Review: Computational identification of microRNAs and their targets
Computational Biology and Chemistry
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
MicroRNAs and cancer-the search begins!
IEEE Transactions on Information Technology in Biomedicine
DuplexFinder: Predicting the miRNA miRNA* duplex from the animal precursors
International Journal of Bioinformatics Research and Applications
Statistical inference on distinct RNA stem-loops in genomic sequences
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Accelerated microRNA-precursor detection using the Smith-Waterman algorithm on FPGAs
GCCB'06 Proceedings of the 2006 international conference on Distributed, high-performance and grid computing in computational biology
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
International Journal of Data Mining and Bioinformatics
Prediction of pre-miRNA with multiple stem-loops using pruning algorithm
Computers in Biology and Medicine
Mining Featured Patterns of MiRNA Interaction Based on Sequence and Structure Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
mirPD: A pattern-based approach for identifying microRNAs from deep sequencing data
Digital Signal Processing
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Motivation: MicroRNAs (miRNA) are ∼22 nt long non-coding RNAs that are derived from larger hairpin RNA precursors and play important regulatory roles in both animals and plants. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods to finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure rather than in primary sequences. Therefore, secondary structural features should be more fully exploited in the homologue search for new miRNA genes. Results: In this paper, we present a novel genome-wide computational approach to detect miRNAs in animals based on both sequence and structure alignment. Experiments show this approach has higher sensitivity and comparable specificity than other reported homologue searching methods. We applied this method on Anopheles gambiae and detected 59 new miRNA genes. Availability: This program is available at http://bioinfo.au.tsinghua.edu.cn/miralign Contact: daulyd@tsinghua.edu.cn Supplementary information: Supplementary information is available at http://bioinfo.au.tsinghua.edu.cn/miralign/supplementary.htm