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MiRmap: comprehensive prediction of microRNA target repression strength.

Vejnar CE, Zdobnov EM - Nucleic Acids Res. (2012)

Bottom Line: We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel.This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments.Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetic Medicine and Development, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland.

ABSTRACT
MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA 'seed' region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap.

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miRmap library usage: after importing the library (lines 1 and 2), a ‘mimset’ object is created containing the mRNA and miRNA sequences. We then call a method of the mimset object to search (line 5) for seeds with a length of 7 (all parameters have defaults that can be changed this way). The link with the C libraries is initalized on line 7. We then manually evaluate the repression strength with differents methods (lines 9–16). Each of these methods have modifiable parameters. We finally print a report (line 18).
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gks901-F1: miRmap library usage: after importing the library (lines 1 and 2), a ‘mimset’ object is created containing the mRNA and miRNA sequences. We then call a method of the mimset object to search (line 5) for seeds with a length of 7 (all parameters have defaults that can be changed this way). The link with the C libraries is initalized on line 7. We then manually evaluate the repression strength with differents methods (lines 9–16). Each of these methods have modifiable parameters. We finally print a report (line 18).

Mentions: We developed a comprehensive prediction model implemented as the miRmap open-source Python library (Figure 1) with a total of 11 features covering a wide range of published and novel methods (Table 2). With our own implementation, we compared the different features without the biases inherent to comparison of pre-computed predictions. We evaluated the features' individual predictive power, measured their intercorrelations and examined different combinations of methods. Additionally, in order to facilitate the library usage, five features are implemented in pure Python.Figure 1.


MiRmap: comprehensive prediction of microRNA target repression strength.

Vejnar CE, Zdobnov EM - Nucleic Acids Res. (2012)

miRmap library usage: after importing the library (lines 1 and 2), a ‘mimset’ object is created containing the mRNA and miRNA sequences. We then call a method of the mimset object to search (line 5) for seeds with a length of 7 (all parameters have defaults that can be changed this way). The link with the C libraries is initalized on line 7. We then manually evaluate the repression strength with differents methods (lines 9–16). Each of these methods have modifiable parameters. We finally print a report (line 18).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3526310&req=5

gks901-F1: miRmap library usage: after importing the library (lines 1 and 2), a ‘mimset’ object is created containing the mRNA and miRNA sequences. We then call a method of the mimset object to search (line 5) for seeds with a length of 7 (all parameters have defaults that can be changed this way). The link with the C libraries is initalized on line 7. We then manually evaluate the repression strength with differents methods (lines 9–16). Each of these methods have modifiable parameters. We finally print a report (line 18).
Mentions: We developed a comprehensive prediction model implemented as the miRmap open-source Python library (Figure 1) with a total of 11 features covering a wide range of published and novel methods (Table 2). With our own implementation, we compared the different features without the biases inherent to comparison of pre-computed predictions. We evaluated the features' individual predictive power, measured their intercorrelations and examined different combinations of methods. Additionally, in order to facilitate the library usage, five features are implemented in pure Python.Figure 1.

Bottom Line: We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel.This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments.Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetic Medicine and Development, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland.

ABSTRACT
MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA 'seed' region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap.

Show MeSH