MiRDR-OSG: MicroRNA dynamic regulation analysis utilizing open science grid

MicroRNA is a type of short non-coding RNAs, which post-transcriptionally regulate gene expressions. It has been well-documented that human microRNAs contribute in the disease development, such as cancers and obesity. While most microRNA functional studies heavily rely on the regulatory interactions between microRNAs and their target messenger RNAs, the accumulating evidence has shown that the altered availability of microRNAs, their target, and other types of endogenous RNAs competing are able to affect the microRNA-target interactions efficiently, which reflects the dynamic and conditional property of microRNA-mediated gene regulation. Here we present a new computational pipeline, miRDR-OSG, that utilizing the high-throughput computing resource provided by Open Science Grid to study the dynamic regulation of microRNAs in cancer development with the consideration of both competing and cooperative mechanisms. A large-scale genomic dataset from over four thousand patients with 9 major types of cancer was used to demonstrate the usage of miRDR-OSG. As a result, we identified 10,726 microRNA regulatory interactions that only occurred in a specific stage and/or cancer type. This observation demonstrated the dynamic and conditional microRNA regulation during cancer progression. miRDR-OSG is freely available at http://sbbi.unl.edu/miRDR.

[1]  Gargi Dasgupta,et al.  Distributed and Adaptive Execution of Condor DAGMan Workflows , 2010, SEKE.

[2]  B. Melnik.  MiR-21: an environmental driver of malignant melanoma? , 2015, Journal of Translational Medicine.

[3]  R. Giegerich,et al.  Fast and effective prediction of microRNA/target duplexes. , 2004, RNA.

[4]  P. Sharp,et al.  Small RNA Regulators of Gene Expression , 2008, Cell.

[5]  C. Dani,et al.  microRNA miR-27b impairs human adipocyte differentiation and targets PPARgamma. , 2009, Biochemical and biophysical research communications.

[6]  Wei Chen,et al.  Two-Stage Regression Analysis and Biased Estimates in Accounting Research: An Application of the Frisch-Waugh-Lovell Theorem , 2016 .

[7]  A. Näär,et al.  MicroRNAs in metabolism and metabolic disorders , 2012, Nature Reviews Molecular Cell Biology.

[8]  W. Krzyzosiak,et al.  Cooperation meets competition in microRNA-mediated DMPK transcript regulation , 2015, Nucleic acids research.

[9]  A. Schetter,et al.  MiR-21 is an EGFR-regulated anti-apoptotic factor in lung cancer in never-smokers , 2009, Proceedings of the National Academy of Sciences.

[10]  W. Filipowicz,et al.  Regulation of mRNA translation and stability by microRNAs. , 2010, Annual review of biochemistry.

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

[12]  Ana Kozomara,et al.  miRBase: annotating high confidence microRNAs using deep sequencing data , 2013, Nucleic Acids Res..

[13]  J. Pekow,et al.  MicroRNAs in inflammatory bowel disease , 2012, Inflammatory bowel diseases.

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

[15]  Jun S. Liu,et al.  Inference of transcriptional regulation in cancers , 2015, Proceedings of the National Academy of Sciences.

[16]  Nectarios Koziris,et al.  DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association , 2011, Nucleic Acids Res..

[17]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

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

[19]  R. McGregor,et al.  microRNAs in the Regulation of Adipogenesis and Obesity , 2011, Current molecular medicine.

[20]  Richard J Jackson,et al.  How Do MicroRNAs Regulate Gene Expression? , 2007, Science's STKE.

[21]  G. Goodall,et al.  The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1 , 2008, Nature Cell Biology.

[22]  N. Perrone-Bizzozero,et al.  Competing Interactions of RNA-Binding Proteins, MicroRNAs, and Their Targets Control Neuronal Development and Function , 2015, Biomolecules.

[23]  M. Lovell.  A Simple Proof of the FWL Theorem , 2008 .

[24]  D. Tollervey,et al.  Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding , 2013, Cell.

[25]  Hanfei Sun,et al.  Target analysis by integration of transcriptome and ChIP-seq data with BETA , 2013, Nature Protocols.

[26]  J. Cui,et al.  Dynamic and Modularized MicroRNA Regulation and Its Implication in Human Cancers , 2017, Scientific Reports.

[27]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[28]  M. Scheideler,et al.  MicroRNA-30c promotes human adipocyte differentiation and co-represses PAI-1 and ALK2 , 2011, RNA biology.

[29]  Martin Renqiang Min,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

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

[31]  W. Krzyzosiak,et al.  Practical Aspects of microRNA Target Prediction , 2011, Current molecular medicine.

[32]  Hervé Seitz,et al.  Redefining MicroRNA Targets , 2009, Current Biology.

[33]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[34]  William Ritchie,et al.  Integrated miRNA expression analysis and target prediction. , 2012, Methods in molecular biology.

[35]  Paul Avery,et al.  The Open Science Grid , 2007 .

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

[37]  Grace X. Y. Zheng,et al.  Dynamic regulation of miRNA expression in ordered stages of cellular development. , 2007, Genes & development.

[38]  Sijian Wang,et al.  Meta‐analysis based variable selection for gene expression data , 2014, Biometrics.