Computational Methods for Identification of Human microRNA Precursors

MicroRNA (miRNA), one of non-coding RNAs (ncRNAs), regulates gene expression directly by arresting the messenger RNA (mRNA) translation, which is important for identifying putative miRNAs. In this study, we suggest a searching procedure for human miRNA precursors using genetic programming that automatically learn common structures of miRNAs from a set of known miRNA precursors. Our method consists of three-steps. At first, for each miRNA precursor, we adopted genetic programming techniques to optimize the RNA Common-Structural Grammar (RCSG) of populations until certain fitness is achieved. In this step, the specificity and the sensitivity of a RCSG for the training data set were used as the fitness criteria. Next, for each optimized RCSG, we collected candidates of matching miRNA precursors with the corresponding grammar from genome databases. Finally, we selected miRNA precursors over a threshold (= 365) of scoring model from the candidates. This step would reduce false positives in the candidates. To validate the effectiveness of our miRNA method, we evaluated the learned RCSG and the scoring model with test data. Here, we obtained satisfactory results, with high specificity (= 51/64) and proper sensitivity (= 51/82) using human miRNA precursors as a test data set.

[1]  Rolf Backofen,et al.  MARNA: A server for multiple alignment of RNAs , 2003, German Conference on Bioinformatics.

[2]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[3]  D. Ecker,et al.  RNAMotif, an RNA secondary structure definition and search algorithm. , 2001, Nucleic acids research.

[4]  Peter J. Angeline,et al.  Advances in genetic programming: volume 2 , 1996 .

[5]  Byoung-Tak Zhang,et al.  Evolutionary neural trees for modeling and predicting complex systems , 1997 .

[6]  P. Lawrence,et al.  Genes in development , 1977, Nature.

[7]  Sholom M. Weiss,et al.  An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods , 1989, IJCAI.

[8]  A. Hüttenhofer,et al.  RNomics: identification and function of small, non-messenger RNAs. , 2002, Current opinion in chemical biology.

[9]  G. Rubin,et al.  Computational identification of Drosophila microRNA genes , 2003, Genome Biology.

[10]  G. Dreyfuss,et al.  Numerous microRNPs in neuronal cells containing novel microRNAs. , 2003, RNA.

[11]  V. Ambros.  microRNAs Tiny Regulators with Great Potential , 2001, Cell.

[12]  Phillip D Zamore,et al.  Ancient Pathways Programmed by Small RNAs , 2002, Science.

[13]  S. Gottesman,et al.  Stealth regulation: biological circuits with small RNA switches. , 2002, Genes & development.

[14]  C. Burge,et al.  Vertebrate MicroRNA Genes , 2003, Science.

[15]  T. Tuschl,et al.  New microRNAs from mouse and human. , 2003, RNA.

[16]  T. Tuschl,et al.  Identification of Novel Genes Coding for Small Expressed RNAs , 2001, Science.