Comparing Sequence and Expression for Predicting microRNA Targets Using GenMIR3

We present a new model and learning algorithm, GenMiR3, which takes into account mRNA sequence features in addition to paired mRNA and miRNA expression profiles when scoring candidate miRNA-mRNA interactions. We evaluate three candidate sequence features for predicting miRNA targets by assessing the expression support for the predictions of each feature and the consistency of Gene Ontology Biological Process annotation of their target sets. We consider as sequence features the total energy of hybridization between the microRNA and target, conservation of the target site and the context score which is a composite of five individual sequence features. We demonstrate that only the total energy of hybridization is predictive of paired miRNA and mRNA expression data and Gene Ontology enrichment but this feature adds little to the total accuracy of GenMiR3 predictions using for expression features alone.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[3]  Hagai Attias,et al.  Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.

[4]  T. Poggio,et al.  Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Ivo L. Hofacker,et al.  Vienna RNA secondary structure server , 2003, Nucleic Acids Res..

[6]  Terrence S. Furey,et al.  The UCSC Genome Browser Database , 2003, Nucleic Acids Res..

[7]  V. Ambros The functions of animal microRNAs , 2004, Nature.

[8]  K. Gunsalus,et al.  Combinatorial microRNA target predictions , 2005, Nature Genetics.

[9]  J. Castle,et al.  Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs , 2005, Nature.

[10]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[11]  Bart De Moor,et al.  BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis , 2005, Bioinform..

[12]  D. Haussler,et al.  Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. , 2005, Genome research.

[13]  H. Horvitz,et al.  MicroRNA expression profiles classify human cancers , 2005, Nature.

[14]  A. Pasquinelli,et al.  Regulation by let-7 and lin-4 miRNAs Results in Target mRNA Degradation , 2005, Cell.

[15]  B. Frey,et al.  Bayesian Learning of MicroRNA Targets from Sequence and Expression Data , 2006 .

[16]  N. Rajewsky,et al.  Cell-type-specific signatures of microRNAs on target mRNA expression. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[17]  L. Lim,et al.  MicroRNA targeting specificity in mammals: determinants beyond seed pairing. , 2007, Molecular cell.

[18]  Dang D. Long,et al.  Potent effect of target structure on microRNA function , 2007, Nature Structural &Molecular Biology.

[19]  B. Frey,et al.  Using expression profiling data to identify human microRNA targets , 2007, Nature Methods.