siRecords: an extensive database of mammalian siRNAs with efficacy ratings

siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis

The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from traditional data analysis to advanced machine learning techniques. However, previous studies have inadequately considered the full complexity of the siRNA silencing mechanism, neglecting critical elements such as siRNA positioning on mRNA, RNA base-pairing probabilities, and RNA-AGO2 interactions, thereby limiting the insight and accuracy of existing models. Here, we introduce siRNADiscovery, a Graph Neural Network (GNN) framework that leverages both non-empirical and empirical rule-based features of siRNA and mRNA to effectively capture the complex dynamics of gene silencing. On multiple internal datasets, siRNADiscovery achieves state-of-the-art performance. Significantly, siRNADiscovery also outperforms existing methodologies in in vitro studies and on an externally validated dataset. Additionally, we develop a new data-splitting methodology that addresses the data leakage issue, a frequently overlooked problem in previous studies, ensuring the robustness and stability of our model under various experimental settings. Through rigorous testing, siRNADiscovery has demonstrated remarkable predictive accuracy and robustness, making significant contributions to the field of gene silencing. Furthermore, our approach to redefining data-splitting standards aims to set new benchmarks for future research in the domain of predictive biological modeling for siRNA.

Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy

The rational modification of siRNA molecules is crucial for ensuring their drug-like properties. Machine learning-based prediction of chemically modified siRNA (cm-siRNA) efficiency can significantly optimize the design process of siRNA chemical modifications, saving time and cost in siRNA drug development. However, existing in-silico methods suffer from limitations such as small datasets, inadequate data representation capabilities, and lack of interpretability. Therefore, in this study, we developed the Cm-siRPred algorithm based on a multi-view learning strategy. The algorithm employs a multi-view strategy to represent the double-strand sequences, chemical modifications, and physicochemical properties of cm-siRNA. It incorporates a cross-attention model to globally correlate different representation vectors and a two-layer CNN module to learn local correlation features. The algorithm demonstrates exceptional performance in cross-validation experiments, independent dataset, and case studies on approved siRNA drugs, and showcasing its robustness and generalization ability. In addition, we developed a user-friendly webserver that enables efficient prediction of cm-siRNA efficiency and assists in the design of siRNA drug chemical modifications. In summary, Cm-siRPred is a practical tool that offers valuable technical support for siRNA chemical modification and drug efficiency research, while effectively assisting in the development of novel small nucleic acid drugs. Cm-siRPred is freely available at https://cellknowledge.com.cn/sirnapredictor/.

Cheminformatics Modeling of Gene Silencing for Both Natural and Chemically Modified siRNAs

In designing effective siRNAs for a specific mRNA target, it is critically important to have predictive models for the potency of siRNAs. None of the published methods characterized the chemical structures of individual nucleotides constituting a siRNA molecule; therefore, they cannot predict the potency of gene silencing by chemically modified siRNAs (cm-siRNA). We propose a new approach that can predict the potency of gene silencing by cm-siRNAs, which characterizes each nucleotide (NT) using 12 BCUT cheminformatics descriptors describing its charge distribution, hydrophobic and polar properties. Thus, a 21-NT siRNA molecule is described by 252 descriptors resulting from concatenating all the BCUT values of its composing nucleotides. Partial Least Square is employed to develop statistical models. The Huesken data (2431 natural siRNA molecules) were used to perform model building and evaluation for natural siRNAs. Our results were comparable with or superior to those from Huesken's algorithm. The Bramsen dataset (48 cm-siRNAs) was used to build and test the models for cm-siRNAs. The predictive r2 of the resulting models reached 0.65 (or Pearson r values of 0.82). Thus, this new method can be used to successfully model gene silencing potency by both natural and chemically modified siRNA molecules.

siRNA Design and GalNAc-Empowered Hepatic Targeted Delivery

Small interfering RNA (siRNA) is a clinically approved therapeutic modality, which has attracted widespread attention not only from basic research but also from pharmaceutical industry. As siRNA can theoretically modulate any disease-related gene's expression, plenty of siRNA therapeutic pipelines have been established by tens of biotechnology companies. The drug performance of siRNA heavily depends on the sequence, the chemical modification, and the delivery of siRNA. Here, we describe the rational design protocol of siRNA, and provide some modification patterns that can enhance siRNA's stability and reduce its off-target effect. Also, the delivery method based on N-acetylgalactosamine (GalNAc)-siRNA conjugate that is widely employed to develop therapeutic regimens for liver-related diseases is also recapitulated.

PVsiRNAPred: Prediction of plant exclusive virus-derived small interfering RNAs by deep convolutional neural network

Plant exclusive virus-derived small interfering RNAs (vsiRNAs) regulate various biological processes, especially important in antiviral immunity. The identification of plant vsiRNAs is important for understanding the biogenesis and function mechanisms of vsiRNAs and further developing anti-viral plants. In this study, we extracted plant vsiRNA sequences from the PVsiRNAdb database. We then utilized deep convolutional neural network (CNN) to develop a deep learning algorithm for predicting plant vsiRNAs based on vsiRNA sequence composition, known as PVsiRNAPred. The key part of PVsiRNAPred is the CNN module, which automatically learns hierarchical representations of vsiRNA sequences related to vsiRNA profiles in plants. When evaluated using an independent testing dataset, the accuracy of the model was 65.70%, which was higher than those of five conventional machine learning method-based classifiers. In addition, PVsiRNAPred obtained a sensitivity of 67.11%, specificity of 64.26% and Matthews correlation coefficient (MCC) of 0.31, and the area under the receiver operating characteristic (ROC) curve (AUC) of PVsiRNAPred was 0.71 in the independent test. The permutation test with 1000 shuffles resulted in a p value of<0.001. The above results reveal that PVsiRNAPred has favorable generalization capabilities. We hope PVsiRNAPred, the first bioinformatics algorithm for predicting plant vsiRNAs, will allow efficient discovery of new vsiRNAs.

Strategies for improving the specificity of siRNAs for enhanced therapeutic potential

INTRODUCTION

RNA interference has become a tool of choice in the development of drugs in various therapeutic areas of Post Transcriptional Gene Silencing (PTGS). The critical element in developing successful RNAi therapeutics lies in designing small interfering RNA (siRNA) using an efficient algorithm satisfying the designing criteria. Further, translation of siRNA from bench-side to bedside needs an efficient delivery system and/or chemical modification. Areas covered: This review emphasizes the importance of dicer, the criteria for efficient siRNA design, the currently available algorithms and strategies to overcome off-target effects, immune stimulatory effects and endosomal trap. Expert opinion: Specificity and stability are the primary concerns for siRNA therapeutics. The design criteria and algorithms should be chosen rationally to have a siRNA sequence that binds to the corresponding mRNA as it happens in the Watson and Crick base pairing. However, it must evade a few more hurdles (Endocytosis, Serum stability etc.) to be functional in the cytosol.

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.

Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity

Small interfering RNAs (siRNAs) induce posttranscriptional gene silencing in various organisms. siRNAs targeted to different positions of the same gene show different effectiveness; hence, predicting siRNA activity is a crucial step. In this paper, we developed and evaluated a powerful tool named “siRNApred” with a new mixed feature set to predict siRNA activity. To improve the prediction accuracy, we proposed 2-3NTs as our new features. A Random Forest siRNA activity prediction model was constructed using the feature set selected by our proposed Binary Search Feature Selection (BSFS) algorithm. Experimental data demonstrated that the binding site of the Argonaute protein correlates with siRNA activity. “siRNApred” is effective for selecting active siRNAs, and the prediction results demonstrate that our method can outperform other current siRNA activity prediction methods in terms of prediction accuracy.

Silencing efficacy prediction: a retrospective study on target mRNA features

Post-transcriptional gene silencing is a widely used method to suppress gene expression. Unfortunately only a portion of siRNAs do successfully reduce gene expression. Target mRNA secondary structures and siRNA-mRNA thermodynamic features are believed to contribute to the silencing activity. However, there is still an open discussion as to what determines siRNA efficacy. In this retrospective study, we analysed the target accessibility comparing very high (VH) compared with low (L) efficacy siRNA sequences obtained from the siRecords Database. We determined the contribution of mRNA target local secondary structures on silencing efficacy. Both the univariable and the multivariable logistic regression evidenced no relationship between siRNA efficacy and mRNA target secondary structures. Moreover, none of the thermodynamic and sequence-base parameters taken into consideration (H-b index, ΔG°_overall, ΔG°_duplex, ΔG°_break-target and GC%) was associated with siRNA efficacy. We found that features believed to be predictive of silencing efficacy are not confirmed to be so when externally evaluated in a large heterogeneous sample. Although it was proposed that silencing efficacy could be influenced by local target accessibility we show that this could be not generalizable because of the diversity of experimental setting that may not be representative of biological systems especially in view of the many local protein factors, usually not taken into consideration, which could hamper the silencing process.

We analysed several siRNA-mRNA target features involved in silencing efficacy. We found out that features believed to be predictive of silencing efficacy are not such when transferred to a larger dataset of experiments and different experimental settings.

A semi-supervised tensor regression model for siRNA efficacy prediction

BACKGROUND

Short interfering RNAs (siRNAs) can knockdown target genes and thus have an immense impact on biology and pharmacy research. The key question of which siRNAs have high knockdown ability in siRNA research remains challenging as current known results are still far from expectation.

RESULTS

This work aims to develop a generic framework to enhance siRNA knockdown efficacy prediction. The key idea is first to enrich siRNA sequences by incorporating them with rules found for designing effective siRNAs and representing them as enriched matrices, then to employ the bilinear tensor regression to predict knockdown efficacy of those matrices. Experiments show that the proposed method achieves better results than existing models in most cases.

CONCLUSIONS

Our model not only provides a suitable siRNA representation but also can predict siRNA efficacy more accurate and stable than most of state-of-the-art models. Source codes are freely available on the web at: http://www.jaist.ac.jp/\~bao/BiLTR/ .

Bioinformatics of siRNA design

RNA interference mediated by small interfering RNAs is a powerful tool for investigation of gene functions and is increasingly used as a therapeutic agent. However, not all siRNAs are equally potent, and although simple rules for the selection of good siRNAs were proposed early on, siRNAs are still plagued with widely fluctuating efficiency. Recently, new design tools incorporating both the structural features of the targeted RNAs and the sequence features of the siRNAs substantially improved the efficacy of siRNAs. In this chapter we will present a review of sequence and structure-based algorithms behind them.

An siRNA designing tool with a unique functional off-target filtering approach

Investigations have revealed that silencing unwanted transcripts or off-targeting can induce false positive phenotype during RNA interference (RNAi)-based gene function study. But still the standard computational approaches towards small interfering RNA (siRNA) off-target minimization fall short in terms of addressing this false positive phenotype issue. Some of these off-targets may interfere with the biochemical pathway being investigated. It may also inadvertently target cell's metabolic pathways with unquantifiable consequences on the processes of user's interest. Here, we report the development of a siRNA selection tool that, for the first time, implements a functional off-target filtering that aims to minimize false positive phenotypes arising from inadvertent targets that are functionally similar or related to the direct target gene, along with a multi-parametric classifier (support vector machine) for optimized selection of potent siRNAs. The functional off-target filtering minimizes the number of off-target genes which are functionally related to the direct target gene, i.e. involved in a common biological process and may have similar phenotype. A text-mining algorithm is used to find related biological processes associated with the direct target and each off-target transcript by comparison of the biological processes associated with these genes. It also gives the user a choice to select one or more off-targets that may be potentially more harmful, from a predicted off-target gene list to be filtered out. Testing with huge set of biologically validated siRNAs from three different sources showed consistent good performance of our tool in terms of effective siRNA selection. It outperformed four potent siRNA selection algorithms of present day in terms of specificity in the selection of highly efficient siRNAs when compared on a common test set. A genome wide testing with potent siRNAs used in high-content screening confirmed validation of 2767 designed siRNAs in terms of phenotypic output. This tool presently supports siRNA designs for human genes and is freely available at http://gyanxet-beta.com .

Reconsideration of in-silico siRNA design based on feature selection: a cross-platform data integration perspective

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html.

VIRsiRNAdb: a curated database of experimentally validated viral siRNA/shRNA

RNAi technology has been emerging as a potential modality to inhibit viruses during past decade. In literature a few siRNA databases have been reported that focus on targeting human and mammalian genes but experimentally validated viral siRNA databases are lacking. We have developed VIRsiRNAdb, a manually curated database having comprehensive details of 1358 siRNA/shRNA targeting viral genome regions. Further, wherever available, information regarding alternative efficacies of above 300 siRNAs derived from different assays has also been incorporated. Important fields included in the database are siRNA sequence, virus subtype, target genome region, cell type, target object, experimental assay, efficacy, off-target and siRNA matching with reference viral sequences. Database also provides the users with facilities of advance search, browsing, data submission, linking to external databases and useful siRNA analysis tools especially siTarAlign which align the siRNA with reference viral genomes or user defined sequences. VIRsiRNAdb contains extensive details of siRNA/shRNA targeting 42 important human viruses including influenza virus, hepatitis B virus, HPV and SARS Corona virus. VIRsiRNAdb would prove useful for researchers in picking up the best viral siRNA for antiviral therapeutics development and also for developing better viral siRNA design tools. The database is freely available at http://crdd.osdd.net/servers/virsirnadb.

mRNA turnover rate limits siRNA and microRNA efficacy

Based on a simple model of the mRNA life cycle, we predict that mRNAs with high turnover rates in the cell are more difficult to perturb with RNAi.

We test this hypothesis using a luciferase reporter system and obtain additional evidence from a variety of large-scale data sets, including microRNA overexpression experiments and RT–qPCR-based efficacy measurements for thousands of siRNAs.

Our results suggest that mRNA half-lives will influence how mRNAs are differentially perturbed whenever small RNA levels change in the cell, not only after transfection but also during differentiation, pathogenesis and normal cell physiology.

What determines how strongly an mRNA responds to a microRNA or an siRNA? We know that properties of the sequence match between the small RNA and the mRNA are crucial. However, large-scale validations of siRNA efficacies have shown that certain transcripts remain recalcitrant to perturbation even after repeated redesign of the siRNA (Krueger et al, 2007). Weak response to RNAi may thus be an inherent property of the mRNA, but the underlying factors have proven difficult to uncover.

siRNAs induce degradation by sequence-specific cleavage of their target mRNAs (Elbashir et al, 2001). MicroRNAs, too, induce mRNA degradation, and ∼80% of their effect on protein levels can be explained by changes in transcript abundance (Hendrickson et al, 2009; Guo et al, 2010). Given that multiple factors act simultaneously to degrade individual mRNAs, we here consider whether variable responses to micro/siRNA regulation may, in part, be explained simply by the basic dynamics of mRNA turnover. If a transcript is already under strong destabilizing regulation, it is theoretically possible that the relative change in abundance after the addition of a novel degrading factor would be less pronounced compared with a stable transcript (Figure 1). mRNA turnover is achieved by a multitude of factors, and the influence of such factors on targetability can be explored. However, their combined action, including yet unknown factors, is summarized into a single property: the mRNA decay rate.

First, we explored the theoretical relationship between the pre-existing turnover rate of an mRNA, and its expected susceptibility to perturbation by a small RNA. We assumed a basic model of the mRNA life cycle, in which the rate of transcription is constant and the rate of degradation is described by first-order kinetics. Under this model, the relative change in steady-state expression level will become smaller as the pre-existing decay rate grows larger, independent of the transcription rate. This relationship persists also if we assume various degrees of synergy and antagonism between the pre-existing factors and the external factor, with increasing synergism leading to transcripts being more equally targetable, regardless of their pre-existing decay rate.

We next generated a series of four luciferase reporter constructs with destabilizing AU-rich elements (AREs) of various strengths incorporated into their 3′ UTRs. To evaluate how the different constructs would respond to perturbation, we performed co-transfections with an siRNA targeted at the coding region of the luciferase gene. This reduced the signal of the non-destabilized construct to 26% compared with a control siRNA. In contrast, the most destabilized construct showed 42% remaining reporter activity, and we could observe a dose–response relationship across the series.

The reporter experiment encouraged an investigation of this effect on real-world mRNAs. We analyzed a set of 2622 siRNAs, for which individual efficacies were determined using RT–qPCR 48 h post-transfection in HeLa cells (www.appliedbiosystems.com). Of these, 1778 could be associated with an experimentally determined decay rate (Figure 4A). Although the overall correlation between the two variables was modest (Spearman's rank correlation r_s=0.22, P<1e−20), we found that siRNAs directed at high-turnover (t_1/2<200 min) and medium-turnover (200<t_1/2<1000 min) mRNAs caused significantly less repression than those targeting long-lived (t_1/2>1000 min) transcripts (P<8e−11 and 4e−9, respectively, two-tailed KS-test, Figure 4B). While 41.6% (498/1196) of the siRNAs directed at low-turnover transcripts reached 10% remaining expression or better, only 16.7% (31/186) of the siRNAs that targeted high-turnover mRNAs reached this high degree of silencing (Figure 4B). Reduced targetability (25.2%, 100/396) was also seen for transcripts with medium-turnover rate.

Our results based on siRNA data suggested that turnover rates could also influence microRNA targeting. By assembling genome-wide mRNA expression data from 20 published microRNA transfections in HeLa cells, we found that predicted target mRNAs with short and medium half-life were significantly less repressed after transfection than their long-lived counterparts (P<8e−5 and P<0.03, respectively, two-tailed KS-test). Specifically, 10.2% (293/2874) of long-lived targets versus 4.4% (41/942) of short-lived targets were strongly (z-score <−3) repressed. siRNAs are known to cause off-target effects that are mediated, in part, by microRNA-like seed complementarity (Jackson et al, 2006). We analyzed changes in transcript levels after transfection of seven different siRNAs, each with a unique seed region (Jackson et al, 2006). Putative ‘off-targets' were identified by mapping of non-conserved seed matches in 3′ UTRs. We found that low-turnover mRNAs (t_1/2 >1000 min) were more affected by seed-mediated off-target silencing than high-turnover mRNAs (t_1/2 <200 min), with twice as many long-lived seed-containing transcripts (3.8 versus 1.9%) being strongly (z-score <−3) repressed.

In summary, mRNA turnover rates have an important influence on the changes exerted by small RNAs on mRNA levels. It can be assumed that mRNA half-lives will influence how mRNAs are differentially perturbed whenever small RNA levels change in the cell, not only after transfection but also during differentiation, pathogenesis and normal cell physiology.

The microRNA pathway participates in basic cellular processes and its discovery has enabled the development of si/shRNAs as powerful investigational tools and potential therapeutics. Based on a simple kinetic model of the mRNA life cycle, we hypothesized that mRNAs with high turnover rates may be more resistant to RNAi-mediated silencing. The results of a simple reporter experiment strongly supported this hypothesis. We followed this with a genome-wide scale analysis of a rich corpus of experiments, including RT–qPCR validation data for thousands of siRNAs, siRNA/microRNA overexpression data and mRNA stability data. We find that short-lived transcripts are less affected by microRNA overexpression, suggesting that microRNA target prediction would be improved if mRNA turnover rates were considered. Similarly, short-lived transcripts are more difficult to silence using siRNAs, and our results may explain why certain transcripts are inherently recalcitrant to perturbation by small RNAs.

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.

Toward a complete in silico, multi-layered embryonic stem cell regulatory network

Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high-throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high-throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi-layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low-throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature.

Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study

BACKGROUND

Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs.

RESULTS

An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy.

CONCLUSIONS

The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism.

Designing optimal siRNA based on target site accessibility

RNA interference, mediated by small interfering RNAs (siRNAs), is a powerful tool for investigation of gene functions and it is increasingly being used as a therapeutic agent. However, not all siRNAs are equally potent - although simple rules for the selection of good siRNAs were proposed early on, siRNAs are still plagued with widely fluctuating efficiency. Recently, new design tools that incorporate both the structural features of the targeted RNAs and the sequence features of the siRNAs have substantially improved the efficacy of siRNAs. In this chapter, we present the algorithms behind these accessibility-aided tools and show how to design efficient siRNAs with their help.

siRNAs: their potential as therapeutic agents--Part I. Designing of siRNAs

RNA interference (RNAi) is a novel and essential biological process, as well as a powerful experimental tool with the potential to be used in therapeutic development. RNAi-based strategies have the capability of being able to be driven from bench to bedside. It is very important to develop the precise tools for designing the siRNAs to get the most efficient knockdown of the target genes and to reduce any off-target effects. In this review we have discussed the strategies and parameters required for effective siRNA designing and synthesis, based on already published literature.

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.

www.rnaworkbench.com: A new program for analyzing RNA interference

RNA interference (RNAi) has become an important tool to study and utilize gene silencing by introducing short interfering RNA (siRNA). In order to predict the most efficient siRNAs, a new software tool, RNA Workbench (RNAWB), has been designed and is freely available (after registration) on http://www.rnaworkbench.com. In addition to the standard selection rules, RNAWB includes the possibility of statistical analyses of the applied selection rules (criteria). The role of RNA secondary structures in the RNA interference process as well as the application of sequence rules are discussed to show the applicability of the software.

Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities

We developed a simple algorithm, i-Score (inhibitory-Score), to predict active siRNAs by applying a linear regression model to 2431 siRNAs. Our algorithm is exclusively comprised of nucleotide (nt) preferences at each position, and no other parameters are taken into account. Using a validation dataset comprised of 419 siRNAs, we found that the prediction accuracy of i-Score is as good as those of s-Biopredsi, ThermoComposition21 and DSIR, which employ a neural network model or more parameters in a linear regression model. Reynolds and Katoh also predict active siRNAs efficiently, but the numbers of siRNAs predicted to be active are less than one-eighth of that of i-Score. We additionally found that exclusion of thermostable siRNAs, whose whole stacking energy (ΔG) is less than −34.6 kcal/mol, improves the prediction accuracy in i-Score, s-Biopredsi, ThermoComposition21 and DSIR. We also developed a universal target vector, pSELL, with which we can assay an siRNA activity of any sequence in either the sense or antisense direction. We assayed 86 siRNAs in HEK293 cells using pSELL, and validated applicability of i-Score and the whole ΔG value in designing siRNAs.

sIR: siRNA Information Resource, a web-based tool for siRNA sequence design and analysis and an open access siRNA database

Background

RNA interference has revolutionized our ability to study the effects of altering the expression of single genes in mammalian (and other) cells through targeted knockdown of gene expression. In this report we describe a web-based computational tool, siRNA Information Resource (sIR), which consists of a new open source database that contains validation information about published siRNA sequences and also provides a user-friendly interface to design and analyze siRNA sequences against a chosen target sequence.

Results

The siRNA design tool described in this paper employs empirically determined rules derived from a meta-analysis of the published data; it uses a weighted scoring system that determines the optimal sequence within a target mRNA and thus aids in the rational selection of siRNA sequences. This scoring system shows a non-linear correlation with the knockdown efficiency of siRNAs. sIR provides a fast, customized BLAST output for all selected siRNA sequences against a variety of databases so that the user can verify the uniqueness of the design. We have pre-designed siRNAs for all the known human genes (24,502) in the Refseq database. These siRNAs were pre-BLASTed against the human Unigene database to estimate the target specificity and all results are available online.

Conclusion

Although most of the rules for this scoring system were influenced by previously published rules, the weighted scoring system provides better flexibility in designing an appropriate siRNA when compared to the un-weighted scoring system. sIR is not only a comprehensive tool used to design siRNA sequences and lookup pre-designed siRNAs, but it is also a platform where researchers can share information on siRNA design and use.

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.

An accurate and interpretable model for siRNA efficacy prediction

Background

The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging.

Results

We propose a simple linear model combining basic features of siRNA sequences for siRNA efficacy prediction. Trained and tested on a large dataset of siRNA sequences made recently available, it performs as well as more complex state-of-the-art models in terms of potency prediction accuracy, with the advantage of being directly interpretable. The analysis of this linear model allows us to detect and quantify the effect of nucleotide preferences at particular positions, including previously known and new observations. We also detect and quantify a strong propensity of potent siRNAs to contain short asymmetric motifs in their sequence, and show that, surprisingly, these motifs alone contain at least as much relevant information for potency prediction as the nucleotide preferences for particular positions.

Conclusion

The model proposed for prediction of siRNA potency is as accurate as a state-of-the-art nonlinear model and is easily interpretable in terms of biological features. It is freely available on the web at

Integrated siRNA design based on surveying of features associated with high RNAi effectiveness

Background

Short interfering RNAs have allowed the development of clean and easily regulated methods for disruption of gene expression. However, while these methods continue to grow in popularity, designing effective siRNA experiments can be challenging. The various existing siRNA design guidelines suffer from two problems: they differ considerably from each other, and they produce high levels of false-positive predictions when tested on data of independent origins.

Results

Using a distinctly large set of siRNA efficacy data assembled from a vast diversity of origins (the siRecords data, containing records of 3,277 siRNA experiments targeting 1,518 genes, derived from 1,417 independent studies), we conducted extensive analyses of all known features that have been implicated in increasing RNAi effectiveness. A number of features having positive impacts on siRNA efficacy were identified. By performing quantitative analyses on cooperative effects among these features, then applying a disjunctive rule merging (DRM) algorithm, we developed a bundle of siRNA design rule sets with the false positive problem well curbed. A comparison with 15 online siRNA design tools indicated that some of the rule sets we developed surpassed all of these design tools commonly used in siRNA design practice in positive predictive values (PPVs).

Conclusion

The availability of the large and diverse siRNA dataset from siRecords and the approach we describe in this report have allowed the development of highly effective and generally applicable siRNA design rule sets. Together with ever improving RNAi lab techniques, these design rule sets are expected to make siRNAs a more useful tool for molecular genetics, functional genomics, and drug discovery studies.

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/).

On the art of identifying effective and specific siRNAs

Small interfering RNAs (siRNAs) have been widely exploited for sequence-specific gene knockdown, predominantly to investigate gene function in cultured vertebrate cells, and also hold promise as therapeutic agents. Because not all siRNAs that are cognate to a given target mRNA are equally effective, computational tools have been developed based on experimental data to increase the likelihood of selecting effective siRNAs. Furthermore, because target-complementary siRNAs can also target other mRNAs containing sequence segments that are partially complementary to the siRNA, most computational tools include ways to reduce potential off-target effects in the siRNA selection process. Though these methods facilitate selection of functional siRNAs, they do not yet alleviate the need for experimental validation. This perspective provides a practical guide based on current wisdom for selecting siRNAs.