ArticleBMC bioinformatics2008
Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- The Wright stuff: reimagining path analysis reveals novel components of the sex determination hierarchy in Drosophila melanogaster.BMC systems biology · 2015Article
- Feature Identification of Compensatory Gene Pairs without Sequence Homology in Yeast.Comparative and functional genomics · 2012Article
- Inferring Genetic Interactions via a Data-Driven Second Order Model.Frontiers in genetics · 2012Article
- Is my network module preserved and reproducible?PLoS computational biology · 2011Article
- Application of structure equation modeling for inferring a serial transcriptional regulation in yeast.Gene regulation and systems biology · 2011Article
- Reverse engineering large-scale genetic networks: synthetic versus real data.Journal of genetics · 2010Article
- Inferring genetic interactions via a nonlinear model and an optimization algorithm.BMC systems biology · 2010Article
- Gene expression profiling in C57BL/6J and A/J mouse inbred strains reveals gene networks specific for brain regions independent of genetic background.BMC genomics · 2010Article
- Maximization of negative correlations in time-course gene expression data for enhancing understanding of molecular pathways.Nucleic acids research · 2010Article
- Uncovering transcriptional interactions via an adaptive fuzzy logic approach.BMC bioinformatics · 2009Article
- Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients.BMC genomics · 2009Article
- Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells.BMC genomics · 2009Article
- Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome.BMC systems biology · 2008Article
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6 authors.
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Abstract
backgroundWith 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.
resultsMotivated 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.
conclusionSSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.
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