A new method promises to discover viral microRNAs (small non-coding RNAs critically regulating gene expression) more efficiently, thus providing better insights into host-pathogen interactions1.
MicroRNAs are critical regulators of gene expression in multi-cellular eukaryotes, especially in humans. Studies have proved that viruses also express microRNAs.
Earlier, computational predictions were mostly dependent on structural features and sequence conservation which limited their use in discovering novel virus expressed microRNAs and non-conserved eukaryotic microRNAs.
The new prediction method makes use of the hypothesis that sequence and structure features that discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA . The viral microRNA depends on host machinery for processing microRNA precursors.
The new method can predict microRNAs in other eukaryotic organisms as well as discover novel non-conserved microRNAs systematically missed by prediction methods which rely on evolutionary conservation of sequences. It employs a support vector machine trained on sequence-structure feature elements for an efficient discrimination between microRNA precursor hairpins and pseudo microRNA hairpins.
The researchers have validated this approach for a number of known viral, chimpanzee, mouse and worm microRNA precursors derived from public databases.