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


RESULTS
Here, 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.


CONCLUSIONS
This 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/ .
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, Shanghai, 20031, China. zhenggy@sibs.ac.cn.
Guangyong
G
Zheng
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, Shanghai, 20031, China.|Software Engineering Institute, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China.
Yaochen
Y
Xu
Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.
Xiujun
X
Zhang
Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.
Zhi-Ping
ZP
Liu
College of Life Science and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China.
Zhuo
Z
Wang
Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China. lnchen@sibs.ac.cn.
Luonan
L
Chen
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, Shanghai, 20031, China. zhuxinguang@picb.ac.cn.
Xin-Guang
XG
Zhu
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D053263:Gene Regulatory Networks; D016678:Genome; D006801:Humans; D012984:Software; D059467:Transcriptome
Gene regulatory network; Genome-wide; Parallel computing; Software