A quantitative map of human primary microRNA processing sites [2]
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ABSTRACT:
PROVIDER: PRJNA728879 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14494537_1.fastq.gz | Fastqsanger.gz | |||
| SRR14494537_2.fastq.gz | Fastqsanger.gz | |||
| SRR14494538_1.fastq.gz | Fastqsanger.gz | |||
| SRR14494538_2.fastq.gz | Fastqsanger.gz | |||
| SRR14494539_1.fastq.gz | Fastqsanger.gz |
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Project description:A quantitative map of human primary microRNA processing sites
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Project description:A quantitative map of human primary microRNA processing sites [1]
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Project description:Maturation of canonical microRNA (miRNA) is initiated by DROSHA that cleaves the primary transcript (pri-miRNA). Over 1,800 miRNA loci are annotated in humans, but it remains largely unknown if and at which sites the pri-miRNAs are cleaved by DROSHA. Here we performed in vitro processing on a full set of human pri-miRNAs (miRBase v21) followed by sequencing. This comprehensive profiling enabled us to classify miRNAs based on DROSHA-dependence and map their cleavage sites with respective processing efficiency measures. Only 758 pri-miRNAs are confidently processed by DROSHA, while the majority may be non-canonical or false entries. Analyses of the DROSHA-dependent pri-miRNAs show key cis-elements for processing. We observe widespread alternative processing as well as unproductive cleavage events such as “nick” or “inverse” processing. SRSF3 is a broad-acting auxiliary factor modulating alternative processing and suppressing unproductive processing. The profiling data and methods developed in this study will allow systematic analyses of miRNA regulation.
2021-07-27 | GSE174143 | GEO
Project description:Maturation of canonical microRNA (miRNA) is initiated by DROSHA that cleaves the primary transcript (pri-miRNA). Over 1,800 miRNA loci are annotated in humans, but it remains largely unknown if and at which sites the pri-miRNAs are cleaved by DROSHA. Here we performed in vitro processing on a full set of human pri-miRNAs (miRBase v21) followed by sequencing. This comprehensive profiling enabled us to classify miRNAs based on DROSHA-dependence and map their cleavage sites with respective processing efficiency measures. Only 758 pri-miRNAs are confidently processed by DROSHA, while the majority may be non-canonical or false entries. Analyses of the DROSHA-dependent pri-miRNAs show key cis-elements for processing. We observe widespread alternative processing as well as unproductive cleavage events such as “nick” or “inverse” processing. SRSF3 is a broad-acting auxiliary factor modulating alternative processing and suppressing unproductive processing. The profiling data and methods developed in this study will allow systematic analyses of miRNA regulation.
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Project description:All classes of RNA undergo processing. The two largest RNA processing complexes are those involved in rRNA biogenesis and mRNA splicing. Because of their complexity and essential roles, it has been difficult to dissect the functional interactions within these machines as well as to identify possible cross-talk with other steps in gene expression. Here we report the results of a high-density, quantitative genetic interaction map comprising 107,155 individual interactions involving 552 mutants, 166 of which are hypomorphic alleles of essential genes (This data is available at The Krogan Lab Interactome Database). Our data allow unique insights into the functional organization of the RNA processing machines. For example, we identify unexpected connections involving a component of the 19S proteasome, Sem1/Dss1. We show that Sem1 has genetic, physical and functional interactions with the mRNA export complex, Thp1-Sac3; it also interacts physically with a putative component of the COP9 signalosome, Csn12. We show using splicing-specific microarrays that Csn12 has an unanticipated role in mRNA splicing. Finally, we demonstrate the utility of this dataset in determining functions for uncharacterized ORFs. We show that deletion of the uncharacterized YNR004W causes a very similar splicing defect to that caused by deletion of TGS1, the enzyme that trimethylates the caps of snRNAs and snoRNAs. Keywords: E-MAP, RNA Processing, splicing, splicing-specific microarray, CSN12, COP9 signalosome
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Project description:All classes of RNA undergo processing. The two largest RNA processing complexes are those involved in rRNA biogenesis and mRNA splicing. Because of their complexity and essential roles, it has been difficult to dissect the functional interactions within these machines as well as to identify possible cross-talk with other steps in gene expression. Here we report the results of a high-density, quantitative genetic interaction map comprising 107,155 individual interactions involving 552 mutants, 166 of which are hypomorphic alleles of essential genes (This data is available at The Krogan Lab Interactome Database). Our data allow unique insights into the functional organization of the RNA processing machines. For example, we identify unexpected connections involving a component of the 19S proteasome, Sem1/Dss1. We show that Sem1 has genetic, physical and functional interactions with the mRNA export complex, Thp1-Sac3; it also interacts physically with a putative component of the COP9 signalosome, Csn12. We show using splicing-specific microarrays that Csn12 has an unanticipated role in mRNA splicing. Finally, we demonstrate the utility of this dataset in determining functions for uncharacterized ORFs. We show that deletion of the uncharacterized YNR004W causes a very similar splicing defect to that caused by deletion of TGS1, the enzyme that trimethylates the caps of snRNAs and snoRNAs. Keywords: E-MAP, RNA Processing, splicing, splicing-specific microarray, CSN12, COP9 signalosome Splicing-specific microarrays were used to assay the changes to splicing caused by deletion of several non-essential genes which had no previously characterized role in mRNA splicing but were suggested to be involved in this process by high-throughput genetic and/or protein-protein interaction data in Saccharomyces cerevisiae. The data includes samples collected from some mutants in log phase growth at 30 degrees C and from some mutants shifted to 16 degrees C, all competitively hybridized against wild type samples collected under the same conditions. .
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