An accurate and interpretable model for siRNA efficacy prediction

Vert, Jean-Philippe; Foveau, Nicolas; Lajaunie, Christian; Vandenbrouck, Yves

doi:10.1186/1471-2105-7-520

Cited by 278 publications

(222 citation statements)

References 46 publications

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“…When we consider only shRNAs with a 5' U, the correlation rises to 0.78, likely due to the greater number of data point available for training that algorithm. For comparison, DSIR achieves a correlation of 0.4 and a prior shRNA prediction algorithm trained on a subset of the sensor data used in this study achieves 0.56 (Matveeva, Nazipova, Ogurtsov, & Shabalina, 2012;Vert et al, 2006). This indicates that shERWOOD achieves a roughly 180% increase in performance over currently existing siRNA prediction algorithms and a 126% increase in efficacy over existing shRNA specific prediction algorithms.…”

Section: A Sensor-based Computational Algorithm To Predict Shrna Effimentioning

confidence: 81%

“…To compare our results to existing siRNA-based design tools, we obtained the top 50 predictions for all nine transcripts using three different algorithms (Huesken et al, 2005;Sachidanandam, 2004;Vert et al, 2006) and compared them to the 50 highest scoring Sensor-derived shRNAs for each gene. Strikingly, >70% of our scoring shRNAs were not identified in the top 50 predictions of any algorithm ( Figure S5A).…”

Section: Comparison To Existing Design Algorithmsmentioning

confidence: 99%

“…Experience in the field supported the effectiveness of BIOPREDSi; however, access to the algorithm eventually became impossible. The same dataset was subsequently used to produce a second algorithm, DSIR, which included additional input variables (the frequency of each nucleotide, each 2mer and each 3mer within the guide) (Vert, Foveau, Lajaunie, & Vandenbrouck, 2006). To accommodate this large number of parameters, linear modeling was performed using Lasso Regression (a form of linear regression that iteratively decreases the use of non-predictive variables in the linear model) (Tibshirani, 1995).…”

Section: Dataset S2mentioning

confidence: 99%

“…Machine-learning-based applications trained on siRNA data sets have produced algorithms that facilitate prediction of potent siRNAs (Huesken et al, 2005;Vert et al, 2006). However, such analyses have not been applied to shRNAs, which may require more stringent criteria as they rely on transcription and multistep miRNA processing for the production of small RNA duplexes.…”

Section: Introductionmentioning

confidence: 99%

“…These include a preference for thermodynamic asymmetry (Khvorova et al, 2003;, low G/C content , and a strong bias for A/U at the 5 0 end of the guide strand (Tomari and Zamore, 2005). Nonetheless, these features are not sufficient to accurately distinguish between potent and weak RNAi triggers.Machine-learning-based applications trained on siRNA data sets have produced algorithms that facilitate prediction of potent siRNAs (Huesken et al, 2005;Vert et al, 2006). However, such analyses have not been applied to shRNAs, which may require more stringent criteria as they rely on transcription and multistep miRNA processing for the production of small RNA duplexes.…”

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confidence: 99%

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RNAi as a Routine Route Toward Breast Cancer Therapy

Hannon¹

2014

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)May 2014 (From -To) 1Sep2008-28Feb2014 REPORT TYPE Final DATES COVERED TITLE AND SUBTITLERNAi as a Routine Route Toward Breast Cancer Therapy 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-08-1-0572 5c. PROGRAM ELEMENT NUMBER AUTHOR(S)Gregory J. Hannon, PhD. 5d. PROJECT NUMBERemail: hannon@cshl.edu 5e. TASK NUMBER 5f. WORK UNIT NUMBER PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERCold Spring Harbor Laboratory One Bungtown Road Cold Spring Harbor, NY 11724 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)US Army Medical Research and Materiel Command Fort Detrick, MA 21702 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution unlimited SUPPLEMENTARY NOTES ABSTRACTDuring the first year of this innovator award, we made significant progress toward two of our aims. We constructed a third generation RNAi library and made that available to the breast cancer community. This resource has nearly 75,000 independent, sequence verified clones targeting ~18,000 human genes. A similar library for the mouse genome is nearing completion. We also scaled up our shRNA screening platform in preparation for lethality surveys of all suitable and available BC cell lines, including matched pairs of lines that have acquired resistance to herceptin in vitro. Relevant to our second aim, we have profiled microRNA from each of the identifiable epithelial cell types in the mouse mammary gland and are undertaking similar efforts in human. The goal is to develop microRNA sensor strategies that will permit visualization of each cell type in vivo and enable their isolation and manipulation in vitro. Finally, we showed that two microRNAs, let-7 and miR-93, can deplete tumor initiating cells from a number of basal breast cancer cell lines. SUBJECT TERMS INTRODUCTIONThe goal of this innovator award is to continue to develop and apply RNAi-based screening methods to discover new routes toward breast cancer therapy. The project has three sets of goals. First is to integrate genomic and geneti...

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Section: A Sensor-based Computational Algorithm To Predict Shrna Effimentioning

confidence: 81%

Section: Comparison To Existing Design Algorithmsmentioning

confidence: 99%

Section: Dataset S2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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RNAi as a Routine Route Toward Breast Cancer Therapy

Hannon¹

2014

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Development and application of an RNA nanostructure to induce transient RNAi in difficult transgenic plants

Zhao,

Liu,

Liu

et al. 2024

Biotechnology Journal

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The development of RNA interference (RNAi) is crucial for studying plant gene function. Its use, is limited to a few plants with well‐established transgenic techniques. Spray‐induced gene silencing (SIGS) introduces exogenous double‐stranded RNA (dsRNA) into plants by spraying, injection, or irrigation, triggering the RNAi pathway to instantly silence target genes. As is a transient RNAi technology that does not rely on transgenic methods, SIGS has significant potential for studying gene function in plants lacking advanced transgenic technology. In this study, to enhance their stability and delivery efficiency, siRNAs were used as structural motifs to construct RNA nanoparticles (NPs) of four shapes: triangle, square, pentagon, and hexagon. These NPs, when synthesized by Escherichia coli, showed that triangular and square shapes accumulated more efficiently than pentagon and hexagon shapes. Bioassays revealed that RNA squares had the highest RNAi efficiency, followed by RNA triangles, with GFP‐dsRNA showing the lowest efficiency at 4 and 7 days post‐spray. We further explored the use of RNA squares in inducing transient RNAi in plants that are difficult to transform genetically. The results indicated that Panax notoginseng‐derived MYB2 (PnMYB2) and Camellia oleifera‐derived GUT (CoGUT) were significantly suppressed in P. notoginseng and C. oleifera, respectively, following the application of PnMYB2‐ and CoGUT‐specific RNA squares. These findings suggest that RNA squares are highly effective in SIGS and can be utilized for gene function research in plants.

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ENAH‐202 promotes cancer progression in oral squamous cell carcinoma by regulating ZNF502/VIM axis

Zhang,

Chen,

Sun

et al. 2023

Cancer Medicine

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BackgroundWe aimed to demonstrate the regulatory effect of long non‐coding RNA (lncRNA) ENAH‐202 on oral squamous cell carcinoma (OSCC) development as well as its molecular mechanism.MethodsWe detected ENAH‐202 expression in OSCC tissues and cell lines by quantitative real‐time PCR (qPCR). The biological function of ENAH‐202 was assessed in vitro and in vivo using CCK‐8, colony formation assays, transwell assays, xenograft formation, and tail vein injection. The further molecular mechanism by which ENAH‐202 promoted OSCC progression was identified using RNA pull‐down, LS‐MS/MS analysis, RNA immunoprecipitation (RIP), and chromatin immunoprecipitation (ChIP) assays.ResultsENAH‐202 was significantly upregulated in OSCC tissues and cells. ENAH‐202 promoted OSCC cell proliferation, migration, and invasion in vitro and in vivo. The expression of enabled homolog (ENAH) and epithelial‐to‐mesenchymal transition (EMT)‐related proteins was changed with the expression of ENAH‐202. Moreover, ENAH‐202 promoted the transcription of Vimentin (VIM) by binding with ZNF502, which can help ENAH‐202 promote OSCC progression.ConclusionsENAH‐202 facilitated OSCC cell proliferation and metastasis by regulating ZNF502/VIM axis, which played an important role in OSCC progression.

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An accurate and interpretable model for siRNA efficacy prediction

Cited by 278 publications

References 46 publications

RNAi as a Routine Route Toward Breast Cancer Therapy

RNAi as a Routine Route Toward Breast Cancer Therapy

Development and application of an RNA nanostructure to induce transient RNAi in difficult transgenic plants

ENAH‐202 promotes cancer progression in oral squamous cell carcinoma by regulating ZNF502/VIM axis

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