MIPRIP: The Mixed Integer linear Programming based Regulatory Interaction Predictor
MIPRIP is a software package for R (www.r-project.org) to predict regulators of a gene of interest from gene expression profiles of the samples under study and known regulator binding information (from e.g. ChIP-seq/ChIP-chip databases). It is developed to study the specific regulation of the gene of interest in one group of samples compared to a control group. MIPRIP can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. two different phenotypes, disease/healthy controls or treatment/controls.
Best, you follow the small tutorial in the short manual (Downloads) which explains how to use the method along our case study. In the case study, regulators of telomerase genes of Saccharomyces cerevisiae are predicted, and knockout strains of short telomere length and controls (normal telomere length) are used as the two groups of samples. We used (z-transformed) expression data of 269 yeast deletion strains (data is taken from the study by Reimand et al. (1)), regulator binding information (for nearly all known yeast transcription factors, mostly taken from YEASTRACT database (www.yeastract.com)), and the telomere length class labels were inferred Askree et al. (2004), Gatbonton et al. (2006), Ungar et al. (2009), Shachar et al. (2008) and Ben-Shitrit et al. (2012) (2-6), details can be read in Poos et al. (7).
Citing MIPRIP: please cite Poos et al. (2016) Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast, Nucleic Acids Research,Feb 22. pii: gkw111. [Epub ahead of print]
MIPRIP version 1.1:
Data for the case study in the tutorial:
- Edge strength Matrix for S. cerevisiae
- Z-transformed gene expression data (269 samples, data from Reimand et al. (1))
- Activity matrix for the regulators calculated from the gene expression data above
- Phenotypic class labels (telomere length of the 269 deletion strains, taken from Askree et al. (2004), Gatbonton et al. (2006), Ungar et al. (2009), Shachar et al. (2008) and Ben-Shitrit et al. (2012) (2-6))
- Reimand, J., Vaquerizas, J.M., Todd, A.E., Vilo, J. and Luscombe, N.M. (2010) Comprehensive reanalysis of transcription factor knockout expression data in Saccharomyces cerevisiae reveals many new targets. Nucleic acids research, 38, 4768-4777.
- Askree, S.H., Yehuda, T., Smolikov, S., Gurevich, R., Hawk, J., Coker, C., Krauskopf, A., Kupiec, M. and McEachern, M.J. (2004) A genome-wide screen for Saccharomyces cerevisiae deletion mutants that affect telomere length. Proceedings of the National Academy of Sciences of the United States of America, 101, 8658-8663.
- Ben-Shitrit, T., Yosef, N., Shemesh, K., Sharan, R., Ruppin, E. and Kupiec, M. (2012) Systematic identification of gene annotation errors in the widely used yeast mutation collections. Nature methods, 9, 373-378.
- Gatbonton, T., Imbesi, M., Nelson, M., Akey, J.M., Ruderfer, D.M., Kruglyak, L., Simon, J.A. and Bedalov, A. (2006) Telomere length as a quantitative trait: genome-wide survey and genetic mapping of telomere length-control genes in yeast. PLoS Genet, 2, e35.
- Shachar, R., Ungar, L., Kupiec, M., Ruppin, E. and Sharan, R. (2008) A systems-level approach to mapping the telomere length maintenance gene circuitry. Molecular systems biology, 4, 172.
- Ungar, L., Yosef, N., Sela, Y., Sharan, R., Ruppin, E. and Kupiec, M. (2009) A genome-wide screen for essential yeast genes that affect telomere length maintenance.Nucleic acids research,37, 3840-3849.
- Poos, A., Maicher, A., Dieckmann, A., Oswald, M., Eils, R., Kupiec, M., Luke, B. and König, R. (2016) Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast. Nucleic acids research, Feb 22. pii: gkw111. [Epub ahead of print]