Review: Computational identification of microRNAs and their targets
Computational Biology and Chemistry
Computational Biology and Chemistry
PMirP: A pre-microRNA prediction method based on structure-sequence hybrid features
Artificial Intelligence in Medicine
Exploring essential attributes for detecting MicroRNA precursors from background sequences
VDMB'06 Proceedings of the First international conference on Data Mining and Bioinformatics
An SVM-Based approach to discover MicroRNA precursors in plant genomes
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
mirPD: A pattern-based approach for identifying microRNAs from deep sequencing data
Digital Signal Processing
| Hi-index | 3.84 |
Motivation: Most non-coding RNAs are characterized by a specific secondary and tertiary structure that determines their function. Here, we investigate the folding energy of the secondary structure of non-coding RNA sequences, such as microRNA precursors, transfer RNAs and ribosomal RNAs in several eukaryotic taxa. Statistical biases are assessed by a randomization test, in which the predicted minimum free energy of folding is compared with values obtained for structures inferred from randomly shuffling the original sequences. Results: In contrast with transfer RNAs and ribosomal RNAs, the majority of the microRNA sequences clearly exhibit a folding free energy that is considerably lower than that for shuffled sequences, indicating a high tendency in the sequence towards a stable secondary structure. A possible usage of this statistical test in the framework of the detection of genuine miRNA sequences is discussed. Availability: The dataset, software and additional data files are freely available as supplementary information on our Website. Supplementary information: http://www.psb.ugent.be/bioinformatics/