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Prediction of viral microRNA precursors based on human microRNA precursor sequence and structural features.

Kumar S, Ansari FA, Scaria V - Virol. J. (2009)

Bottom Line: Computational predictions have greatly accelerated the discovery of microRNAs.In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA as they depend on host machinery for the processing of microRNA precursors.The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals.

View Article: PubMed Central - HTML - PubMed

Affiliation: GN Ramachandran Knowledge Center for Genome Informatics, Institute of Genomics and Integrative Biology, CSIR, Delhi, India. shiva.kumar@igib.res.in

ABSTRACT
MicroRNAs (small approximately 22 nucleotide long non-coding endogenous RNAs) have recently attracted immense attention as critical regulators of gene expression in multi-cellular eukaryotes, especially in humans. Recent studies have proved that viruses also express microRNAs, which are thought to contribute to the intricate mechanisms of host-pathogen interactions. Computational predictions have greatly accelerated the discovery of microRNAs. However, most of these widely used tools are dependent on structural features and sequence conservation which limits their use in discovering novel virus expressed microRNAs and non-conserved eukaryotic microRNAs. In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA as they depend on host machinery for the processing of microRNA precursors. The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals.

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Related in: MedlinePlus

Illustrative summary of the process flow in the presented method for microRNA precursor prediction. The lines in red denote the process flow in prediction while the lines in dark blue denote the process flow during training.
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Figure 1: Illustrative summary of the process flow in the presented method for microRNA precursor prediction. The lines in red denote the process flow in prediction while the lines in dark blue denote the process flow during training.

Mentions: The models were created by taking a non overlapping random samples amounting to half of the positive and negative datasets for training and were evaluated on the remaining dataset. The process of model generation and testing is summarized in Fig 1. One hundred such models were created and evaluated by random sampling. Models were analysed for sensitivity and specificity, where:


Prediction of viral microRNA precursors based on human microRNA precursor sequence and structural features.

Kumar S, Ansari FA, Scaria V - Virol. J. (2009)

Illustrative summary of the process flow in the presented method for microRNA precursor prediction. The lines in red denote the process flow in prediction while the lines in dark blue denote the process flow during training.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2743665&req=5

Figure 1: Illustrative summary of the process flow in the presented method for microRNA precursor prediction. The lines in red denote the process flow in prediction while the lines in dark blue denote the process flow during training.
Mentions: The models were created by taking a non overlapping random samples amounting to half of the positive and negative datasets for training and were evaluated on the remaining dataset. The process of model generation and testing is summarized in Fig 1. One hundred such models were created and evaluated by random sampling. Models were analysed for sensitivity and specificity, where:

Bottom Line: Computational predictions have greatly accelerated the discovery of microRNAs.In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA as they depend on host machinery for the processing of microRNA precursors.The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals.

View Article: PubMed Central - HTML - PubMed

Affiliation: GN Ramachandran Knowledge Center for Genome Informatics, Institute of Genomics and Integrative Biology, CSIR, Delhi, India. shiva.kumar@igib.res.in

ABSTRACT
MicroRNAs (small approximately 22 nucleotide long non-coding endogenous RNAs) have recently attracted immense attention as critical regulators of gene expression in multi-cellular eukaryotes, especially in humans. Recent studies have proved that viruses also express microRNAs, which are thought to contribute to the intricate mechanisms of host-pathogen interactions. Computational predictions have greatly accelerated the discovery of microRNAs. However, most of these widely used tools are dependent on structural features and sequence conservation which limits their use in discovering novel virus expressed microRNAs and non-conserved eukaryotic microRNAs. In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA as they depend on host machinery for the processing of microRNA precursors. The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals.

Show MeSH
Related in: MedlinePlus