Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA238583 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:miRNAs associated with the different human Argonaute proteins
| PRJNA195387 | ENA
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: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:It is commonly known that mammalian microRNAs guide the RNA-induced silencing complex (RISC) to target mRNAs through the seed-pairing rule. However, recent experiments by co-immunoprecipitating the argonaute proteins (AGOs), the central catalytic component of RISC, have consistently revealed extensive AGO-associated mRNAs lacking seed complementarity with microRNAs. We herein test the hypothesis that AGO has its own binding preference within target mRNAs, independent of guide microRNAs. By systematically analyzing the data from in vivo cross-linking experiments with human AGOs, we have identified a structurally accessible and evolutionarily conserved region (~10 nucleotides) that alone can accurately predict AGO-mRNA associations, independent of the presence of microRNA binding sites. Within this region, we further identified an enriched motif that is also consistently present in several independent AGO-immunoprecipitation datasets. We used RNAcompete to enumerate the RNA-binding preference of human AGO2 to all possible 7-mer RNA sequences, and validated our motif in vitro. These findings reveal a novel function of AGOs as sequence-specific RNA-binding proteins, which may aid microRNAs in recognizing their targets with high specificity. Here, we analyze the RNA-binding preference of human Argonaute 2 protein using RNAcompete assay
2014-02-19 | E-GEOD-55122 | biostudies-arrayexpress
Project description:Sequencing studies from several model systems have suggested that diverse and abundant small RNAs may derive from tRNA, but the function of these molecules remains undefined. Here we demonstrate that one such tRNA fragment, cloned from human B cells and designated CU1276, in fact possesses the functional characteristics of a microRNA, including a DICER1-dependent biogenesis, physical association with Argonaute proteins, and the ability to repress mRNA transcripts in a sequence-specific manner. The gene expression profiling undertaken for this study was done in order to assay mRNA-level changes in 293T cells upon modulation of CU1276 levels, and thereby to identify direct targets of this sequence. Ultimately, we fully validated the endogenous gene RPA1 as a CU1276 target.
2012-12-19 | GSE42989 | GEO
Project description:Correct pre-mRNA processing in higher eukaryotes vastly depends on splice site recognition. Beyond conserved 5’ss and 3’ss motifs, splicing regulatory elements (SREs) play a pivotal role in this recognition process. Here, we present in silico designed sequences with arbitrary a priori prescribed splicing regulatory HEXplorer properties that can be concatenated to arbitrary length without changing their regulatory properties. We performed pulldown analysis to identify RNA-binding proteins, that bind either sequence segments with a HEXplorer score amplitude of +10.32, –0.15 or -10.35.
2022-10-13 | PXD030139 | Pride
Project description:Post-transcriptional gene regulation controls the amount of a protein produced from an individual mRNA transcript by altering mRNA decay and translation rates. Many putative post-transcriptional cis-regulatory elements have been identified from computational, molecular biology, and biochemical studies studies; however, identifying which sequence elements are sufficient to regulate expression remains challenging. We created a high-throughput, cell-based screen that tested the post-transcriptional regulatory potential for thousands of short sequence elements. Sequences with known effects have the expected performance in this screen, showing this methodology is robust. Hundreds of novel short sequences were identified as being able to alter gene expression, both by increasing and decreasing protein production from the fluorescence reporter, and we validated the effects for fifty of these sequences. Importantly, sequences discovered in this screen are conserved in human 3′UTRs, and furthermore, the sequences can regulate expression in the context of those endogenous 3′UTRs. Hundreds of previously unknown post-transcriptional cis-regulatory elements exist, many of which increase gene expression. These results suggest that each human 3′UTR has many small cis-regulatory elements that interact with RNA binding proteins, and these interactions control the fate of an mRNA transcript.
2016-03-07 | GSE75136 | GEO