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Open Access Research article

Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

Grace S Shieh1*, Chung-Ming Chen2, Ching-Yun Yu1, Juiling Huang1, Woei-Fuh Wang2 and Yi-Chen Lo3

Author Affiliations

1 Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan

2 Institute of Biomedical Engineering, National Taiwan University, Taipei, 106, Taiwan

3 Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, 115, Taiwan

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BMC Bioinformatics 2008, 9:134  doi:10.1186/1471-2105-9-134

Published: 3 March 2008

Abstract

Background

With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.

Results

Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.

Conclusion

SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.