Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE124530 | GEO | 2019/01/11
REPOSITORIES: GEO
Similar Datasets
Project description:Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data
| PRJNA512378 | ENA
Project description:Validation of human microRNA target pathways enables evaluation of target prediction tools [miRNA]
| PRJNA557593 | ENA
Project description:Objective: The purpose of this study was to investigate the miRNAs basis of tumorigenesis in LSCC, and analyze the physiological processes of target genes predicted by the screened miRNAs that may be useful for the effective therapeutic strategies. Methods: A total number of 6 patients who underwent surgery for primary laryngeal squamous cell carcinoma were recruited for miRNA array analysis. LSCC tissues compared with corresponding adjacent non-neoplastic tissues were analysed by the Affymetrix GeneChip miRNA Array 3.0 to screen effective miRNAs, then TargetScan,PITA and microRNAorg were used for target gene prediction in GeneSpring 12.5 software.Moreover, pathway-based methods were adopted to analyse physiological processes of the target genes. The screened miRNAs and the significant target genes were also validated by qRT-PCR in another 36 patients diagnosed for LSCC. Results: Analysed by the miRNAs arrays, there were seven miRNAs (hsa-miR-224, hsa-miR-183, hsa-miR-23a, hsa-miR-675, hsa-miR-27a, hsa-miR-503 and hsa-miR-4800-3p) significantly related to tumorigenesis ,and all the screened miRNAs in laryngeal squamous cell carcinoma tissues were significantly up-regulated(P<0.05). The expressions of these miRNAs were also validated by qRT-PCR. Then analysed by GeneSpring 12.5 software, there were 72 putative target genes corresponding to the seven significant miRNAs. Moreover,analysed by String database,the result indicated that most target genes could be composed of gene networks; Analysed by GO database, we observed that these target genes were involved in processes such as metabolic process, biological regulation,membrane,protein binding,ion binding and so on (P <0.05); Analysed by KEGG pathways database, MAPK signaling pathway, adherens junction and pathways in cancer played especially important role in tumorigenesis of LSCC (P<0.05). As the two important genes in MAPK signaling pathway which plays pivotal roles in tumorigenesis, FGF2 and MAP2K4 were validated by qRT-PCR, and they were significantly down-regulated(P<0.05). Conclusions: The research revealed seven miRNAs expression signature(hsa-miR-224, hsa-miR-183, hsa-miR-23a, hsa-miR-675, hsa-miR-27a, hsa-miR-503 and hsa-miR-4800-3p) of tumorigenesis in laryngeal squamous cell carcinoma,and analysed the physiological processes of the predicted target genes regulated by screened miRNAs in LSCC. The result will contribute to the understanding of the molecular basis of LSCC and help to improve the treatment. A total number of 6 patients who underwent surgery for primary laryngeal squamous cell carcinoma were recruited for miRNA array analysis. LSCC tissues compared with corresponding adjacent non-neoplastic tissues were analysed by the Affymetrix GeneChip miRNA Array 3.0 to screen effective miRNAs, then TargetScan,PITA and microRNAorg were used for target gene prediction in GeneSpring 12.5 software.Moreover, pathway-based methods were adopted to analyse physiological processes of the target genes. The screened miRNAs and the significant target genes were also validated by qRT-PCR in another 36 patients diagnosed for LSCC.
2014-10-30 | E-GEOD-62819 | biostudies-arrayexpress
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:MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA–target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute–miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.
2019-11-27 | GSE140218 | GEO
Project description:MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA–target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute–miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.
2019-11-27 | GSE140217 | GEO
Project description:MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA–target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute–miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.
2019-11-27 | GSE140216 | GEO
Project description:MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA–target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute–miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.
2019-11-27 | GSE140215 | GEO
Project description:MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA–target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute–miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.
2019-11-27 | GSE140214 | GEO
Project description:Validation of human microRNA target pathways enables evaluation of target prediction tools
| PRJNA557590 | ENA