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The DIANA-mirExTra web server: from gene expression data to microRNA function.

Alexiou P, Maragkakis M, Papadopoulos GL, Simmosis VA, Zhang L, Hatzigeorgiou AG - PLoS ONE (2010)

Bottom Line: Here, we introduce a user-friendly web-server, DIANA-mirExTra (www.microrna.gr/mirextra) that allows the comparison of frequencies of miRNA associated motifs between sets of genes that can lead to the identification of miRNAs responsible for the deregulation of large numbers of genes.On several datasets of miRNA overexpression and repression experiments, our proposed approaches have successfully identified the deregulated miRNA.Additionally, in case information about miRNA expression changes is provided, the results can be filtered to display the analysis for miRNAs of interest only.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Sciences Research Center "Alexander Fleming", Institute of Molecular Oncology, Varkiza, Greece.

ABSTRACT

Background: High-throughput gene expression experiments are widely used to identify the role of genes involved in biological conditions of interest. MicroRNAs (miRNA) are regulatory molecules that have been functionally associated with several developmental programs and their deregulation with diverse diseases including cancer.

Methodology/principal findings: Although miRNA expression levels may not be routinely measured in high-throughput experiments, a possible involvement of miRNAs in the deregulation of gene expression can be computationally predicted and quantified through analysis of overrepresented motifs in the deregulated genes 3' untranslated region (3'UTR) sequences. Here, we introduce a user-friendly web-server, DIANA-mirExTra (www.microrna.gr/mirextra) that allows the comparison of frequencies of miRNA associated motifs between sets of genes that can lead to the identification of miRNAs responsible for the deregulation of large numbers of genes. To this end, we have investigated different approaches and measures, and have practically implemented them on experimental data.

Conclusions/significance: On several datasets of miRNA overexpression and repression experiments, our proposed approaches have successfully identified the deregulated miRNA. Beyond the prediction of miRNAs responsible for the deregulation of transcripts, the web-server provides extensive links to DIANA-mirPath, a functional analysis tool incorporating miRNA targets in biological pathways. Additionally, in case information about miRNA expression changes is provided, the results can be filtered to display the analysis for miRNAs of interest only.

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Related in: MedlinePlus

Combination of weighted -lnp values of the three hexamers.The weight for hexamer 1 is on the Y axis, for hexamer 2 is held constant at a value of 1, and for hexamer 3 is on the X axis. The mean normalized difference of the correct miRNA versus the next highest miRNA was maximized for 5 datasets of knocked out miRNAs (see Methods). The optimal weights combination for hexamers 1 and 3 were identified as 0.6 and 0 respectively. The value for hexamer 3 is still given in the Results page (see Figure 4) although it is not used for the combined score calculation.
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pone-0009171-g005: Combination of weighted -lnp values of the three hexamers.The weight for hexamer 1 is on the Y axis, for hexamer 2 is held constant at a value of 1, and for hexamer 3 is on the X axis. The mean normalized difference of the correct miRNA versus the next highest miRNA was maximized for 5 datasets of knocked out miRNAs (see Methods). The optimal weights combination for hexamers 1 and 3 were identified as 0.6 and 0 respectively. The value for hexamer 3 is still given in the Results page (see Figure 4) although it is not used for the combined score calculation.

Mentions: The hexamer starting at position 2 of the miRNA, frequently called the ‘seed’ hexamer (Figure 1), can be used for an approximate identification of miRNA binding sites, with identification precision similar to some dedicated target prediction algorithms (Selbach et al. 2008). However, more than one miRNAs may share the same seed hexamer. We investigated whether it is possible to distinguish between similar miRNAs by using the p-values of flanking hexamers 1 and 3. Weighted -lnp values of three hexamers corresponding to each miRNA were summed using different weights to produce a total hexamer score (Figure 5). As a result, DIANA-mirExTra provides a combinatorial hexamer score in which the -lnp value of hexamer 1 is multiplied by a weight of 0.6 and added to the -lnp value of hexamer 2, and hexamer 3 is not taken This approach allows a single score per miRNA that takes into account the whole active region of the 8 first nucleotides of the miRNA.


The DIANA-mirExTra web server: from gene expression data to microRNA function.

Alexiou P, Maragkakis M, Papadopoulos GL, Simmosis VA, Zhang L, Hatzigeorgiou AG - PLoS ONE (2010)

Combination of weighted -lnp values of the three hexamers.The weight for hexamer 1 is on the Y axis, for hexamer 2 is held constant at a value of 1, and for hexamer 3 is on the X axis. The mean normalized difference of the correct miRNA versus the next highest miRNA was maximized for 5 datasets of knocked out miRNAs (see Methods). The optimal weights combination for hexamers 1 and 3 were identified as 0.6 and 0 respectively. The value for hexamer 3 is still given in the Results page (see Figure 4) although it is not used for the combined score calculation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2820085&req=5

pone-0009171-g005: Combination of weighted -lnp values of the three hexamers.The weight for hexamer 1 is on the Y axis, for hexamer 2 is held constant at a value of 1, and for hexamer 3 is on the X axis. The mean normalized difference of the correct miRNA versus the next highest miRNA was maximized for 5 datasets of knocked out miRNAs (see Methods). The optimal weights combination for hexamers 1 and 3 were identified as 0.6 and 0 respectively. The value for hexamer 3 is still given in the Results page (see Figure 4) although it is not used for the combined score calculation.
Mentions: The hexamer starting at position 2 of the miRNA, frequently called the ‘seed’ hexamer (Figure 1), can be used for an approximate identification of miRNA binding sites, with identification precision similar to some dedicated target prediction algorithms (Selbach et al. 2008). However, more than one miRNAs may share the same seed hexamer. We investigated whether it is possible to distinguish between similar miRNAs by using the p-values of flanking hexamers 1 and 3. Weighted -lnp values of three hexamers corresponding to each miRNA were summed using different weights to produce a total hexamer score (Figure 5). As a result, DIANA-mirExTra provides a combinatorial hexamer score in which the -lnp value of hexamer 1 is multiplied by a weight of 0.6 and added to the -lnp value of hexamer 2, and hexamer 3 is not taken This approach allows a single score per miRNA that takes into account the whole active region of the 8 first nucleotides of the miRNA.

Bottom Line: Here, we introduce a user-friendly web-server, DIANA-mirExTra (www.microrna.gr/mirextra) that allows the comparison of frequencies of miRNA associated motifs between sets of genes that can lead to the identification of miRNAs responsible for the deregulation of large numbers of genes.On several datasets of miRNA overexpression and repression experiments, our proposed approaches have successfully identified the deregulated miRNA.Additionally, in case information about miRNA expression changes is provided, the results can be filtered to display the analysis for miRNAs of interest only.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Sciences Research Center "Alexander Fleming", Institute of Molecular Oncology, Varkiza, Greece.

ABSTRACT

Background: High-throughput gene expression experiments are widely used to identify the role of genes involved in biological conditions of interest. MicroRNAs (miRNA) are regulatory molecules that have been functionally associated with several developmental programs and their deregulation with diverse diseases including cancer.

Methodology/principal findings: Although miRNA expression levels may not be routinely measured in high-throughput experiments, a possible involvement of miRNAs in the deregulation of gene expression can be computationally predicted and quantified through analysis of overrepresented motifs in the deregulated genes 3' untranslated region (3'UTR) sequences. Here, we introduce a user-friendly web-server, DIANA-mirExTra (www.microrna.gr/mirextra) that allows the comparison of frequencies of miRNA associated motifs between sets of genes that can lead to the identification of miRNAs responsible for the deregulation of large numbers of genes. To this end, we have investigated different approaches and measures, and have practically implemented them on experimental data.

Conclusions/significance: On several datasets of miRNA overexpression and repression experiments, our proposed approaches have successfully identified the deregulated miRNA. Beyond the prediction of miRNAs responsible for the deregulation of transcripts, the web-server provides extensive links to DIANA-mirPath, a functional analysis tool incorporating miRNA targets in biological pathways. Additionally, in case information about miRNA expression changes is provided, the results can be filtered to display the analysis for miRNAs of interest only.

Show MeSH
Related in: MedlinePlus