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Prediction of novel microRNA genes in cancer-associated genomic regions--a combined computational and experimental approach.

Oulas A, Boutla A, Gkirtzou K, Reczko M, Kalantidis K, Poirazi P - Nucleic Acids Res. (2009)

Bottom Line: The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array.Finally, four of the top scoring predictions are verified experimentally using northern blot analysis.Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Biology and Biotechnology-FORTH, Heraklion, University of Crete, Heraklion, Greece.

ABSTRACT
The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html.

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Histograms of the distributions of human miRNA (Red–Positive) and negative sequences (Blue–Negitive), as displayed by SSCprofiler. Only three of the eight filtering parameters are shown here. (A) Hairpin length, (B) Asymmetry and (C) Bulges-loops count. Looking at the distributions of positive and negative data, it is possible for the user to select cut-offs that separate the two distributions which can be used for filtering the data.
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Figure 4: Histograms of the distributions of human miRNA (Red–Positive) and negative sequences (Blue–Negitive), as displayed by SSCprofiler. Only three of the eight filtering parameters are shown here. (A) Hairpin length, (B) Asymmetry and (C) Bulges-loops count. Looking at the distributions of positive and negative data, it is possible for the user to select cut-offs that separate the two distributions which can be used for filtering the data.

Mentions: Illustration of data distributions for the various filtering parameters was done to facilitate the filtering process by enabling the adjustment of cut-off values according to the specific dataset. Cut-off values for each of these features are modifiable both prior and after the training procedure (see ‘Results’ section, Figure 4).Figure 4.


Prediction of novel microRNA genes in cancer-associated genomic regions--a combined computational and experimental approach.

Oulas A, Boutla A, Gkirtzou K, Reczko M, Kalantidis K, Poirazi P - Nucleic Acids Res. (2009)

Histograms of the distributions of human miRNA (Red–Positive) and negative sequences (Blue–Negitive), as displayed by SSCprofiler. Only three of the eight filtering parameters are shown here. (A) Hairpin length, (B) Asymmetry and (C) Bulges-loops count. Looking at the distributions of positive and negative data, it is possible for the user to select cut-offs that separate the two distributions which can be used for filtering the data.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2691815&req=5

Figure 4: Histograms of the distributions of human miRNA (Red–Positive) and negative sequences (Blue–Negitive), as displayed by SSCprofiler. Only three of the eight filtering parameters are shown here. (A) Hairpin length, (B) Asymmetry and (C) Bulges-loops count. Looking at the distributions of positive and negative data, it is possible for the user to select cut-offs that separate the two distributions which can be used for filtering the data.
Mentions: Illustration of data distributions for the various filtering parameters was done to facilitate the filtering process by enabling the adjustment of cut-off values according to the specific dataset. Cut-off values for each of these features are modifiable both prior and after the training procedure (see ‘Results’ section, Figure 4).Figure 4.

Bottom Line: The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array.Finally, four of the top scoring predictions are verified experimentally using northern blot analysis.Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Biology and Biotechnology-FORTH, Heraklion, University of Crete, Heraklion, Greece.

ABSTRACT
The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html.

Show MeSH