Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases

MOTIVATION It is popular to explore meaningful molecular targets and infer new functions of genes through gene functional similarity measuring and gene functional network construction. However, little work is available in this field for microRNA (miRNA) genes due to limited miRNA functional annotations. With the rapid accumulation of miRNAs, it is increasingly needed to uncover their functional relationships in a systems level. RESULTS It is known that genes with similar functions are often associated with similar diseases, and the relationship of different diseases can be represented by a structure of directed acyclic graph (DAG). This is also true for miRNA genes. Therefore, it is feasible to infer miRNA functional similarity by measuring the similarity of their associated disease DAG. Based on the above observations and the rapidly accumulated human miRNA-disease association data, we presented a method to infer the pairwise functional similarity and functional network for human miRNAs based on the structures of their disease relationships. Comparisons showed that the calculated miRNA functional similarity is well associated with prior knowledge of miRNA functional relationship. More importantly, this method can also be used to predict novel miRNA biomarkers and to infer novel potential functions or associated diseases for miRNAs. In addition, this method can be easily extended to other species when sufficient miRNA-associated disease data are available for specific species. AVAILABILITY The online tool is available at http://cmbi.bjmu.edu.cn/misim CONTACT cuiqinghua@hsc.pku.edu.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  Phillip W. Lord,et al.  Semantic Similarity in Biomedical Ontologies , 2009, PLoS Comput. Biol..

[2]  Philip S. Yu,et al.  G-SESAME: web tools for GO-term-based gene similarity analysis and knowledge discovery , 2009, Nucleic Acids Res..

[3]  Edwin Wang,et al.  Cepred: Predicting the Co-Expression Patterns of the Human Intronic microRNAs with Their Host Genes , 2009, PloS one.

[4]  D. Bartel.  MicroRNAs: Target Recognition and Regulatory Functions , 2009, Cell.

[5]  Martin Reczko,et al.  The database of experimentally supported targets: a functional update of TarBase , 2008, Nucleic Acids Res..

[6]  Q. Cui,et al.  An Analysis of Human MicroRNA and Disease Associations , 2008, PloS one.

[7]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[8]  Catia Pesquita,et al.  Metrics for GO based protein semantic similarity: a systematic evaluation , 2008, BMC Bioinformatics.

[9]  M. Latronico,et al.  Emerging role of microRNAs in cardiovascular biology. , 2007, Circulation research.

[10]  G. Parmigiani,et al.  A multidimensional analysis of genes mutated in breast and colorectal cancers. , 2007, Genome research.

[11]  Dana Ridzon,et al.  Characterization of microRNA expression profiles in normal human tissues , 2007, BMC Genomics.

[12]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[13]  Philip S. Yu,et al.  A new method to measure the semantic similarity of GO terms , 2007, Bioinform..

[14]  R. Sharan,et al.  Network-based prediction of protein function , 2007, Molecular systems biology.

[15]  Eugene Berezikov,et al.  Approaches to microRNA discovery , 2006, Nature Genetics.

[16]  F. Slack,et al.  Oncomirs — microRNAs with a role in cancer , 2006, Nature Reviews Cancer.

[17]  D. Bartel,et al.  Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. , 2005, RNA.

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

[19]  Junguk Hur,et al.  A graph-theoretic modeling on GO space for biological interpretation of gene clusters , 2004, Bioinform..

[20]  Carole A. Goble,et al.  Investigating Semantic Similarity Measures Across the Gene Ontology: The Relationship Between Sequence and Annotation , 2003, Bioinform..

[21]  Lois Oakes,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[22]  A. Ballabio,et al.  MicroRNA target prediction by expression analysis of host genes. , 2009, Genome research.

[23]  Y. Leea,et al.  Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target , 2006 .

[24]  Terrence S. Furey,et al.  The UCSC Table Browser data retrieval tool , 2004, Nucleic Acids Res..

[25]  Sam Griffiths-Jones,et al.  The microRNA Registry , 2004, Nucleic Acids Res..