, S. Scaringe, W.S. Marshall, A. Khvorova, Rational siRNA design for RNA interference, Nat. Biotechnol. 22 (2004) 326-330; K. Ui-Tei, Y. Naito, F. Takahashi, T. Haraguchi, H. Ohki-Hamazaki, A. Juni, R. Ueda, K. Saigo, Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference, Nucleic Acids Res. 32 (2004) 936-948; A.C. Hsieh, R. Bo, J. Manola, F. Vazquez, O. Bare, A. Khvorova, S. Scaringe, W.R. Sellers, A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: determinants of gene silencing for use in cell-based screens, Nucleic Acids Res. 32 (2004) 893-901; M. Amarzguioui, H. Prydz, An algorithm for selection of functional siRNA sequences, Biochem. Biophys. Res. Commun. 316 (2004) 1050-1058) our RFR model achieved the best performance of all. A web-server, RFRCDB-siRNA (http://www.bioinf.seu.edu.cn/siRNA/index.htm), has been developed. RFRCDB-siRNA consists of two modules: a siRNA-centric database and a RFR prediction system. RFRCDB-siRNA works as follows: (1) Instead of directly predicting the gene silencing activity of siRNAs, the service takes these siRNAs as queries to search against the siRNA-centric database. The matched sequences with the exceeding the user defined functionality value threshold are kept. (2) The mismatched sequences are then processed into the RFR prediction system for further analysis.

Specific residues at every third position of siRNA shape its efficient RNAi activity

Small interfering RNA (siRNA) induces sequence-specific post-transcriptional gene silencing in mammalian cells. Different efficacy of each siRNA is considered to result from sequence preference by protein components in RNAi. To obtain mechanistic insight into siRNA functionality, here we describe a complete data set of siRNA activities targeting all possible position of a single mRNA in human cells. Seven hundred and two siRNAs covering open reading frame of enhanced green fluorescent protein mRNA ( 720 bases) were examined with minimized error factors. The most important finding is that specific residues at every third position of siRNAs greatly influence its RNAi activity; the optimized base composition at positions 3n + 1 (4,7,10,13,16,19) in siRNAs have positive effects on the activity, which can explain the waving siRNA activity with 3 nucleotides (nt) periodicity in the sequential positions of mRNAs. Since there was an obvious correlation between siRNA activity and its binding affinity to TRBP, a partner protein of human Dicer, the 3-nt periodicity might correlate with the affinity to TRBP. As an algorithm (‘siExplorer’) developed by this observation successfully calculated the activities of siRNAs targeting endogenous human genes, the 3-nt periodicity provides a new aspect unveiling siRNA functionality.

More complete gene silencing by fewer siRNAs: transparent optimized design and biophysical signature

Highly accurate knockdown functional analyses based on RNA interference (RNAi) require the possible most complete hydrolysis of the targeted mRNA while avoiding the degradation of untargeted genes (off-target effects). This in turn requires significant improvements to target selection for two reasons. First, the average silencing activity of randomly selected siRNAs is as low as 62%. Second, applying more than five different siRNAs may lead to saturation of the RNA-induced silencing complex (RISC) and to the degradation of untargeted genes. Therefore, selecting a small number of highly active siRNAs is critical for maximizing knockdown and minimizing off-target effects. To satisfy these needs, a publicly available and transparent machine learning tool is presented that ranks all possible siRNAs for each targeted gene. Support vector machines (SVMs) with polynomial kernels and constrained optimization models select and utilize the most predictive effective combinations from 572 sequence, thermodynamic, accessibility and self-hairpin features over 2200 published siRNAs. This tool reaches an accuracy of 92.3% in cross-validation experiments. We fully present the underlying biophysical signature that involves free energy, accessibility and dinucleotide characteristics. We show that while complete silencing is possible at certain structured target sites, accessibility information improves the prediction of the 90% active siRNA target sites. Fast siRNA activity predictions can be performed on our web server at http://optirna.unl.edu/.

In silico selection of active siRNA

RNA interference (RNAi) mediated by short interfering RNA (siRNA) represents a powerful reverse genetics tool, and siRNAs are attracting increasing interest as potential therapeutics. Progress in the design of functional siRNAs has significantly contributed to our understanding of cellular RNA silencing pathways and vice versa. Parameters related to RNA sequence and structure have a strong impact on various steps along the silencing pathway and build the backbone of many siRNA design tools. Recent work has demonstrated that there is more to siRNA design than enhancement of gene silencing activity. Current efforts aim at avoidance of off-target effects, the understanding of siRNA-triggered immunostimulation, and evasion of interference with cellular regulatory RNA. Molecular features determining the biological functions of siRNA and their meaning for computational (in silico) selection are the focus of this review.