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Towards computational prediction of microRNA function and activity.

ABSTRACT: While it has been established that microRNAs (miRNAs) play key roles throughout development and are dysregulated in many human pathologies, the specific processes and pathways regulated by individual miRNAs are mostly unknown. Here, we use computational target predictions in order to automatically infer the processes affected by human miRNAs. Our approach improves upon standard statistical tools by addressing specific characteristics of miRNA regulation. Our analysis is based on a novel compendium of experimentally verified miRNA-pathway and miRNA-process associations that we constructed, which can be a useful resource by itself. Our method also predicts novel miRNA-regulated pathways, refines the annotation of miRNAs for which only crude functions are known, and assigns differential functions to miRNAs with closely related sequences. Applying our approach to groups of co-expressed genes allows us to identify miRNAs and genomic miRNA clusters with functional importance in specific stages of early human development. A full list of the predicted mRNA functions is available at http://acgt.cs.tau.ac.il/fame/.

SUBMITTER: Ulitsky I

PROVIDER: S-EPMC2926627 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Towards computational prediction of microRNA function and activity.

Ulitsky Igor I Laurent Louise C LC Shamir Ron R

Nucleic acids research 20100624 15

While it has been established that microRNAs (miRNAs) play key roles throughout development and are dysregulated in many human pathologies, the specific processes and pathways regulated by individual miRNAs are mostly unknown. Here, we use computational target predictions in order to automatically infer the processes affected by human miRNAs. Our approach improves upon standard statistical tools by addressing specific characteristics of miRNA regulation. Our analysis is based on a novel compendi ...[more]

PMID: 20576699

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Project description:BACKGROUND: MicroRNA are a class of small RNAs that regulate gene expression by inhibiting translation of protein encoding transcripts through targeting of a microRNA-protein complex by base-pairing of the microRNA sequence to cognate recognition sequences in the 3' untranslated region (UTR) of the mRNA. Target identification for a given microRNA sequence is generally accomplished by informatics analysis of predicted mRNA sequences present in the genome or in databases of transcript sequence for the tissue of interest. However, gene models for porcine skeletal muscle transcripts in current databases, specifically complete sequence of the 3' UTR, are inadequate for this exercise. METHODOLOGY/PRINCIPAL FINDINGS: To provide data necessary to identify gene targets for microRNA in porcine skeletal muscle, normalized cDNA libraries were sequenced using Roche 454 GS-FLX pyrosequencing and de novo assembly of transcripts enriched in the 3' UTR was performed using the MIRA sequence assembly program. Over 725 million bases of sequence were generated, which assembled into 18,202 contigs. Sequence reads were mapped to a 3' UTR database containing porcine sequences. The 3' UTR that mapped to the database were examined to predict targets for previously identified microRNA that had been separately sequenced from the same porcine muscle sample used to generate the cDNA libraries. For genes with microRNA-targeted 3' UTR, KEGG pathways were computationally determined in order to identify potential functional effects of these microRNA-targeted transcripts. CONCLUSIONS: Through next-generation sequencing of transcripts expressed in skeletal muscle, mapping reads to a 3' UTR database, and prediction of microRNA target sites in the 3' UTR, our results identified genes expressed in porcine skeletal muscle and predicted the microRNA that target these genes. Additionally, identification of pathways regulated by these microRNA-targeted genes provides us with a set of genes that can be further evaluated for their potential role in skeletal muscle development and growth.

Dataset Information

Towards computational prediction of microRNA function and activity.

Publications

Towards computational prediction of microRNA function and activity.

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