miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules

BackgroundA microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our knowledge of their complex functions remains limited. Moreover, there is still a lack of miRNA sponge research tools that help researchers to quickly compare their proposed methods with other methods, apply existing methods to new datasets, or select appropriate methods for assisting in subsequent experimental design.ResultsTo fill the gap, we present an R/Bioconductor package, miRspongeR, for simplifying the procedure of identifying and analyzing miRNA sponge interaction networks and modules. It provides seven popular methods and an integrative method to identify miRNA sponge interactions. Moreover, it supports the validation of miRNA sponge interactions and the identification of miRNA sponge modules, as well as functional enrichment and survival analysis of miRNA sponge modules.ConclusionsThis package enables researchers to quickly evaluate their new methods, apply existing methods to new datasets, and consequently speed up miRNA sponge research.

[1]  Ales Hampl,et al.  miRNAsong: a web-based tool for generation and testing of miRNA sponge constructs in silico , 2016, Scientific Reports.

[2]  Salvatore Alaimo,et al.  A novel computational method for inferring competing endogenous interactions , 2016, Briefings Bioinform..

[3]  Mariano J. Alvarez,et al.  Genome-wide Identification of Post-translational Modulators of Transcription Factor Activity in Human B-Cells , 2009, Nature Biotechnology.

[4]  Lorenzo Farina,et al.  Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer , 2014, BMC Systems Biology.

[5]  Beth Israel,et al.  Decision letter: Replication Study: A coding-independent function of gene and pseudogene mRNAs regulates tumour biology , 2010 .

[6]  D. Cacchiarelli,et al.  A Long Noncoding RNA Controls Muscle Differentiation by Functioning as a Competing Endogenous RNA , 2011, Cell.

[7]  Juan Liu,et al.  Construction and investigation of breast-cancer-specific ceRNA network based on the mRNA and miRNA expression data. , 2014, IET systems biology.

[8]  V. Ambros The functions of animal microRNAs , 2004, Nature.

[9]  Jiang Yu,et al.  The epithelial-to-mesenchymal transition activator ZEB1 initiates a prometastatic competing endogenous RNA network , 2018, The Journal of clinical investigation.

[10]  Hsien-Da Huang,et al.  miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions , 2017, Nucleic Acids Res..

[11]  Peng Wang,et al.  miRSponge: a manually curated database for experimentally supported miRNA sponges and ceRNAs , 2015, Database J. Biol. Databases Curation.

[12]  J. Kjems,et al.  Natural RNA circles function as efficient microRNA sponges , 2013, Nature.

[13]  P. Pandolfi,et al.  The multilayered complexity of ceRNA crosstalk and competition , 2014, Nature.

[14]  K. Pearson NOTES ON THE HISTORY OF CORRELATION , 1920 .

[15]  Junpeng Zhang,et al.  Identifying miRNA sponge modules using biclustering and regulatory scores , 2017, BMC Bioinformatics.

[16]  H. Dweep,et al.  miRWalk2.0: a comprehensive atlas of microRNA-target interactions , 2015, Nature Methods.

[17]  Tingting Shao,et al.  The mRNA related ceRNA–ceRNA landscape and significance across 20 major cancer types , 2015, Nucleic acids research.

[18]  Anjali J. Koppal,et al.  Supplementary data: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites , 2010 .

[19]  Junpeng Zhang,et al.  Computational methods for identifying miRNA sponge interactions , 2016, Briefings Bioinform..

[20]  Pavel Tomancak,et al.  linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type , 2011, Bioinform..

[21]  R. Gregory,et al.  MicroRNA biogenesis pathways in cancer , 2015, Nature Reviews Cancer.

[22]  Martin Reczko,et al.  DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows , 2013, Nucleic Acids Res..

[23]  Cong Pian,et al.  LncCeRBase: a database of experimentally validated human competing endogenous long non-coding RNAs , 2019, Database : the journal of biological databases and curation.

[24]  P. Pandolfi,et al.  A ceRNA Hypothesis: The Rosetta Stone of a Hidden RNA Language? , 2011, Cell.

[25]  Yin Tong,et al.  miRNACancerMAP: an integrative web server inferring miRNA regulation network for cancer , 2018, Bioinform..

[26]  Hui Zhou,et al.  starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data , 2013, Nucleic Acids Res..

[27]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.

[28]  Anton J. Enright,et al.  spongeScan: A web for detecting microRNA binding elements in lncRNA sequences , 2016, Nucleic Acids Res..

[29]  Dawood B. Dudekula,et al.  CircInteractome: A web tool for exploring circular RNAs and their interacting proteins and microRNAs , 2016, RNA biology.

[30]  Ferdinando Di Cunto,et al.  Coding-Independent Regulation of the Tumor Suppressor PTEN by Competing Endogenous mRNAs , 2011, Cell.

[31]  Artemis G. Hatzigeorgiou,et al.  DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions , 2017, Nucleic Acids Res..

[32]  D. Bartel,et al.  Predicting effective microRNA target sites in mammalian mRNAs , 2015, eLife.

[33]  J. Mendell,et al.  MicroRNAs in cell proliferation, cell death, and tumorigenesis. , 2007, British journal of cancer.

[34]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Xuerui Yang,et al.  An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma , 2011, Cell.

[36]  Alexander Lex,et al.  UpSetR: An R Package for the Visualization of Intersecting Sets and their Properties , 2017 .

[37]  Sebastian D. Mackowiak,et al.  Circular RNAs are a large class of animal RNAs with regulatory potency , 2013, Nature.

[38]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[39]  H. Seitz,et al.  microRNA target prediction programs predict many false positives , 2017, Genome research.

[40]  Marcel H. Schulz,et al.  JAMI: fast computation of conditional mutual information for ceRNA network analysis , 2018, Bioinform..

[41]  Anton J. Enright,et al.  An efficient algorithm for large-scale detection of protein families. , 2002, Nucleic acids research.

[42]  Yunpeng Zhang,et al.  LncACTdb 2.0: an updated database of experimentally supported ceRNA interactions curated from low- and high-throughput experiments , 2018, Nucleic Acids Res..

[43]  Subbaya Subramanian,et al.  Competing endogenous RNA database , 2012, Bioinformation.