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Journal of Immunology Research
Volume 2015, Article ID 140819, 10 pages
http://dx.doi.org/10.1155/2015/140819
Research Article

MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation

1School of Information, Beijing Forestry University, Beijing 100083, China
2Department of Natural Science in Medicine, Peking University Health Science Center, Beijing 100191, China
3Center for Computational Biology, Beijing Forestry University, Beijing 100083, China

Received 29 May 2015; Accepted 9 November 2015

Academic Editor: Francesco Pappalardo

Copyright © 2015 Luman Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. Barrett, D. B. Troup, S. E. Wilhite et al., “NCBI GEO: archive for high-throughput functional genomic data,” Nucleic Acids Research, vol. 37, no. 1, pp. D885–D890, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Brazma, H. Parkinson, U. Sarkans et al., “ArrayExpress—a public repository for microarray gene expression data at the EBI,” Nucleic Acids Research, vol. 31, no. 1, pp. 68–71, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Al-Qahtani, M. Al-Anazi, A. A. Abdo et al., “Correlation between genetic variations and serum level of interleukin 28B with virus genotypes and disease progression in chronic hepatitis C virus infection,” Journal of Immunology Research, vol. 2015, Article ID 768470, 10 pages, 2015. View at Publisher · View at Google Scholar
  4. N. Nagi-Miura, D. Okuzaki, K. Torigata et al., “CAWS administration increases the expression of interferon γ and complement factors that lead to severe vasculitis in DBA/2 mice,” BMC Immunology, vol. 14, article 44, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. W. C. Yim, Y. Yu, K. Song, C. S. Jang, and B.-M. Lee, “PLANEX: the plant co-expression database,” BMC Plant Biology, vol. 13, 83, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Obayashi, K. Kinoshita, K. Nakai et al., “ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis,” Nucleic Acids Research, vol. 35, no. 1, pp. D863–D869, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Ogata, H. Suzuki, N. Sakurai, and D. Shibata, “CoP: a database for characterizing co-expressed gene modules with biological information in plants,” Bioinformatics, vol. 26, no. 9, pp. 1267–1268, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. Fei, J.-G. Joung, X. Tang et al., “Tomato functional genomics database: a comprehensive resource and analysis package for tomato functional genomics,” Nucleic Acids Research, vol. 39, no. 1, pp. D1156–D1163, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Lescot, P. Déhais, G. Thijs et al., “PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences,” Nucleic Acids Research, vol. 30, no. 1, pp. 325–327, 2002. View at Google Scholar
  10. T. Obayashi, S. Hayashi, M. Shibaoka, M. Saeki, H. Ohta, and K. Kinoshita, “COXPRESdb: a database of coexpressed gene networks in mammals,” Nucleic Acids Research, vol. 36, no. 1, pp. D77–D82, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. S. van Dam, T. Craig, and J. P. de Magalhães, “GeneFriends: a human RNA-seq-based gene and transcript co-expression database,” Nucleic Acids Research, vol. 43, no. 1, pp. D1124–D1132, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. I. Michalopoulos, G. A. Pavlopoulos, A. Malatras et al., “Human gene correlation analysis (HGCA): a tool for the identification of transcriptionally co-expressed genes,” BMC Research Notes, vol. 5, article 265, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. P. Wang, H. Qi, S. Song et al., “ImmuCo: a database of gene co-expression in immune cells,” Nucleic Acids Research, vol. 43, no. 1, pp. D1133–D1139, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. N. Gupta and S. Aggarwal, “MIB: using mutual information for biclustering gene expression data,” Pattern Recognition, vol. 43, no. 8, pp. 2692–2697, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. I. Priness, O. Maimon, and I. Ben-Gal, “Evaluation of gene-expression clustering via mutual information distance measure,” BMC Bioinformatics, vol. 8, article 111, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Steuer, J. Kurths, C. O. Daub, J. Weise, and J. Selbig, “The mutual information: detecting and evaluating dependencies between variables,” Bioinformatics, vol. 18, supplement 2, pp. S231–S240, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. R. C. Gentleman, V. J. Carey, D. M. Bates et al., “Bioconductor: open software development for computational biology and bioinformatics,” Genome Biology, vol. 5, no. 10, article R80, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. P. E. Meyer, F. Lafitte, and G. Bontempi, “minet: A R/bioconductor package for inferring large transcriptional networks using mutual information,” BMC Bioinformatics, vol. 9, article 461, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, “Cluster analysis and display of genome-wide expression patterns,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 25, pp. 14863–14868, 1998. View at Publisher · View at Google Scholar · View at Scopus
  20. A. J. Butte and I. S. Kohane, “Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements,” Pacific Symposium on Biocomputing, vol. 5, pp. 418–429, 2000. View at Google Scholar · View at Scopus
  21. J. Wang, B. Chen, Y. Wang et al., “Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information,” Nucleic Acids Research, vol. 41, no. 8, article e97, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. T. Barrett, S. E. Wilhite, P. Ledoux et al., “NCBI GEO: archive for functional genomics data sets—update,” Nucleic Acids Research, vol. 41, no. 1, pp. D991–D995, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Sean and P. S. Meltzer, “GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor,” Bioinformatics, vol. 23, no. 14, pp. 1846–1847, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. C. L. Wilson and C. J. Miller, “Simpleaffy: a BioConductor package for Affymetrix quality control and data analysis,” Bioinformatics, vol. 21, no. 18, pp. 3683–3685, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. S. D. Pepper, E. K. Saunders, L. E. Edwards, C. L. Wilson, and C. J. Miller, “The utility of MAS5 expression summary and detection call algorithms,” BMC Bioinformatics, vol. 8, article 273, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. L. Gautier, L. Cope, B. M. Bolstad, and R. A. Irizarry, “Affy—analysis of Affymetrix GeneChip data at the probe level,” Bioinformatics, vol. 20, no. 3, pp. 307–315, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948. View at Publisher · View at Google Scholar
  28. A. F. Villaverde, J. Ross, F. Morán, and J. R. Banga, “MIDER: network inference with mutual information distance and entropy reduction,” PLoS ONE, vol. 9, no. 5, Article ID e96732, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. F. M. Giorgi, G. Lopez, J. H. Woo, B. Bisikirska, A. Califano, and M. Bansal, “Inferring protein modulation from gene expression data using conditional mutual information,” PLoS ONE, vol. 9, no. 10, Article ID e109569, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. T. Obayashi and K. Kinoshita, “COXPRESdb: a database to compare gene coexpression in seven model animals,” Nucleic Acids Research, vol. 39, no. 1, pp. D1016–D1022, 2011. View at Publisher · View at Google Scholar · View at Scopus