TY - JOUR
T1 - Influence of mRNA decay rates on the computational prediction of transcription rate profiles from gene expression profiles
AU - Chin, Chi Fang
AU - Shih, Arthur Chun Chieh
AU - Fan, Kuo Chin
PY - 2007/12
Y1 - 2007/12
N2 - 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.
AB - 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.
KW - Cell cycle data
KW - Condition-specific data
KW - Gene expression profile
KW - Glucose-limitation data
KW - Transcription-rate profile
KW - mRNA decay rate
UR - http://www.scopus.com/inward/record.url?scp=37449017225&partnerID=8YFLogxK
U2 - 10.1007/s12038-007-0134-9
DO - 10.1007/s12038-007-0134-9
M3 - 期刊論文
C2 - 18202449
AN - SCOPUS:37449017225
SN - 0250-5991
VL - 32
SP - 1251
EP - 1262
JO - Journal of Biosciences
JF - Journal of Biosciences
IS - 3
ER -