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1.  GUItars: A GUI Tool for Analysis of High-Throughput RNA Interference Screening Data 
PLoS ONE  2012;7(11):e49386.
High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.
The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.
With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at
PMCID: PMC3502531  PMID: 23185323
2.  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 ( 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 rs=0.22, P<1e−20), we found that siRNAs directed at high-turnover (t1/2<200 min) and medium-turnover (2001000 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 (t1/2 >1000 min) were more affected by seed-mediated off-target silencing than high-turnover mRNAs (t1/2 <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.
PMCID: PMC3010119  PMID: 21081925
microRNA; mRNA decay; RNAi; siRNA
3.  A Direct Phenotypic Comparison of siRNA Pools and Multiple Individual Duplexes in a Functional Assay 
PLoS ONE  2009;4(12):e8471.
RNAi is a prominent tool for the identification of novel regulatory elements within complex cellular pathways. In invertebrates, RNAi is a relatively straightforward process, where large double-stranded RNA molecules initiate sequence-specific transcript destruction in target cells. In contrast, RNAi in mammalian cell culture assays requires the delivery of short interfering RNA duplexes to target cells. Due to concerns over off-target phenotypes and extreme variability in duplex efficiency, investigators typically deliver and analyze multiple duplexes per target. Currently, duplexes are delivered and analyzed either individually or as a pool of several independent duplexes. A choice between experiments based on siRNA pools or multiple individual duplexes has considerable implications for throughput, reagent requirements and data analysis in genome-wide surveys, yet there are relatively few data that directly compare the efficiency of the two approaches.
Methodology/Principal Findings
To address this critical issue, we conducted a direct comparison of siRNA pools and multiple single siRNAs that target all human phosphatases in a robust functional assay. We determined the frequency with which both approaches uncover loss-of-function phenotypes and compared the phenotypic severity for siRNA pools and the constituent individual duplexes.
Our survey indicates that screens with siRNA pools have several significant advantages over identical screens with the corresponding individual siRNA duplexes. Of note, we frequently observed greater phenotypic penetrance for siRNA pools than for the parental individual duplexes. Thus, our data indicate that experiments with siRNA pools have a greater likelihood of generating loss-of-function phenotypes than individual siRNA duplexes.
PMCID: PMC2793519  PMID: 20041186
4.  Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive Factors 
PLoS Computational Biology  2013;9(9):e1003235.
Systematic, genome-wide RNA interference (RNAi) analysis is a powerful approach to identify gene functions that support or modulate selected biological processes. An emerging challenge shared with some other genome-wide approaches is that independent RNAi studies often show limited agreement in their lists of implicated genes. To better understand this, we analyzed four genome-wide RNAi studies that identified host genes involved in influenza virus replication. These studies collectively identified and validated the roles of 614 cell genes, but pair-wise overlap among the four gene lists was only 3% to 15% (average 6.7%). However, a number of functional categories were overrepresented in multiple studies. The pair-wise overlap of these enriched-category lists was high, ∼19%, implying more agreement among studies than apparent at the gene level. Probing this further, we found that the gene lists implicated by independent studies were highly connected in interacting networks by independent functional measures such as protein-protein interactions, at rates significantly higher than predicted by chance. We also developed a general, model-based approach to gauge the effects of false-positive and false-negative factors and to estimate, from a limited number of studies, the total number of genes involved in a process. For influenza virus replication, this novel statistical approach estimates the total number of cell genes involved to be ∼2,800. This and multiple other aspects of our experimental and computational results imply that, when following good quality control practices, the low overlap between studies is primarily due to false negatives rather than false-positive gene identifications. These results and methods have implications for and applications to multiple forms of genome-wide analysis.
Author Summary
Genome-wide RNA interference assays of gene functions offer the potential for systematic, global analysis of biological processes. A pressing challenge is to develop meta-analysis methods that effectively combine information from multiple studies. One puzzle is that implicated gene lists from independent studies of the same process often show relatively low overlap. This disagreement might arise from false-positive factors, such as imperfect gene targeting (off-target effects), or from false negatives if separate studies access different components of large, complex systems. We present new methods to examine the relations between individual genome-wide RNAi studies, using studies of host genes in influenza virus replication as a test case. We find that cross-study agreement is greater than suggested by overlap of reported gene lists. This better agreement is evidenced by the strong relation of independent gene lists in functional pathways and protein interaction networks, and by a statistical model that relates multi-study, gene-level findings to factors driving correct, false-negative, and false-positive gene identification. Our analysis of multiple genome-wide studies predicts that there are many undetected host genes important for influenza virus infection, and that false negatives are the major concerns for genome-wide studies.
PMCID: PMC3777922  PMID: 24068911
5.  Off-target effects of siRNA specific for GFP 
Gene knock down by RNAi is a highly effective approach to silence gene expression in experimental as well as therapeutic settings. However, this widely used methodology entails serious pitfalls, especially concerning specificity of the RNAi molecules.
We tested the most widely used control siRNA directed against GFP for off-target effects and found that it deregulates in addition to GFP a set of endogenous target genes. The off-target effects were dependent on the amount of GFP siRNA transfected and were detected in a variety of cell lines. Since the respective siRNA molecule specific for GFP is widely used as negative control for RNAi experiments, we studied the complete set of off-target genes of this molecule by genome-wide expression profiling. The detected modulated mRNAs had target sequences homologous to the siRNA as small as 8 basepairs in size. However, we found no restriction of sequence homology to 3'UTR of target genes.
We can show that even siRNAs without a physiological target have sequence-specific off-target effects in mammalian cells. Furthermore, our analysis defines the off-target genes affected by the siRNA that is commonly used as negative control and directed against GFP. Since off-target effects can hardly be avoided, the best strategy is to identify false positives and exclude them from the results. To this end, we provide the set of false positive genes deregulated by the commonly used GFP siRNA as a reference resource for future siRNA experiments.
PMCID: PMC2443166  PMID: 18577207
6.  Systematic analysis of off-target effects in an RNAi screen reveals microRNAs affecting sensitivity to TRAIL-induced apoptosis 
BMC Genomics  2010;11:175.
RNA inhibition by siRNAs is a frequently used approach to identify genes required for specific biological processes. However RNAi screening using siRNAs is hampered by non-specific or off target effects of the siRNAs, making it difficult to separate genuine hits from false positives. It is thought that many of the off-target effects seen in RNAi experiments are due to siRNAs acting as microRNAs (miRNAs), causing a reduction in gene expression of unintended targets via matches to the 6 or 7 nt 'seed' sequence. We have conducted a careful examination of off-target effects during an siRNA screen for novel regulators of the TRAIL apoptosis induction pathway(s).
We identified 3 hexamers and 3 heptamer seed sequences that appeared multiple times in the top twenty siRNAs in the TRAIL apoptosis screen. Using a novel statistical enrichment approach, we systematically identified a further 17 hexamer and 13 heptamer seed sequences enriched in high scoring siRNAs. The presence of one of these seeds sequences (which could explain 6 of 8 confirmed off-target effects) is sufficient to elicit a phenotype. Three of these seed sequences appear in the human miRNAs miR-26a, miR-145 and miR-384. Transfection of mimics of these miRNAs protects several cell types from TRAIL-induced cell death.
We have demonstrated a role for miR-26a, miR-145 and miR-26a in TRAIL-induced apoptosis. Further these results show that RNAi screening enriches for siRNAs with relevant off-target effects. Some of these effects can be identified by the over-representation of certain seed sequences in high-scoring siRNAs and we demonstrate the usefulness of such systematic analysis of enriched seed sequences.
PMCID: PMC2996961  PMID: 20230625
7.  Power and type I error rate of false discovery rate approaches in genome-wide association studies 
BMC Genetics  2005;6(Suppl 1):S134.
In genome-wide genetic studies with a large number of markers, balancing the type I error rate and power is a challenging issue. Recently proposed false discovery rate (FDR) approaches are promising solutions to this problem. Using the 100 simulated datasets of a genome-wide marker map spaced about 3 cM and phenotypes from the Genetic Analysis Workshop 14, we studied the type I error rate and power of Storey's FDR approach, and compared it to the traditional Bonferroni procedure. We confirmed that Storey's FDR approach had a strong control of FDR. We found that Storey's FDR approach only provided weak control of family-wise error rate (FWER). For these simulated datasets, Storey's FDR approach only had slightly higher power than the Bonferroni procedure. In conclusion, Storey's FDR approach is more powerful than the Bonferroni procedure if strong control of FDR or weak control of FWER is desired. Storey's FDR approach has little power advantage over the Bonferroni procedure if there is low linkage disequilibrium among the markers. Further evaluation of the type I error rate and power of the FDR approaches for higher linkage disequilibrium and for haplotype analyses is warranted.
PMCID: PMC1866802  PMID: 16451593
8.  Genome-wide screens for effective siRNAs through assessing the size of siRNA effects 
BMC Research Notes  2008;1:33.
RNA interference (RNAi) has been seen as a revolution in functional genomics and system biology. Genome-wide RNAi research relies on the development of RNAi high-throughput screening (HTS) assays. One of the most fundamental challenges in RNAi HTS is to glean biological significance from mounds of data, which relies on the development of effective analytic methods for selecting effective small interfering RNAs (siRNAs).
Based on a recently proposed parameter, strictly standardized mean difference (SSMD), I propose an analytic method for genome-wide screens of effective siRNAs through assessing and testing the size of siRNA effects. Central to this method is the capability of SSMD in quantifying siRNA effects. This method has relied on normal approximation, which works only in the primary screens but not in the confirmatory screens. In this paper, I explore the non-central t-distribution property of SSMD estimates and use this property to extend the SSMD-based method so that it works effectively in either primary or confirmatory screens as well as in any HTS screens with or without replicates. The SSMD-based method maintains a balanced control of false positives and false negatives.
The central interest in genome-wide RNAi research is the selection of effective siRNAs which relies on the development of analytic methods to measure the size of siRNA effects. The new analytic method for hit selection provided in this paper offers a good analytic tool for selecting effective siRNAs, better than current analytic methods, and thus may have broad utility in genome-wide RNAi research.
PMCID: PMC2526086  PMID: 18710486
9.  Activation Likelihood Estimation meta-analysis revisited 
Neuroimage  2011;59(3):2349-2361.
A widely used technique for coordinate-based meta-analysis of neuroimaging data is activation likelihood estimation (ALE), which determines the convergence of foci reported from different experiments. ALE analysis involves modelling these foci as probability distributions whose width is based on empirical estimates of the spatial uncertainty due to the between-subject and between-template variability of neuroimaging data. ALE results are assessed against a null-distribution of random spatial association between experiments, resulting in random-effects inference. In the present revision of this algorithm, we address two remaining drawbacks of the previous algorithm. First, the assessment of spatial association between experiments was based on a highly time-consuming permutation test, which nevertheless entailed the danger of underestimating the right tail of the null-distribution. In this report, we outline how this previous approach may be replaced by a faster and more precise analytical method. Second, the previously applied correction procedure, i.e. controlling the false discovery rate (FDR), is supplemented by new approaches for correcting the family-wise error rate and the cluster-level significance. The different alternatives for drawing inference on meta-analytic results are evaluated on an exemplary dataset on face perception as well as discussed with respect to their methodological limitations and advantages. In summary, we thus replaced the previous permutation algorithm with a faster and more rigorous analytical solution for the null-distribution and comprehensively address the issue of multiple-comparison corrections. The proposed revision of the ALE-algorithm should provide an improved tool for conducting coordinate-based meta-analyses on functional imaging data.
PMCID: PMC3254820  PMID: 21963913
fMRI; PET; permutation; inference; cluster-thresholding
10.  A network-based integrative approach to prioritize reliable hits from multiple genome-wide RNAi screens in Drosophila 
BMC Genomics  2009;10:220.
The recently developed RNA interference (RNAi) technology has created an unprecedented opportunity which allows the function of individual genes in whole organisms or cell lines to be interrogated at genome-wide scale. However, multiple issues, such as off-target effects or low efficacies in knocking down certain genes, have produced RNAi screening results that are often noisy and that potentially yield both high rates of false positives and false negatives. Therefore, integrating RNAi screening results with other information, such as protein-protein interaction (PPI), may help to address these issues.
By analyzing 24 genome-wide RNAi screens interrogating various biological processes in Drosophila, we found that RNAi positive hits were significantly more connected to each other when analyzed within a protein-protein interaction network, as opposed to random cases, for nearly all screens. Based on this finding, we developed a network-based approach to identify false positives (FPs) and false negatives (FNs) in these screening results. This approach relied on a scoring function, which we termed NePhe, to integrate information obtained from both PPI network and RNAi screening results. Using a novel rank-based test, we compared the performance of different NePhe scoring functions and found that diffusion kernel-based methods generally outperformed others, such as direct neighbor-based methods. Using two genome-wide RNAi screens as examples, we validated our approach extensively from multiple aspects. We prioritized hits in the original screens that were more likely to be reproduced by the validation screen and recovered potential FNs whose involvements in the biological process were suggested by previous knowledge and mutant phenotypes. Finally, we demonstrated that the NePhe scoring system helped to biologically interpret RNAi results at the module level.
By comprehensively analyzing multiple genome-wide RNAi screens, we conclude that network information can be effectively integrated with RNAi results to produce suggestive FPs and FNs, and to bring biological insight to the screening results.
PMCID: PMC2697172  PMID: 19435510
11.  Factors affecting reproducibility between genome-scale siRNA-based screens 
Journal of biomolecular screening  2010;15(7):735-747.
RNA interference-based screening is a powerful new genomic technology which addresses gene function en masse. To evaluate factors influencing hit list composition and reproducibility, we performed two identically designed small interfering RNA (siRNA)-based, whole genome screens for host factors supporting yellow fever virus infection. These screens represent two separate experiments completed five months apart and allow the direct assessment of the reproducibility of a given siRNA technology when performed in the same environment. Candidate hit lists generated by sum rank, median absolute deviation, z-score, and strictly standardized mean difference were compared within and between whole genome screens. Application of these analysis methodologies within a single screening dataset using a fixed threshold equivalent to a p-value ≤ 0.001 resulted in hit lists ranging from 82 to 1,140 members and highlighted the tremendous impact analysis methodology has on hit list composition. Intra- and inter-screen reproducibility was significantly influenced by the analysis methodology and ranged from 32% to 99%. This study also highlighted the power of testing at least two independent siRNAs for each gene product in primary screens. To facilitate validation we conclude by suggesting methods to reduce false discovery at the primary screening stage.
In this study we present the first comprehensive comparison of multiple analysis strategies, and demonstrate the impact of the analysis methodology on the composition of the “hit list”. Therefore, we propose that the entire dataset derived from functional genome-scale screens, especially if publicly funded, should be made available as is done with data derived from gene expression and genome-wide association studies.
PMCID: PMC3149892  PMID: 20625183
RNA interference; analysis; RNAi screen analysis; siRNA; RNAi; siRNA screening; sum rank; median absolute deviation; strictly standardized mean difference; genome-wide; whole-genome; comparison; overlap; hit list
12.  An efficient RNA interference screening strategy for gene functional analysis 
BMC Genomics  2012;13:491.
RNA interference (RNAi) is commonly applied in genome-scale gene functional screens. However, a one-on-one RNAi analysis that targets each gene is cost-ineffective and laborious. Previous studies have indicated that siRNAs can also affect RNAs that are near-perfectly complementary, and this phenomenon has been termed an off-target effect. This phenomenon implies that it is possible to silence several genes simultaneously with a carefully designed siRNA.
We propose a strategy that is combined with a heuristic algorithm to design suitable siRNAs that can target multiple genes and a group testing method that would reduce the number of required RNAi experiments in a large-scale RNAi analysis. To verify the efficacy of our strategy, we used the Orchid expressed sequence tag data as a case study to screen the putative transcription factors that are involved in plant disease responses. According to our computation, 94 qualified siRNAs were sufficient to examine all of the predicated 229 transcription factors. In addition, among the 94 computer-designed siRNAs, an siRNA that targets both TF15 (a previously identified transcription factor that is involved in the plant disease-response pathway) and TF21 was introduced into orchids. The experimental results showed that this siRNA can simultaneously silence TF15 and TF21, and application of our strategy successfully confirmed that TF15 is involved in plant defense responses. Interestingly, our second-round analysis, which used an siRNA specific to TF21, indicated that TF21 is a previously unidentified transcription factor that is related to plant defense responses.
Our computational results showed that it is possible to screen all genes with fewer experiments than would be required for the traditional one-on-one RNAi screening. We also verified that our strategy is capable of identifying genes that are involved in a specific phenotype.
PMCID: PMC3533828  PMID: 22988976
RNA interference; RNAi screening; SiRNA design; Gene functional analysis; Group testing
13.  RNA interference targeting virion core protein ORF095 inhibits Goatpox virus replication in Vero cells 
Virology Journal  2012;9:48.
Goatpox is an economically important disease in goat and sheep-producing areas of the world. Many vaccine strategies developed to control the disease are not yet completely successful. Hairpin expression vectors have been used to induce gene silencing in a large number of studies on viruses. However, none of these studies has been attempted to study GTPV. In the interest of exploiting improved methods to control goat pox, it is participated that RNAi may provide effective protection against GTPV. In this study we show the suppression of Goatpox virus (GTPV) replication via knockdown of virion core protein using RNA interference.
Four short interfering RNA (siRNA) sequences (siRNA-61, siRNA-70, siRNA-165 and siRNA-296) against a region of GTPV ORF095 were selected. Sense and antisense siRNA-encoding sequences separated by a hairpin loop sequence were designed as short hairpin RNA (shRNA) expression cassettes under the control of a human U6 promoter. ORF095 amplicon was generated using PCR, and then cloned into pEGFP-N1 vector, named as p095/EGFP. p095/EGFP and each of the siRNA expression cassettes (p61, p70, p165 and p296) were co-transfected into BHK-21 cells. Fluorescence detection, flow cytometric analysis, retro transcription PCR (RT-PCR) and real time PCR were used to check the efficiency of RNAi. The results showed that the ORF095-specific siRNA-70 effectively down-regulated the expression of ORF095. When Vero cells were transfected with shRNA expression vectors (p61/GFP, p70/GFP, p165/GFP and p296/GFP) and then infected with GTPV, GTPV-ORF095-70 was found to be the most effective inhibition site in decreasing cytopathic effect (CPE) induced by GTPV. The results presented here indicated that DNA-based siRNA could effectively inhibit the replication of GTPV (approximately 463. 5-fold reduction of viral titers) on Vero cells.
This study demonstrates that vector-based shRNA methodology can effectively inhibit GTPV replication on Vero cells. Simultaneously, this work represents a strategy for controlling goatpox, potentially facilitating new experimental approaches in the analysis of both viral and cellular gene functions during of GTPV infection.
PMCID: PMC3298800  PMID: 22340205
RNAi; shRNA; ORF095; Goatpox virus
14.  Designing Highly Active siRNAs for Therapeutic Applications 
The FEBS journal  2010;277(23):4806-4813.
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 will 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.
PMCID: PMC3052974  PMID: 21078115
15.  Detection of Simultaneous Group Effects in MicroRNA Expression and Related Target Gene Sets 
PLoS ONE  2012;7(6):e38365.
Expression levels of mRNAs are among other factors regulated by microRNAs. A particular microRNA can bind specifically to several target mRNAs and lead to their degradation. Expression levels of both, mRNAs and microRNAs, can be obtained by microarray experiments. In order to increase the power of detecting microRNAs that are differentially expressed between two different groups of samples, we incorporate expression levels of their related target gene sets. Group effects are determined individually for each microRNA, and by enrichment tests and global tests for target gene sets. The resulting lists of p-values from individual and set-wise testing are combined by means of meta analysis. We propose a new approach to connect microRNA-wise and gene set-wise information by means of p-value combination as often used in meta-analysis. In this context, we evaluate the usefulness of different approaches of gene set tests. In a simulation study we reveal that our combination approach is more powerful than microRNA-wise testing alone. Furthermore, we show that combining microRNA-wise results with ‘competitive’ gene set tests maintains a pre-specified false discovery rate. In contrast, a combination with ‘self-contained’ gene set tests can harm the false discovery rate, particularly when gene sets are not disjunct.
PMCID: PMC3378551  PMID: 22723856
16.  Downregulation of BCL11A by siRNA induces apoptosis in B lymphoma cell lines 
Biomedical Reports  2012;1(1):47-52.
The B-cell chronic lymphocytic leukemia (CLL)/lymphoma 11A gene (BCL11A) encodes a krüppel-like zinc finger protein, which is important in thymopoiesis and has been associated with hematopoietic malignancies. In this study, we investigated whether the downregulation of BCL11A mRNA by small interference RNA (siRNA) was capable of inducing apoptosis, and tested the effect of BCL11A siRNA combined with BCL2 siRNA in B lymphoma cell lines (SUDHL6, EB1). BCL11A siRNA was transfected into SUDHL6, EB1 cells with HiPerfect transfection reagents. After transient transfection with BCL11A siRNA, the expression levels of BCL11A mRNA and protein were assayed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot analysis. The cell proliferation was determined by a cell counting kit-8 (CCK8) assay. Apoptosis was determined by morphological observation and flow cytometric analysis. The results showed that the expression levels of BCL11A mRNA and protein from SUDHL6, EB1 cells transfected with BCL11A siRNA decreased, compared with either the scrambled negative control siRNA group or untransfected cells group (P<0.05). Viability of cells transfected with BCL11A siRNA was less compared to cells transfected with control siRNA and untransfected SUDHL6, EB1 cells, respectively (P<0.05). BCL11A siRNA induced apoptosis in both SUDHL6 and EB1 cells. BCL11A siRNA combined with BCL2 siRNA significantly inhibited cell growth. Apoptotic rates of SUDHL6, EB1 cells treated with BCL11A siRNA combined with BCL2 siRNA significantly increased (P<0.05), compared with either the scrambled control (Sc) siRNA and BCL2 siRNA combination or BCL2 or BCL11A siRNA-treated cells alone. Findings of this study suggest the downregulation of BCL11A mRNA by siRNA was able to induce apoptosis. Moreover, BCL11A siRNA combined with BCL2 siRNA increased apoptosis in SUDHL6, EB1 cells. Thus, suppression of BCL11A expression may be a useful approach in the treatment of B lymphoma.
PMCID: PMC3956826  PMID: 24648892
B-cell chronic lymphocytic leukemia/lymphoma 11A; siRNA small interfering RNA; SUDHL6 cells; EB1 cells; BCL2; apoptosis
17.  Differential susceptibility of Human Primary Aortic and Coronary Artery Vascular Cells to RNA interference 
RNAi technology is a promising tool for gene therapy of vascular disease. However, the biological heterogeneity between endothelial (EC) and vascular smooth muscle cells (SMC) and within different vascular beds make them differentially susceptible to siRNA. This is further complicated by the task of choosing the right transfection reagent that leads to consistent gene silencing across all the cell types with minimal toxicity. The goal of this study was to investigate the intrinsic RNAi susceptibility of primary human aortic and coronary artery endothelial and vascular smooth muscle cells (AoEC, CoEC, AoSMC, CoSMC) using adherent cell cytometry.
Cells were seeded at a density of 5000 cells/well of a 96well plate. 24 hours later cells were transfected with either non-targeting unlabeled control siRNA (50nM), or non-targeting red fluorescence labeled siRNA (siGLO Red, 5 or 50nM) using no transfection reagent, HiPerfect or Lipofectamine RNAiMax. Hoechst nuclei stain was used to label cells for counting. For data analysis an adherent cell cytometer, Celigo® was used.
Red fluorescence counts were normalized to the cell count.
EC displayed a higher susceptibility towards siRNA delivery than SMC from the corresponding artery. CoSMC were more susceptible than AoSMC. In all cell types RNAiMax was more potent compared to HiPerfect or no transfection reagent. However, after 24 hours, RNAiMax led to a significant cell loss in both AoEC and CoEC. None of the other transfection conditions led to a significant cell loss.
This study confirms our prior observation that EC are more susceptible to siRNA than SMC based on intracellular siRNA delivery. RNAiMax treatment led to significant cell loss in AoEC and CoEC, but not in the SMC populations. Additionally, this study is the first to demonstrate that coronary SMC are more susceptible to siRNA than aortic SMC.
PMCID: PMC3430013  PMID: 22842581
RNAi; vascular; gene therapy; endothelial cell; smooth muscle cell; coronary; aortic
18.  Were Genome-Wide Linkage Studies a Waste of Time? Exploiting Candidate Regions Within Genome-Wide Association Studies 
Genetic epidemiology  2010;34(2):107-118.
A central issue in genome-wide association (GWA) studies is assessing statistical significance while adjusting for multiple hypothesis testing. An equally important question is the statistical efficiency of the GWA design as compared to the traditional sequential approach in which genome-wide linkage analysis is followed by region-wise association mapping. Nevertheless, GWA is becoming more popular due in part to cost efficiency: commercially available 1M chips are nearly as inexpensive as a custom-designed 10K chip. It is becoming apparent, however, that most of the on-going GWA studies with 2,000~5,000 samples are in fact underpowered. As a means to improve power, we emphasize the importance of utilizing prior information such as results of previous linkage studies via a stratified false discovery rate (FDR) control. The essence of the stratified FDR control is to prioritize the genome and maintain power to interrogate candidate regions within the GWA study. These candidate regions can be defined as, but are by no means limited to, linkage-peak regions. Furthermore, we theoretically unify the stratified FDR approach and the weighted p-value method, and we show that stratified FDR can be formulated as a robust version of weighted FDR. Finally, we demonstrate the utility of the methods in two GWA datasets: Type 2 Diabetes (FUSION) and an on-going study of long-term diabetic complications (DCCT/EDIC). The methods are implemented as a user-friendly software package, SFDR. The same stratification framework can be readily applied to other type of studies, for example, using GWA results to improve the power of sequencing data analyses.
PMCID: PMC2811772  PMID: 19626703
genome-wide association; genome-wide linkage; statistical power; prior information; false discovery rate
19.  In Silico Target-Specific siRNA Design Based on Domain Transfer in Heterogeneous Data 
PLoS ONE  2012;7(12):e50697.
RNA interference via exogenous small interference RNAs (siRNA) is a powerful tool in gene function study and disease treatment. Designing efficient and specific siRNA on target gene remains the key issue in RNAi. Although various in silico models have been proposed for rational siRNA design, most of them focus on the efficiencies of selected siRNAs, while limited effort has been made to improve their specificities targeted on specific mRNAs, which is related to reducing off-target effects (OTEs) in RNAi. In our study, we propose for the first time that the enhancement of target specificity of siRNA design can be achieved computationally by domain transfer in heterogeneous data sources from different siRNA targets. A transfer learning based method i.e., heterogeneous regression (HEGS) is presented for target-specific siRNA efficacy modeling and feature selection. Based on the model, (1) the target regression model can be built by extracting information from related data in other targets/experiments, thus increasing the target specificity in siRNA design with the help of information from siRNAs binding to other homologous genes, and (2) the potential features correlated to the current siRNA design can be identified even when there is lack of experimental validated siRNA affinity data on this target. In summary, our findings present useful instructions for a better target-specific siRNA design, with potential applications in genome-wide high-throughput screening of effective siRNA, and will provide further insights on the mechanism of RNAi.
PMCID: PMC3528743  PMID: 23284642
20.  Efficient siRNA Delivery into Primary Cells by Peptide Transduction-dsRNA Binding Domain (PTD-DRBD) Fusion Protein 
Nature biotechnology  2009;27(6):567-571.
Short interfering RNA (siRNA) induced RNA interference (RNAi) responses allow for discovery research and performing large scale screening1-5; however, due to their size and anionic charge, siRNAs have no bioavailability to enter cells4,5. Current approaches fail to deliver siRNAs into a high percentage of primary cells in a non-cytotoxic fashion. Here we report an efficient siRNA delivery approach that utilizes a Peptide Transduction Domain-dsRNA Binding Domain (PTD-DRBD) fusion protein. DRBDs bind to siRNAs with high avidity, masking the siRNA negative charge and allow for PTD-mediated cellular uptake. PTD-DRBD delivered siRNAs induced rapid RNAi responses in a non-cytotoxic manner in the entire cell population of primary and transformed cells, including T cells, HUVECs and hESCs. Whole genome microarray analysis showed minimal transcriptional changes by PTD-DRBD and we did not detect any innate immune responses in PBMCs. Thus, PTD-DRBD mediated siRNA delivery allows efficient RNAi manipulation of difficult primary cell types.
PMCID: PMC2694965  PMID: 19448630
21.  Clustering phenotype populations by genome-wide RNAi and multiparametric imaging 
How to predict gene function from phenotypic cues is a longstanding question in biology.Using quantitative multiparametric imaging, RNAi-mediated cell phenotypes were measured on a genome-wide scale.On the basis of phenotypic ‘neighbourhoods', we identified previously uncharacterized human genes as mediators of the DNA damage response pathway and the maintenance of genomic integrity.The phenotypic map is provided as an online resource at for discovering further functional relationships for a broad spectrum of biological module
Genetic screens for phenotypic similarity have made key contributions for associating genes with biological processes. Aggregating genes by similarity of their loss-of-function phenotype has provided insights into signalling pathways that have a conserved function from Drosophila to human (Nusslein-Volhard and Wieschaus, 1980; Bier, 2005). Complex visual phenotypes, such as defects in pattern formation during development, greatly facilitated the classification of genes into pathways, and phenotypic similarities in many cases predicted molecular relationships. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cultured cells has become feasible in many organisms whose genome have been sequenced (Boutros and Ahringer, 2008). One of the current challenges is the computational categorization of visual phenotypes and the prediction of gene function and associated biological processes. With large parts of the genome still being in unchartered territory, deriving functional information from large-scale phenotype analysis promises to uncover novel gene–gene relationships and to generate functional maps to explore cellular processes.
In this study, we developed an automated approach using RNAi-mediated cell phenotypes, multiparametric imaging and computational modelling to obtain functional information on previously uncharacterized genes. To generate broad, computer-readable phenotypic signatures, we measured the effect of RNAi-mediated knockdowns on changes of cell morphology in human cells on a genome-wide scale. First, the several million cells were stained for nuclear and cytoskeletal markers and then imaged using automated microscopy. On the basis of fluorescent markers, we established an automated image analysis to classify individual cells (Figure 1A). After cell segmentation for determining nuclei and cell boundaries (Figure 1C), we computed 51 cell descriptors that quantified intensities, shape characteristics and texture (Figure 1F). Individual cells were categorized into 1 of 10 classes, which included cells showing protrusion/elongation, cells in metaphase, large cells, condensed cells, cells with lamellipodia and cellular debris (Figure 1D and E). Each siRNA knockdown was summarized by a phenotypic profile and differences between RNAi knockdowns were quantified by the similarity between phenotypic profiles. We termed the vector of scores a phenoprint (Figure 3C) and defined the phenotypic distance between a pair of perturbations as the distance between their corresponding phenoprints.
To visualize the distribution of all phenoprints, we plotted them in a genome-wide map as a two-dimensional representation of the phenotypic similarity relationships (Figure 3A). The complete data set and an interactive version of the phenotypic map are available at The map identified phenotypic ‘neighbourhoods', which are characterized by cells with lamellipodia (WNK3, ANXA4), cells with prominent actin fibres (ODF2, SOD3), abundance of large cells (CA14), many elongated cells (SH2B2, ELMO2), decrease in cell number (TPX2, COPB1, COPA), increase in number of cells in metaphase (BLR1, CIB2) and combinations of phenotypes such as presence of large cells with protrusions and bright nuclei (PTPRZ1, RRM1; Figure 3B).
To test whether phenotypic similarity might serve as a predictor of gene function, we focused our further analysis on two clusters that contained genes associated with the DNA damage response (DDR) and genomic integrity (Figure 3A and C). The first phenotypic cluster included proteins with kinetochore-associated functions such as NUF2 (Figure 3B) and SGOL1. It also contained the centrosomal protein CEP164 that has been described as an important mediator of the DNA damage-activated signalling cascade (Sivasubramaniam et al, 2008) and the largely uncharacterized genes DONSON and SON. A second phenotypically distinct cluster included previously described components of the DDR pathway such as RRM1 (Figure 3A–C), CLSPN, PRIM2 and SETD8. Furthermore, this cluster contained the poorly characterized genes CADM1 and CD3EAP.
Cells activate a signalling cascade in response to DNA damage induced by exogenous and endogenous factors. Central are the kinases ATM and ATR as they serve as sensors of DNA damage and activators of further downstream kinases (Harper and Elledge, 2007; Cimprich and Cortez, 2008). To investigate whether DONSON, SON, CADM1 and CD3EAP, which were found in phenotypic ‘neighbourhoods' to known DDR components, have a role in the DNA damage signalling pathway, we tested the effect of their depletion on the DDR on γ irradiation. As indicated by reduced CHEK1 phosphorylation, siRNA knock down of DONSON, SON, CD3EAP or CADM1 resulted in impaired DDR signalling on γ irradiation. Furthermore, knock down of DONSON or SON reduced phosphorylation of downstream effectors such as NBS1, CHEK1 and the histone variant H2AX on UVC irradiation. DONSON depletion also impaired recruitment of RPA2 onto chromatin and SON knockdown reduced RPA2 phosphorylation indicating that DONSON and SON presumably act downstream of the activation of ATM. In agreement to their phenotypic profile, these results suggest that DONSON, SON, CADM1 and CD3EAP are important mediators of the DDR. Further experiments demonstrated that they are also required for the maintenance of genomic integrity.
In summary, we show that genes with similar phenotypic profiles tend to share similar functions. The power of our computational and experimental approach is demonstrated by the identification of novel signalling regulators whose phenotypic profiles were found in proximity to known biological modules. Therefore, we believe that such phenotypic maps can serve as a resource for functional discovery and characterization of unknown genes. Furthermore, such approaches are also applicable for other perturbation reagents, such as small molecules in drug discovery and development. One could also envision combined maps that contain both siRNAs and small molecules to predict target–small molecule relationships and potential side effects.
Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.
PMCID: PMC2913390  PMID: 20531400
DNA damage response signalling; massively parallel phenotyping; phenotype networks; RNAi screening
22.  Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data 
BMC Bioinformatics  2005;6:187.
The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustment methods for multiple comparisons such as family-wise error rate (FWER) have been found to be too conservative in analyzing large-screening microarray data, and the False Discovery Rate (FDR), the expected proportion of false positives among all positives, has been recently suggested as an alternative for controlling false positives. Several statistical approaches have been used to estimate and control FDR, but these may not provide reliable FDR estimation when applied to microarray data sets with a small number of replicates.
We propose a rank-invariant resampling (RIR) based approach to FDR evaluation. Our proposed method generates a biologically relevant null distribution, which maintains similar variability to observed microarray data. We compare the performance of our RIR-based FDR estimation with that of four other popular methods. Our approach outperforms the other methods both in simulated and real microarray data.
We found that the SAM's random shuffling and SPLOSH approaches were liberal and the other two theoretical methods were too conservative while our RIR approach provided more accurate FDR estimation than the other approaches.
PMCID: PMC1187876  PMID: 16042779
23.  Modeling RNA interference in mammalian cells 
BMC Systems Biology  2011;5:19.
RNA interference (RNAi) is a regulatory cellular process that controls post-transcriptional gene silencing. During RNAi double-stranded RNA (dsRNA) induces sequence-specific degradation of homologous mRNA via the generation of smaller dsRNA oligomers of length between 21-23nt (siRNAs). siRNAs are then loaded onto the RNA-Induced Silencing multiprotein Complex (RISC), which uses the siRNA antisense strand to specifically recognize mRNA species which exhibit a complementary sequence. Once the siRNA loaded-RISC binds the target mRNA, the mRNA is cleaved and degraded, and the siRNA loaded-RISC can degrade additional mRNA molecules. Despite the widespread use of siRNAs for gene silencing, and the importance of dosage for its efficiency and to avoid off target effects, none of the numerous mathematical models proposed in literature was validated to quantitatively capture the effects of RNAi on the target mRNA degradation for different concentrations of siRNAs. Here, we address this pressing open problem performing in vitro experiments of RNAi in mammalian cells and testing and comparing different mathematical models fitting experimental data to in-silico generated data. We performed in vitro experiments in human and hamster cell lines constitutively expressing respectively EGFP protein or tTA protein, measuring both mRNA levels, by quantitative Real-Time PCR, and protein levels, by FACS analysis, for a large range of concentrations of siRNA oligomers.
We tested and validated four different mathematical models of RNA interference by quantitatively fitting models' parameters to best capture the in vitro experimental data. We show that a simple Hill kinetic model is the most efficient way to model RNA interference. Our experimental and modeling findings clearly show that the RNAi-mediated degradation of mRNA is subject to saturation effects.
Our model has a simple mathematical form, amenable to analytical investigations and a small set of parameters with an intuitive physical meaning, that makes it a unique and reliable mathematical tool. The findings here presented will be a useful instrument for better understanding RNAi biology and as modelling tool in Systems and Synthetic Biology.
PMCID: PMC3040133  PMID: 21272352
24.  RNA Interference in Schistosoma mansoni Schistosomula: Selectivity, Sensitivity and Operation for Larger-Scale Screening 
The possible emergence of resistance to the only available drug for schistosomiasis spurs drug discovery that has been recently incentivized by the availability of improved transcriptome and genome sequence information. Transient RNAi has emerged as a straightforward and important technique to interrogate that information through decreased or loss of gene function and identify potential drug targets. To date, RNAi studies in schistosome stages infecting humans have focused on single (or up to 3) genes of interest. Therefore, in the context of standardizing larger RNAi screens, data are limited on the extent of possible off-targeting effects, gene-to-gene variability in RNAi efficiency and the operational capabilities and limits of RNAi.
Methodology/Principal Findings
We investigated in vitro the sensitivity and selectivity of RNAi using double-stranded (ds)RNA (approximately 500 bp) designed to target 11 Schistosoma mansoni genes that are expressed in different tissues; the gut, tegument and otherwise. Among the genes investigated were 5 that had been previously predicted to be essential for parasite survival. We employed mechanically transformed schistosomula that are relevant to parasitism in humans, amenable to screen automation and easier to obtain in greater numbers than adult parasites. The operational parameters investigated included defined culture media for optimal parasite maintenance, transfection strategy, time- and dose- dependency of RNAi, and dosing limits. Of 7 defined culture media tested, Basch Medium 169 was optimal for parasite maintenance. RNAi was best achieved by co-incubating parasites and dsRNA (standardized to 30 µg/ml for 6 days); electroporation provided no added benefit. RNAi, including interference of more than one transcript, was selective to the gene target(s) within the pools of transcripts representative of each tissue. Concentrations of dsRNA above 90 µg/ml were directly toxic. RNAi efficiency was transcript-dependent (from 40 to >75% knockdown relative to controls) and this may have contributed to the lack of obvious phenotypes observed, even after prolonged incubations of 3 weeks. Within minutes of their mechanical preparation from cercariae, schistosomula accumulated fluorescent macromolecules in the gut indicating that the gut is an important route through which RNAi is expedited in the developing parasite.
Transient RNAi operates gene-selectively in S. mansoni newly transformed schistosomula yet the sensitivity of individual gene targets varies. These findings and the operational parameters defined will facilitate larger RNAi screens.
Author Summary
RNA interference (RNAi) is a technique to selectively suppress mRNA of individual genes and, consequently, their cognate proteins. RNAi using double-stranded (ds) RNA has been used to interrogate the function of mainly single genes in the flatworm, Schistosoma mansoni, one of a number of schistosome species causing schistosomiasis. In consideration of large-scale screens to identify candidate drug targets, we examined the selectivity and sensitivity (the degree of suppression) of RNAi for 11 genes produced in different tissues of the parasite: the gut, tegument (surface) and otherwise. We used the schistosomulum stage prepared from infective cercariae larvae which are accessible in large numbers and adaptable to automated screening platforms. We found that RNAi suppresses transcripts selectively, however, the sensitivity of suppression varies (40%–>75%). No obvious changes in the parasite occurred post-RNAi, including after targeting the mRNA of genes that had been computationally predicted to be essential for survival. Additionally, we defined operational parameters to facilitate large-scale RNAi, including choice of culture medium, transfection strategy to deliver dsRNA, dose- and time-dependency, and dosing limits. Finally, using fluorescent probes, we show that the developing gut allows rapid entrance of dsRNA into the parasite to initiate RNAi.
PMCID: PMC2957409  PMID: 20976050
25.  Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis 
BMC Bioinformatics  2014;15:192.
RNA interference (RNAi) is an effective and important tool used to study gene function. For large-scale screens, RNAi is used to systematically down-regulate genes of interest and analyze their roles in a biological process. However, RNAi is associated with off-target effects (OTEs), including microRNA (miRNA)-like OTEs. The contribution of reagent-specific OTEs to RNAi screen data sets can be significant. In addition, the post-screen validation process is time and labor intensive. Thus, the availability of robust approaches to identify candidate off-targeted transcripts would be beneficial.
Significant efforts have been made to eliminate false positive results attributable to sequence-specific OTEs associated with RNAi. These approaches have included improved algorithms for RNAi reagent design, incorporation of chemical modifications into siRNAs, and the use of various bioinformatics strategies to identify possible OTEs in screen results. Genome-wide Enrichment of Seed Sequence matches (GESS) was developed to identify potential off-targeted transcripts in large-scale screen data by seed-region analysis. Here, we introduce a user-friendly web application that provides researchers a relatively quick and easy way to perform GESS analysis on data from human or mouse cell-based screens using short interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), as well as for Drosophila screens using shRNAs. Online GESS relies on up-to-date transcript sequence annotations for human and mouse genes extracted from NCBI Reference Sequence (RefSeq) and Drosophila genes from FlyBase. The tool also accommodates analysis with user-provided reference sequence files.
Online GESS provides a straightforward user interface for genome-wide seed region analysis for human, mouse and Drosophila RNAi screen data. With the tool, users can either use a built-in database or provide a database of transcripts for analysis. This makes it possible to analyze RNAi data from any organism for which the user can provide transcript sequences.
PMCID: PMC4073188  PMID: 24934636
RNAi; Off-target effects; Data analysis; Seed region; miRNA; siRNA; shRNA; High-throughput screening

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