Literature DB >> 33822666

miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data.

Junpeng Zhang1,2, Lin Liu3, Taosheng Xu4, Wu Zhang5, Chunwen Zhao2, Sijing Li2, Jiuyong Li3, Nini Rao1, Thuc Duy Le3.   

Abstract

In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.

Entities:  

Keywords:  ceRNA; lncRNA; miRNA; miRNA sponge modules; modular analysis

Mesh:

Substances:

Year:  2021        PMID: 33822666      PMCID: PMC8632112          DOI: 10.1080/15476286.2021.1905341

Source DB:  PubMed          Journal:  RNA Biol        ISSN: 1547-6286            Impact factor:   4.652


  50 in total

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Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

Review 2.  The functions of animal microRNAs.

Authors:  Victor Ambros
Journal:  Nature       Date:  2004-09-16       Impact factor: 49.962

3.  Sparse group factor analysis for biclustering of multiple data sources.

Authors:  Kerstin Bunte; Eemeli Leppäaho; Inka Saarinen; Samuel Kaski
Journal:  Bioinformatics       Date:  2016-04-19       Impact factor: 6.937

4.  miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions.

Authors:  Chih-Hung Chou; Sirjana Shrestha; Chi-Dung Yang; Nai-Wen Chang; Yu-Ling Lin; Kuang-Wen Liao; Wei-Chi Huang; Ting-Hsuan Sun; Siang-Jyun Tu; Wei-Hsiang Lee; Men-Yee Chiew; Chun-San Tai; Ting-Yen Wei; Tzi-Ren Tsai; Hsin-Tzu Huang; Chung-Yu Wang; Hsin-Yi Wu; Shu-Yi Ho; Pin-Rong Chen; Cheng-Hsun Chuang; Pei-Jung Hsieh; Yi-Shin Wu; Wen-Liang Chen; Meng-Ju Li; Yu-Chun Wu; Xin-Yi Huang; Fung Ling Ng; Waradee Buddhakosai; Pei-Chun Huang; Kuan-Chun Lan; Chia-Yen Huang; Shun-Long Weng; Yeong-Nan Cheng; Chao Liang; Wen-Lian Hsu; Hsien-Da Huang
Journal:  Nucleic Acids Res       Date:  2018-01-04       Impact factor: 16.971

5.  limma powers differential expression analyses for RNA-sequencing and microarray studies.

Authors:  Matthew E Ritchie; Belinda Phipson; Di Wu; Yifang Hu; Charity W Law; Wei Shi; Gordon K Smyth
Journal:  Nucleic Acids Res       Date:  2015-01-20       Impact factor: 16.971

Review 6.  The multilayered complexity of ceRNA crosstalk and competition.

Authors:  Yvonne Tay; John Rinn; Pier Paolo Pandolfi
Journal:  Nature       Date:  2014-01-16       Impact factor: 49.962

7.  MNDR v2.0: an updated resource of ncRNA-disease associations in mammals.

Authors:  Tianyu Cui; Lin Zhang; Yan Huang; Ying Yi; Puwen Tan; Yue Zhao; Yongfei Hu; Liyan Xu; Enmin Li; Dong Wang
Journal:  Nucleic Acids Res       Date:  2018-01-04       Impact factor: 16.971

8.  A coding-independent function of gene and pseudogene mRNAs regulates tumour biology.

Authors:  Laura Poliseno; Leonardo Salmena; Jiangwen Zhang; Brett Carver; William J Haveman; Pier Paolo Pandolfi
Journal:  Nature       Date:  2010-06-24       Impact factor: 49.962

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

Authors:  Junpeng Zhang; Lin Liu; Taosheng Xu; Yong Xie; Chunwen Zhao; Jiuyong Li; Thuc Duy Le
Journal:  BMC Bioinformatics       Date:  2019-05-10       Impact factor: 3.169

10.  Large-scale inference of competing endogenous RNA networks with sparse partial correlation.

Authors:  Markus List; Azim Dehghani Amirabad; Dennis Kostka; Marcel H Schulz
Journal:  Bioinformatics       Date:  2019-07-15       Impact factor: 6.937

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