ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context

Identifying functionally significant microRNAs (miRs) and their correspondingly most important messenger RNA targets (mRNAs) in specific biological contexts is a critical task to improve our understanding of molecular mechanisms underlying organismal development, physiology and disease. However, current miR–mRNA target prediction platforms rank miR targets based on estimated strength of physical interactions and lack the ability to rank interactants as a function of their potential to impact a given biological system. To address this, we have developed ToppMiR (http://toppmir.cchmc.org), a web-based analytical workbench that allows miRs and mRNAs to be co-analyzed via biologically centered approaches in which gene function associated annotations are used to train a machine learning-based analysis engine. ToppMiR learns about biological contexts based on gene associated information from expression data or from a user-specified set of genes that relate to context-relevant knowledge or hypotheses. Within the biological framework established by the genes in the training set, its associated information content is then used to calculate a features association matrix composed of biological functions, protein interactions and other features. This scoring matrix is then used to jointly rank both the test/candidate miRs and mRNAs. Results of these analyses are provided as downloadable tables or network file formats usable in Cytoscape.

[1]  Filip Pattyn,et al.  The microRNA body map: dissecting microRNA function through integrative genomics , 2011, Nucleic acids research.

[2]  Chi-Ying F. Huang,et al.  miRTarBase: a database curates experimentally validated microRNA–target interactions , 2010, Nucleic Acids Res..

[3]  Kristin C. Gunsalus,et al.  microRNA Target Predictions across Seven Drosophila Species and Comparison to Mammalian Targets , 2005, PLoS Comput. Biol..

[4]  Yadong Wang,et al.  Prioritization of disease microRNAs through a human phenome-microRNAome network , 2010, BMC Systems Biology.

[5]  M. Selbach,et al.  Global analysis of cellular protein translation by pulsed SILAC , 2009, Proteomics.

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

[7]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

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

[9]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[10]  Colin N. Dewey,et al.  A Genome-Wide Map of Conserved MicroRNA Targets in C. elegans , 2006, Current Biology.

[11]  Sanghyuk Lee,et al.  miRGator v2.0 : an integrated system for functional investigation of microRNAs , 2010, Nucleic Acids Res..

[12]  Gabriele Sales,et al.  MAGIA2: from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update) , 2012, Nucleic Acids Res..

[13]  Christos Faloutsos,et al.  Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003 , 2003, KDD.

[14]  Shlomo Moran,et al.  SALSA: the stochastic approach for link-structure analysis , 2001, TOIS.

[15]  E. Cantoni.  Analysis of Robust Quasi-deviances for Generalized Linear Models , 2004 .

[16]  Padhraic Smyth,et al.  Algorithms for estimating relative importance in networks , 2003, KDD '03.

[17]  Jing Chen,et al.  ToppGene Suite for gene list enrichment analysis and candidate gene prioritization , 2009, Nucleic Acids Res..

[18]  C. Burge,et al.  Most mammalian mRNAs are conserved targets of microRNAs. , 2008, Genome research.

[19]  C. Ramírez,et al.  miR-106b impairs cholesterol efflux and increases Aβ levels by repressing ABCA1 expression , 2012, Experimental Neurology.

[20]  A. Hatzigeorgiou,et al.  A guide through present computational approaches for the identification of mammalian microRNA targets , 2006, Nature Methods.

[21]  D. Bartel,et al.  Weak Seed-Pairing Stability and High Target-Site Abundance Decrease the Proficiency of lsy-6 and Other miRNAs , 2011, Nature Structural &Molecular Biology.

[22]  Curtis Balch,et al.  MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression , 2009, Nucleic Acids Res..

[23]  Xianghuo He,et al.  MicroRNA-409 suppresses tumour cell invasion and metastasis by directly targeting radixin in gastric cancers , 2012, Oncogene.

[24]  Tatiana A. Tatusova,et al.  Entrez Gene: gene-centered information at NCBI , 2004, Nucleic Acids Res..

[25]  N. Rajewsky,et al.  Natural selection on human microRNA binding sites inferred from SNP data , 2006, Nature Genetics.

[26]  Kohei Miyazono,et al.  Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis , 2012, Nucleic acids research.

[27]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

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

[29]  Chaoqian Xu,et al.  The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2 , 2011, Nature Medicine.

[30]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[31]  Andrew D. Reynolds,et al.  miR-200c regulates FGFR-dependent epithelial proliferation via Vldlr during submandibular gland branching morphogenesis , 2012, Development.

[32]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.

[33]  Doron Betel,et al.  The microRNA.org resource: targets and expression , 2007, Nucleic Acids Res..

[34]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[35]  G. Hong,et al.  Nucleic Acids Research , 2015, Nucleic Acids Research.

[36]  Anjali J. Koppal,et al.  Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites , 2010, Genome Biology.

[37]  Tongbin Li,et al.  miRecords: an integrated resource for microRNA–target interactions , 2008, Nucleic Acids Res..

[38]  Jing Chen,et al.  Improved human disease candidate gene prioritization using mouse phenotype , 2007, BMC Bioinformatics.

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

[40]  N. Rajewsky,et al.  Widespread changes in protein synthesis induced by microRNAs , 2008, Nature.

[41]  K Shimizu,et al.  BodyMap: a collection of 3' ESTs for analysis of human gene expression information. , 2000, Genome research.

[42]  Shinichi Morishita,et al.  BodyMap incorporated PCR-based expression profiling data and a gene ranking system , 2001, Nucleic Acids Res..

[43]  Jan Koster,et al.  Discovery and visualization of miRNA–mRNA functional modules within integrated data using bicluster analysis , 2013, Nucleic acids research.

[44]  Ruijie Zhang,et al.  Functional combination strategy for prioritization of human miRNA target. , 2014, Gene.

[45]  Padhraic Smyth,et al.  Analysis and Visualization of Network Data using JUNG , 2005 .

[46]  Amarendra S. Yavatkar,et al.  StemCellDB: the human pluripotent stem cell database at the National Institutes of Health. , 2013, Stem cell research.