Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The biochemical basis of microRNA targeting efficacy [5]

ABSTRACT: The biochemical basis of microRNA targeting efficacy [5]

PROVIDER: PRJNA592020 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR10432947.fastq.gz	Fastqsanger.gz
	SRR10432948.fastq.gz	Fastqsanger.gz
	SRR10432949.fastq.gz	Fastqsanger.gz
	SRR10432950.fastq.gz	Fastqsanger.gz
	SRR10432951.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 24

Similar Datasets

The biochemical basis of microRNA targeting efficacy

Project description:The biochemical basis of microRNA targeting efficacy

| PRJNA591816 | ENA

The biochemical basis of microRNA targeting efficacy [1]

Project description:The biochemical basis of microRNA targeting efficacy [1]

| PRJNA588963 | ENA

The biochemical basis of microRNA targeting efficacy [3]

Project description:The biochemical basis of microRNA targeting efficacy [3]

| PRJNA592022 | ENA

The biochemical basis of microRNA targeting efficacy [4]

Project description:The biochemical basis of microRNA targeting efficacy [4]

| PRJNA592021 | ENA

The biochemical basis of microRNA targeting efficacy [2]

Project description:The biochemical basis of microRNA targeting efficacy [2]

| PRJNA591822 | ENA

The biochemical basis of microRNA targeting efficacy [2]

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

The biochemical basis of microRNA targeting efficacy [1]

2019-11-27 | GSE140214 | GEO