Discovery of microRNA Regulatory Networks by Integrating Multidimensional High-Throughput Data

被引:4
|
作者
Yang, Jian-Hua [1 ]
Qu, Liang-Hu [1 ,2 ]
机构
[1] Sun Yat Sen Univ, State Key Lab Biocontrol, Minist Educ, RNA Informat Ctr,Key Lab Gene Engn, Guangzhou 510275, Guangdong, Peoples R China
[2] Sun Yat Sen Univ, Biotechnol Res Ctr, Guangzhou 510275, Guangdong, Peoples R China
关键词
microRNAs; Ago; CLIP-Seq; starBase; RNA-binding proteins; miRNA-target interactions; Cancer-associated miRNAs; Degradome-Seq; Post-transcriptional regulation; deepView; RNA-BINDING PROTEIN; WIDE ANALYSIS; IDENTIFICATION; TARGETS; SITES; GENE; ALIGNMENT; LET-7; SIRNA;
D O I
10.1007/978-94-007-5590-1_13
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
MicroRNAs (miRNAs) are endogenous non-coding RNAs (ncRNAs) of approximately 22 nt that regulate the expression of a large fraction of genes by targeting messenger RNAs (mRNAs). However, determining the biologically significant targets of miRNAs is an ongoing challenge. In this chapter, we describe how to identify miRNA-target interactions and miRNA regulatory networks from high-throughput deep sequencing, CLIP-Seq (HITS-CLIP, PAR-CLIP) and degradome sequencing data using starBase platforms. In starBase, several web-based and stand-alone computational tools were developed to discover Argonaute (Ago) binding and cleavage sites, miRNA-target interactions, perform enrichment analysis of miRNA target genes in Gene Ontology (GO) categories and biological pathways, and identify combinatorial effects between Ago and other RNA-binding proteins (RBPs). Investigating target pathways of miRNAs in human CLIP-Seq data, we found that many cancer-associated miRNAs modulate cancer pathways. Performing an enrichment analysis of genes targeted by highly expressed miRNAs in the mouse brain showed that many miRNAs are involved in cancer-associated MAPK signaling and glioma pathways, as well as neuron-associated neurotrophin signaling and axon guidance pathways. Moreover, thousands of combinatorial binding sites between Ago and RBPs were identified from CLIP-Seq data suggesting RBPs and miRNAs coordinately regulate mRNA transcripts. As a means of comprehensively integrating CLIP-Seq and Degradome-Seq data, the starBase platform is expected to identify clinically relevant miRNA-target regulatory relationships, and reveal multi-dimensional post-transcriptional regulatory networks involving miRNAs and RBPs. starBase is available at http://starbase.sysu.edu.cn/
引用
收藏
页码:251 / 266
页数:16
相关论文
共 50 条
  • [1] Precision multidimensional assay for high-throughput microRNA drug discovery
    Haefliger, Benjamin
    Prochazka, Laura
    Angelici, Bartolomeo
    Benenson, Yaakov
    [J]. NATURE COMMUNICATIONS, 2016, 7
  • [2] Precision multidimensional assay for high-throughput microRNA drug discovery
    Benjamin Haefliger
    Laura Prochazka
    Bartolomeo Angelici
    Yaakov Benenson
    [J]. Nature Communications, 7
  • [3] Integrating high-throughput and computational data elucidates bacterial networks
    Covert, MW
    Knight, EM
    Reed, JL
    Herrgard, MJ
    Palsson, BO
    [J]. NATURE, 2004, 429 (6987) : 92 - 96
  • [4] Integrating high-throughput and computational data elucidates bacterial networks
    Markus W. Covert
    Eric M. Knight
    Jennifer L. Reed
    Markus J. Herrgard
    Bernhard O. Palsson
    [J]. Nature, 2004, 429 : 92 - 96
  • [5] Inferring transcriptional regulatory networks from high-throughput data
    Wang, Rui-Sheng
    Wang, Yong
    Zhang, Xiang-Sun
    Chen, Luonan
    [J]. BIOINFORMATICS, 2007, 23 (22) : 3056 - 3064
  • [6] High-throughput methods of regulatory element discovery
    Hudson, Michael E.
    Snyder, Michael
    [J]. BIOTECHNIQUES, 2006, 41 (06) : 673 - +
  • [7] High-throughput validation of ceRNA regulatory networks
    Hua-Sheng Chiu
    María Rodríguez Martínez
    Mukesh Bansal
    Aravind Subramanian
    Todd R. Golub
    Xuerui Yang
    Pavel Sumazin
    Andrea Califano
    [J]. BMC Genomics, 18
  • [8] High-throughput validation of ceRNA regulatory networks
    Chiu, Hua-Sheng
    Martinez, Maria Rodriguez
    Bansal, Mukesh
    Subramanian, Aravind
    Golub, Todd R.
    Yang, Xuerui
    Sumazin, Pavel
    Califano, Andrea
    [J]. BMC GENOMICS, 2017, 18
  • [9] A Simple, Multidimensional Approach to High-Throughput Discovery of Catalytic Reactions
    Robbins, Daniel W.
    Hartwig, John F.
    [J]. SCIENCE, 2011, 333 (6048) : 1423 - 1427
  • [10] ProteoMirExpress: Inferring MicroRNA and Protein-centered Regulatory Networks from High-throughput Proteomic and mRNA Expression Data
    Qin, Jing
    Li, Mulin Jun
    Wang, Panwen
    Wong, Nai Sum
    Wong, Maria P.
    Xia, Zhengyuan
    Tsao, George S. W.
    Zhang, Michael Q.
    Wang, Junwen
    [J]. MOLECULAR & CELLULAR PROTEOMICS, 2013, 12 (11) : 3379 - 3387