Validation of human microRNA target pathways enables evaluation of target prediction tools
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ABSTRACT:
PROVIDER: PRJNA557590 | ENA |
REPOSITORIES: ENA
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Project description:Validation of human microRNA target pathways enables evaluation of target prediction tools [miRNA]
| PRJNA557593 | ENA
Project description:Validation of human microRNA target pathways enables evaluation of target prediction tools [mRNA]
| PRJNA557595 | ENA
Project description:MicroRNAs are regulators of gene expression. A wide-spread, yet not validated, assumption is that the targetome of miRNAs is non-randomly distributed across the transcriptome but that targets share functional pathways. We developed a computational and experimental strategy termed high-throughput miRNA interaction reporter assay (HiTmIR) to facilitate the validation of target pathways. First, targets and target pathways are predicted and prioritized by computational means to increase the specificity and positive predictive value. Second, the novel webtool MIRTAH facilitates guided designs of reporter assay constructs at scale. Third, automated and standardized reporter assays are performed. We evaluated HiTmIR using miR-34a-5p, for which TNF- and TGFB-signaling, and Parkinson’s Disease (PD)-related categories were identified and repeated the pipeline for miR-7-5p. HiTmIR validated 58.9% of the target genes for miR-34a-5p and 46.7% for miR-7-5p. We confirmed the targeting by measuring the endogenous protein levels of targets in a neuronal cell model. The standardized positive and negative targets are collected in the new miRATBase database, representing a resource for training, or benchmarking new target predictors. Applied to 88 target predictors with different confidence scores, TargetScan 7.2 and miRanda outperformed other tools. Our experiments demonstrate the efficiency of HiTmIR and provide evidence for an orchestrated miRNA-gene targeting.
2020-11-13 | GSE135150 | GEO
Project description:MicroRNAs are regulators of gene expression. A wide-spread, yet not validated, assumption is that the targetome of miRNAs is non-randomly distributed across the transcriptome but that targets share functional pathways. We developed a computational and experimental strategy termed high-throughput miRNA interaction reporter assay (HiTmIR) to facilitate the validation of target pathways. First, targets and target pathways are predicted and prioritized by computational means to increase the specificity and positive predictive value. Second, the novel webtool MIRTAH facilitates guided designs of reporter assay constructs at scale. Third, automated and standardized reporter assays are performed. We evaluated HiTmIR using miR-34a-5p, for which TNF- and TGFB-signaling, and Parkinson’s Disease (PD)-related categories were identified and repeated the pipeline for miR-7-5p. HiTmIR validated 58.9% of the target genes for miR-34a-5p and 46.7% for miR-7-5p. We confirmed the targeting by measuring the endogenous protein levels of targets in a neuronal cell model. The standardized positive and negative targets are collected in the new miRATBase database, representing a resource for training, or benchmarking new target predictors. Applied to 88 target predictors with different confidence scores, TargetScan 7.2 and miRanda outperformed other tools. Our experiments demonstrate the efficiency of HiTmIR and provide evidence for an orchestrated miRNA-gene targeting.
2020-11-13 | GSE135149 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2020-11-13 | GSE135151 | GEO
Project description:MicroRNAs are regulators of gene expression. A wide-spread, yet not validated, assumption is that the targetome of miRNAs is non-randomly distributed across the transcriptome and that targets share functional pathways. We developed a computational and experimental strategy termed high-throughput miRNA interaction reporter assay (HiTmIR) to facilitate the validation of target pathways. First, targets and target pathways are predicted and prioritized by computational means to increase the specificity and positive predictive value. Second, the novel webtool miRTaH facilitates guided designs of reporter assay constructs at scale. Third, automated and standardized reporter assays are performed. We evaluated HiTmIR using miR-34a-5p, for which TNF- and TGFB-signaling, and Parkinson's Disease (PD)-related categories were identified and repeated the pipeline for miR-7-5p. HiTmIR validated 58.9% of the target genes for miR-34a-5p and 46.7% for miR-7-5p. We confirmed the targeting by measuring the endogenous protein levels of targets in a neuronal cell model. The standardized positive and negative targets are collected in the new miRATBase database, representing a resource for training, or benchmarking new target predictors. Applied to 88 target predictors with different confidence scores, TargetScan 7.2 and miRanda outperformed other tools. Our experiments demonstrate the efficiency of HiTmIR and provide evidence for an orchestrated miRNA-gene targeting.
| S-EPMC7797041 | biostudies-literature
Project description:CRISPR tools for zebrafish
| PRJEB39643 | ENA
Project description:Prediction of on-target and off-target activity of CRISPR-Cas13dguide RNAs using deep learning
| PRJNA971257 | ENA
Project description:tRNA fragments (tRFs) are a novel class of small RNAs comparable to the size and function of miRNAs. We and others have shown that tRFs are generally Dicer independent, can be found in abundance in the miRNA effector protein Ago, and can repress expression of specific genes that have complementarity to their 5’ seed-sequences. Given that this greatly expands the repertoire of small RNAs capable of post-transcriptional gene expression, it is important to predict tRF targets with confidence. Some attempts have been made to predict tRF targets, but are limited in the scope of tRF classes used in prediction or limited in feature selection. We hypothesized that established miRNA target prediction features applied to tRFs through a random forest machine learning algorithm will immensely improve tRF target prediction. Using this approach, we show significant improvements in tRF target prediction for all classes of tRFs and validate our predictions in two independent cell lines. Finally, using Gene Ontology analysis, we provide evidence that tRF-3009a targets may be involved in neural development. These improvements to tRF target prediction further our understanding of tRF function broadly across species and tRF types, and provide avenues for testing novel roles for tRFs in biology.
2022-05-16 | GSE189510 | GEO
Project description:Bile acid accumulation and subsequent liver damage is a frequent adverse effect induced by drugs. Considerable efforts have therefore been focused on the introduction and characterization of tools that allow reliable prediction of this type of drug-induced liver injury. Among those are the cholestatic index and transcriptomic profiling, which are typically assessed in in vitro settings. The present study was set up to test the applicability of both tools to non-pharmaceutical compounds with cholestatic potential, including the industrial compound bis(2-ethylhexyl)phthalate, the cosmetic ingredients triclosan and octynoic acid, the herbicides paraquat and quizalofop-para-ethyl, and the food additives sunset yellow and tartrazine, in a human hepatoma cell culture model of cholestatic liver injury. The cholestatic index method showed cholestatic liability of sunset yellow, tartrazine and triclosan. Of those, tartrazine induced transcriptional changes reminiscent of the transcriptional profile of cholestatic drugs. Furthermore, a number of genes were found to be uniquely modulated by tartrazine, in accordance with the cholestatic drugs atazanavir, cyclosporin A and nefazodone, which may have potential as novel transcriptomic biomarkers of chemical-induced cholestatic liver injury. In conclusion, unambiguous identification of the non-pharmaceutical compounds tested in this study as inducers of cholestasis could not be achieved.
2021-03-18 | GSE169072 | GEO