publication . Other literature type . Article . 2018

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.

null null; Thiago Dominguez Crespo Hirata; Helder I Nakaya; Vinicius Maracaja-Coutinho; Joao Santana Silva;
Open Access
  • Published: 20 Feb 2018
  • Publisher: Springer Nature
Abstract
Background The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. Results In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, ou...
Subjects
free text keywords: Co-expression modules, Gene networks, Modular analysis, Leishmaniasis, Transcriptomics, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5, Software, Biochemistry, Applied Mathematics, Molecular Biology, Structural Biology, Computer Science Applications
Communities
Science and Innovation Policy Studies
37 references, page 1 of 3

Oldham, MC, Horvath, S, Geschwind, DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci U S A. 2006; 103: 17973-17978 [OpenAIRE] [PubMed] [DOI]

Liu, J, Jing, L, Tu, X. Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease. BMC Cardiovasc Disord. 2016; 16: 54 [OpenAIRE] [PubMed] [DOI]

Xue, J, Schmidt, SV, Sander, J, Draffehn, A, Krebs, W, Quester, I. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity. 2014; 40: 274-288 [OpenAIRE] [PubMed] [DOI]

Barabási, A-L, Oltvai, ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004; 5: 101-113 [OpenAIRE] [PubMed] [DOI]

5.Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17. 10.2202/1544-6115.1128.

Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102: 15545-15550 [OpenAIRE] [PubMed] [DOI]

Langfelder, P, Zhang, B, Horvath, S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics. 2008; 24: 719-720 [OpenAIRE] [PubMed] [DOI]

Yu, G, Wang, L-G, Han, Y, He, Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16: 284-287 [OpenAIRE] [PubMed] [DOI]

9.Sergushichev A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. BioRxiv. 2016. 10.1101/060012.

Petereit, J, Smith, S, Harris, FC, Schlauch, KA. Petal: co-expression network modelling in R. BMC Syst Biol. 2016; 10 (Suppl 2): 51 [OpenAIRE] [PubMed] [DOI]

Ogata, Y, Suzuki, H, Sakurai, N, Shibata, D. CoP: a database for characterizing co-expressed gene modules with biological information in plants. Bioinformatics. 2010; 26: 1267-1268 [OpenAIRE] [PubMed] [DOI]

Desai, AP, Razeghin, M, Meruvia-Pastor, O, Peña-Castillo, L. GeNET: a web application to explore and share gene co-expression network analysis data. Peer J. 2017; 5: e3678 [OpenAIRE] [PubMed] [DOI]

Tesson, BM, Breitling, R, Jansen, RC. DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics. 2010; 11: 497 [OpenAIRE] [PubMed] [DOI]

Watson, M. CoXpress: differential co-expression in gene expression data. BMC Bioinformatics. 2006; 7: 509 [OpenAIRE] [PubMed] [DOI]

Chiu, DS, Talhouk, A. diceR: an R package for class discovery using an ensemble driven approach. BMC Bioinformatics. 2018; 19: 11 [OpenAIRE] [PubMed] [DOI]

37 references, page 1 of 3
Abstract
Background The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. Results In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, ou...
Subjects
free text keywords: Co-expression modules, Gene networks, Modular analysis, Leishmaniasis, Transcriptomics, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5, Software, Biochemistry, Applied Mathematics, Molecular Biology, Structural Biology, Computer Science Applications
Communities
Science and Innovation Policy Studies
37 references, page 1 of 3

Oldham, MC, Horvath, S, Geschwind, DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci U S A. 2006; 103: 17973-17978 [OpenAIRE] [PubMed] [DOI]

Liu, J, Jing, L, Tu, X. Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease. BMC Cardiovasc Disord. 2016; 16: 54 [OpenAIRE] [PubMed] [DOI]

Xue, J, Schmidt, SV, Sander, J, Draffehn, A, Krebs, W, Quester, I. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity. 2014; 40: 274-288 [OpenAIRE] [PubMed] [DOI]

Barabási, A-L, Oltvai, ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004; 5: 101-113 [OpenAIRE] [PubMed] [DOI]

5.Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17. 10.2202/1544-6115.1128.

Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102: 15545-15550 [OpenAIRE] [PubMed] [DOI]

Langfelder, P, Zhang, B, Horvath, S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics. 2008; 24: 719-720 [OpenAIRE] [PubMed] [DOI]

Yu, G, Wang, L-G, Han, Y, He, Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16: 284-287 [OpenAIRE] [PubMed] [DOI]

9.Sergushichev A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. BioRxiv. 2016. 10.1101/060012.

Petereit, J, Smith, S, Harris, FC, Schlauch, KA. Petal: co-expression network modelling in R. BMC Syst Biol. 2016; 10 (Suppl 2): 51 [OpenAIRE] [PubMed] [DOI]

Ogata, Y, Suzuki, H, Sakurai, N, Shibata, D. CoP: a database for characterizing co-expressed gene modules with biological information in plants. Bioinformatics. 2010; 26: 1267-1268 [OpenAIRE] [PubMed] [DOI]

Desai, AP, Razeghin, M, Meruvia-Pastor, O, Peña-Castillo, L. GeNET: a web application to explore and share gene co-expression network analysis data. Peer J. 2017; 5: e3678 [OpenAIRE] [PubMed] [DOI]

Tesson, BM, Breitling, R, Jansen, RC. DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics. 2010; 11: 497 [OpenAIRE] [PubMed] [DOI]

Watson, M. CoXpress: differential co-expression in gene expression data. BMC Bioinformatics. 2006; 7: 509 [OpenAIRE] [PubMed] [DOI]

Chiu, DS, Talhouk, A. diceR: an R package for class discovery using an ensemble driven approach. BMC Bioinformatics. 2018; 19: 11 [OpenAIRE] [PubMed] [DOI]

37 references, page 1 of 3
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue