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Towards computational prediction of microRNA function and activity.

Ulitsky I, Laurent LC, Shamir R - Nucleic Acids Res. (2010)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. ulitsky@wi.mit.edu

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/.

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mRNA co-expression clusters and miRNA regulation in stem cell lines. (A) Enrichment and depletion of miRNA targets in co-expression clusters. Purple (green) squares indicate over- (under-) representation of miRNA targets in a cluster. Names of genomic clusters of miRNAs (Supplementary Table S1) are written in red. Only clusters with at least 30 genes that were enriched with targets of at least one miRNA with P < 3 × 10−3 and FDR < 0.1 are shown. Cluster-miRNA pairs with P > 0.05 are not shown (white squares). (B–F) Average expression levels of the mRNAs in co-expression clusters and of miRNAs in different families. The top rows in each subfigure show average mRNA expression of the co-expression clusters and the matrices below them show the expression of the miRNA families under the same conditions. The expression pattern of each miRNA and each mRNA were normalized to mean 0 and SD of 1. Fib., fibroblasts; CC, choriocarcinoma (placental cancer).
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Figure 4: mRNA co-expression clusters and miRNA regulation in stem cell lines. (A) Enrichment and depletion of miRNA targets in co-expression clusters. Purple (green) squares indicate over- (under-) representation of miRNA targets in a cluster. Names of genomic clusters of miRNAs (Supplementary Table S1) are written in red. Only clusters with at least 30 genes that were enriched with targets of at least one miRNA with P < 3 × 10−3 and FDR < 0.1 are shown. Cluster-miRNA pairs with P > 0.05 are not shown (white squares). (B–F) Average expression levels of the mRNAs in co-expression clusters and of miRNAs in different families. The top rows in each subfigure show average mRNA expression of the co-expression clusters and the matrices below them show the expression of the miRNA families under the same conditions. The expression pattern of each miRNA and each mRNA were normalized to mean 0 and SD of 1. Fib., fibroblasts; CC, choriocarcinoma (placental cancer).

Mentions: Overall, at FDR < 0.1, we identified enrichment or depletion of targets of 68 miRNA families and 27 genomic clusters in 21 co-expression clusters (Figure 4A). Of the 68 miRNA families, 16 are known to be related to stem cell biology [out of 25 stem cell-related families taken from (52), P = 0.027], indicating that our cluster-based analysis is capable of revealing functionally relevant miRNAs. In comparison at FDR < 0.1, the HG test reported significant enrichment or depletion for 77 miRNA families, but the overlap with the stem cell-related families was not significant (P = 0.344).Figure 4.


Towards computational prediction of microRNA function and activity.

Ulitsky I, Laurent LC, Shamir R - Nucleic Acids Res. (2010)

mRNA co-expression clusters and miRNA regulation in stem cell lines. (A) Enrichment and depletion of miRNA targets in co-expression clusters. Purple (green) squares indicate over- (under-) representation of miRNA targets in a cluster. Names of genomic clusters of miRNAs (Supplementary Table S1) are written in red. Only clusters with at least 30 genes that were enriched with targets of at least one miRNA with P < 3 × 10−3 and FDR < 0.1 are shown. Cluster-miRNA pairs with P > 0.05 are not shown (white squares). (B–F) Average expression levels of the mRNAs in co-expression clusters and of miRNAs in different families. The top rows in each subfigure show average mRNA expression of the co-expression clusters and the matrices below them show the expression of the miRNA families under the same conditions. The expression pattern of each miRNA and each mRNA were normalized to mean 0 and SD of 1. Fib., fibroblasts; CC, choriocarcinoma (placental cancer).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC2926627&req=5

Figure 4: mRNA co-expression clusters and miRNA regulation in stem cell lines. (A) Enrichment and depletion of miRNA targets in co-expression clusters. Purple (green) squares indicate over- (under-) representation of miRNA targets in a cluster. Names of genomic clusters of miRNAs (Supplementary Table S1) are written in red. Only clusters with at least 30 genes that were enriched with targets of at least one miRNA with P < 3 × 10−3 and FDR < 0.1 are shown. Cluster-miRNA pairs with P > 0.05 are not shown (white squares). (B–F) Average expression levels of the mRNAs in co-expression clusters and of miRNAs in different families. The top rows in each subfigure show average mRNA expression of the co-expression clusters and the matrices below them show the expression of the miRNA families under the same conditions. The expression pattern of each miRNA and each mRNA were normalized to mean 0 and SD of 1. Fib., fibroblasts; CC, choriocarcinoma (placental cancer).
Mentions: Overall, at FDR < 0.1, we identified enrichment or depletion of targets of 68 miRNA families and 27 genomic clusters in 21 co-expression clusters (Figure 4A). Of the 68 miRNA families, 16 are known to be related to stem cell biology [out of 25 stem cell-related families taken from (52), P = 0.027], indicating that our cluster-based analysis is capable of revealing functionally relevant miRNAs. In comparison at FDR < 0.1, the HG test reported significant enrichment or depletion for 77 miRNA families, but the overlap with the stem cell-related families was not significant (P = 0.344).Figure 4.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. ulitsky@wi.mit.edu

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/.

Show MeSH
Related in: MedlinePlus