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Ab initio identification of human microRNAs based on structure motifs.

Brameier M, Wiuf C - BMC Bioinformatics (2007)

Bottom Line: All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies.We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns.In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Research Center (BiRC), University of Aarhus, 8000 Aarhus C, Denmark. brameier@birc.au.dk

ABSTRACT

Background: MicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, ab initio approaches are able to discover species-specific miRNAs without known sequence homology.

Results: MiRPred is a novel method for ab initio prediction of miRNAs by genome scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns.

Conclusion: Our motif finding method is at least competitive to state-of-the-art feature-based methods for ab initio miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.

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Performance of the ensemble classifier including 16 individual classifiers for different voting thresholds, i.e., minimum numbers of required positive decisions. The maximum threshold (16/16) achieves a specificity of above 99.9 percent on an independent test set of randomly selected sequences while still maintaining a sensitivity of above 82 percent on all human miRNAs (also used for training). Majority voting (8/16) shows more balanced values, both higher than found for the individual classifiers (1/1).
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Figure 3: Performance of the ensemble classifier including 16 individual classifiers for different voting thresholds, i.e., minimum numbers of required positive decisions. The maximum threshold (16/16) achieves a specificity of above 99.9 percent on an independent test set of randomly selected sequences while still maintaining a sensitivity of above 82 percent on all human miRNAs (also used for training). Majority voting (8/16) shows more balanced values, both higher than found for the individual classifiers (1/1).

Mentions: The ROC curve in Figure 3 shows the performance of the ensemble classifier for different voting thresholds. Values are reported for an independent negative test set of 100,000 windowed sequences (different from the training set) and for all positive examples (as used for training). Majority voting (8/16) shows rather balanced prediction accuracies on the negative (99.3 percent) and positive (97 percent) examples. Such a configuration is appropriate if one wants to decide on certain candidate sequences as, e.g., suggested by other methods.


Ab initio identification of human microRNAs based on structure motifs.

Brameier M, Wiuf C - BMC Bioinformatics (2007)

Performance of the ensemble classifier including 16 individual classifiers for different voting thresholds, i.e., minimum numbers of required positive decisions. The maximum threshold (16/16) achieves a specificity of above 99.9 percent on an independent test set of randomly selected sequences while still maintaining a sensitivity of above 82 percent on all human miRNAs (also used for training). Majority voting (8/16) shows more balanced values, both higher than found for the individual classifiers (1/1).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238772&req=5

Figure 3: Performance of the ensemble classifier including 16 individual classifiers for different voting thresholds, i.e., minimum numbers of required positive decisions. The maximum threshold (16/16) achieves a specificity of above 99.9 percent on an independent test set of randomly selected sequences while still maintaining a sensitivity of above 82 percent on all human miRNAs (also used for training). Majority voting (8/16) shows more balanced values, both higher than found for the individual classifiers (1/1).
Mentions: The ROC curve in Figure 3 shows the performance of the ensemble classifier for different voting thresholds. Values are reported for an independent negative test set of 100,000 windowed sequences (different from the training set) and for all positive examples (as used for training). Majority voting (8/16) shows rather balanced prediction accuracies on the negative (99.3 percent) and positive (97 percent) examples. Such a configuration is appropriate if one wants to decide on certain candidate sequences as, e.g., suggested by other methods.

Bottom Line: All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies.We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns.In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Research Center (BiRC), University of Aarhus, 8000 Aarhus C, Denmark. brameier@birc.au.dk

ABSTRACT

Background: MicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, ab initio approaches are able to discover species-specific miRNAs without known sequence homology.

Results: MiRPred is a novel method for ab initio prediction of miRNAs by genome scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns.

Conclusion: Our motif finding method is at least competitive to state-of-the-art feature-based methods for ab initio miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.

Show MeSH
Related in: MedlinePlus