Inferring the soybean (Glycine max) microRNA functional network based on target gene network

MOTIVATION The rapid accumulation of microRNAs (miRNAs) and experimental evidence for miRNA interactions has ushered in a new area of miRNA research that focuses on network more than individual miRNA interaction, which provides a systematic view of the whole microRNome. So it is a challenge to infer miRNA functional interactions on a system-wide level and further draw a miRNA functional network (miRFN). A few studies have focused on the well-studied human species; however, these methods can neither be extended to other non-model organisms nor take fully into account the information embedded in miRNA-target and target-target interactions. Thus, it is important to develop appropriate methods for inferring the miRNA network of non-model species, such as soybean (Glycine max), without such extensive miRNA-phenotype associated data as miRNA-disease associations in human. RESULTS Here we propose a new method to measure the functional similarity of miRNAs considering both the site accessibility and the interactive context of target genes in functional gene networks. We further construct the miRFNs of soybean, which is the first study on soybean miRNAs on the network level and the core methods can be easily extended to other species. We found that miRFNs of soybean exhibit a scale-free, small world and modular architecture, with their degrees fit best to power-law and exponential distribution. We also showed that miRNA with high degree tends to interact with those of low degree, which reveals the disassortativity and modularity of miRFNs. Our efforts in this study will be useful to further reveal the soybean miRNA-miRNA and miRNA-gene interactive mechanism on a systematic level. AVAILABILITY AND IMPLEMENTATION A web tool for information retrieval and analysis of soybean miRFNs and the relevant target functional gene networks can be accessed at SoymiRNet: http://nclab.hit.edu.cn/SoymiRNet.

[1]  Chunyu Wang,et al.  A novel insight into Gene Ontology semantic similarity. , 2013, Genomics.

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

[3]  Masanori Arita,et al.  Scale-freeness and biological networks. , 2005, Journal of biochemistry.

[4]  Qing-Yu He,et al.  A new method for measuring functional similarity of microRNAs , 2011 .

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

[6]  O. Voinnet Origin, Biogenesis, and Activity of Plant MicroRNAs , 2009, Cell.

[7]  Yun Xiao,et al.  MiRNA–miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features , 2010, Nucleic acids research.

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

[9]  Piers J. Ingram,et al.  Probability models for degree distributions of protein interaction networks , 2005 .

[10]  References , 1971 .

[11]  Chun-Wei Hsu,et al.  Characterization of microRNA‐regulated protein‐protein interaction network , 2008, Proteomics.

[12]  Peter F. Stadler,et al.  Thermodynamics of RNA-RNA Binding , 2006, German Conference on Bioinformatics.

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

[14]  Michael Kertesz,et al.  The role of site accessibility in microRNA target recognition , 2007, Nature Genetics.

[15]  Dong Wang,et al.  Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases , 2010, Bioinform..

[16]  D. Bartel,et al.  MicroRNAS and their regulatory roles in plants. , 2006, Annual review of plant biology.

[17]  Igor Jurisica,et al.  Modeling interactome: scale-free or geometric? , 2004, Bioinform..

[18]  Li-Hsin Chen,et al.  microRNA and aging: A novel modulator in regulating the aging network , 2010, Ageing Research Reviews.

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

[20]  Piero Fariselli,et al.  Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure , 2011, BioData Mining.

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

[22]  Matej Oresic,et al.  Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process , 2007, Bioinform..

[23]  M E J Newman Assortative mixing in networks. , 2002, Physical review letters.

[24]  Jun-ichi Satoh,et al.  Comprehensive analysis of human microRNA target networks , 2011, BioData Mining.

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

[26]  Ruedi Aebersold,et al.  Identification of androgen-coregulated protein networks from the microsomes of human prostate cancer cells , 2003, Genome Biology.

[27]  Anton J. Enright,et al.  MicroRNA targets in Drosophila , 2003, Genome Biology.

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

[29]  Raya Khanin,et al.  How Scale-Free Are Biological Networks , 2006, J. Comput. Biol..

[30]  Stijn van Dongen,et al.  miRBase: microRNA sequences, targets and gene nomenclature , 2005, Nucleic Acids Res..

[31]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

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

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

[34]  Yitzhak Pilpel,et al.  Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network , 2007, PLoS Comput. Biol..