Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes

CARLA LAYANA; LUIS DIAMBRA

PLOS ONE

PUBLIC LIBRARY SCIENCE

Año: 2011 vol. 6 p. 1 - 10

1932-6203

The microarray technique allows the simultaneous measurements of the expression levels ofthousands of mRNAs. By mining these data one can identify the dynamics of the gene expres-sion time series. The detection of genes that are periodically expressed is an important stepthat allows us to study the regulatory mechanisms associated with the circadian cycle. Theproblem of finding periodicity in biological time series poses many challenges. Such challengeoccurs due to the fact that the observed time series usually exhibit non-idealities, such as noise,short length, outliers and unevenly sampled time points. Consequently, the method for findingperiodicity should preferably be robust against such anomalies in the data. In this paper, wepropose a general and robust procedure for identifying genes with a periodic signature at agiven significance level. This identification method is based on autoregressive models and theinformation theory. By using simulated data we show that the suggested method is capable ofidentifying rhythmic profiles even in the presence of noise and when the number of data pointsis small. By recourse to our analysis, we uncover the circadian rhythmic patterns underlying thegene expression profiles from Cyanobacterium Synechocystis. Many of these genes are relatedto energy metabolism, photosynthesis and respiration.