CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data

BMC Bioinformatics

Research

Guangyong Zheng¹  Xin-Guang Zhu¹  Yaochen Xu²  Zhuo Wang³  Zhi-Ping Liu⁴  Xiujun Zhang⁴  Luonan Chen⁴ 

[1] CAS Key Laboratory of Computational Biology and State Key Laboratory of Hybrid Rice, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, 320 Yueyang Road, 20031, Shanghai, China;CAS Key Laboratory of Computational Biology and State Key Laboratory of Hybrid Rice, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, 320 Yueyang Road, 20031, Shanghai, China;Software Engineering Institute, East China Normal University, 3663 North Zhongshan Road, 200062, Shanghai, China;College of Life Science and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, 200240, Shanghai, China;Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, China;

关键词: Gene regulatory network;    Genome-wide;    Parallel computing;    Software;   

DOI  :  10.1186/s12859-016-1324-y

来源: Springer

【 摘 要 】

BackgroundA gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale.ResultsHere, we present a software package for gene regulatory network reconstruction at a genomic level, in which gene interaction is measured by the conditional mutual information measurement using a parallel computing framework (so the package is named CMIP). The package is a greatly improved implementation of our previous PCA-CMI algorithm. In CMIP, we provide not only an automatic threshold determination method but also an effective parallel computing framework for network inference. Performance tests on benchmark datasets show that the accuracy of CMIP is comparable to most current network inference methods. Moreover, running tests on synthetic datasets demonstrate that CMIP can handle large datasets especially genome-wide datasets within an acceptable time period. In addition, successful application on a real genomic dataset confirms its practical applicability of the package.ConclusionsThis new software package provides a powerful tool for genomic network reconstruction to biological community. The software can be accessed at http://www.picb.ac.cn/CMIP/.

【 授权许可】

CC BY   
© The Author(s). 2016