The abundance of an mRNA species depends not only on the transcription rate at which it is produced,but also on its decay rate,which determines how quickly it is degraded. Both transcription rate and decay rate are important factors in regulating gene expression.With the advance of the age of genomics,there are a considerable number of gene expression datasets,in which the expression profiles of tens of thousands of genes are often non-uniformly sampled. Recently,numerous studies have proposed to infer the regulatory networks from expression profiles.Nevertheless, how mRNA decay rates affect the computational prediction of transcription rate profiles from expression profiles has not been well studied.To understand the influences,we present a systematic method based on a gene dynamic regulation model by taking mRNA decay rates,expression profiles and transcription profiles into account.Generally speaking,an expression profile can be regarded as a representation of a biological condition.The rationale behind the concept is that the biological condition is reflected in the changing of gene expression profile.Basically,the biological condition is either associated to the cell cycle or associated to the environmental stresses.The expression profiles of genes that belong to the former,so-called cell cycle data,are characterized by periodicity,whereas the expression profiles of genes that belong to the latter,so-called condition-specific data,are characterized by a steep change after a specific time without periodicity.In this paper,we examine the systematic method on the simulated expression data as well as the real expression data including yeast cell cycle data and condition-specific data (glucose-limitation data).The results indicate that mRNA decay rates do not significantly influence the computational prediction of transcription-rate profiles for cell cycle data.On the contrary,the magnitudes and shapes of transcription-rate profiles for condition specific data are significantly affected by mRNA decay rates.This analysis provides an opportunity for researchers to conduct future research on inferring regulatory networks computationally with available expression profiles under different biological conditions.
|Evidence ID||Analyze ID||Interactor||Interactor Systematic Name||Interactor||Interactor Systematic Name||Type||Assay||Annotation||Action||Modification||Phenotype||Source||Reference||Note|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Gene Ontology Term||Gene Ontology Term ID||Qualifier||Aspect||Method||Evidence||Source||Assigned On||Reference||Annotation Extension|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Phenotype||Experiment Type||Experiment Type Category||Mutant Information||Strain Background||Chemical||Details||Reference|
|Evidence ID||Analyze ID||Regulator||Regulator Systematic Name||Target||Target Systematic Name||Experiment||Conditions||Strain||Source||Reference|