Significance analysis of time-series transcriptomic data: a methodology that enables the identification and further exploration of the differentially expressed genes at each time-point.
Time-series transcriptional profiling experiments are becoming increasingly popular, in light of the abundance of information regarding a biological system's regulation that they are expected to reveal. However, identification of differentially expressed genes as a function of time and comparison between physiological states based on the genes' variability in significance level over time remain intriguing tasks, due to certain limitations in the currently available algorithms. Based on the principles of significance analysis of microarrays (SAM) method, we developed an algorithm that allows for the identification of the differentially expressed genes at each time-point of a time sequence, using a common reference distribution and significance threshold for all time-points. These results are further explored in a systematic way to extract information about (a) individual gene and gene class variability in significance level with time, (b) gene and time-point correlation based on (a), and (c) gene class comparison based on (a). All algorithms have been programmed in C language in the form of four executable files for both Windows and Macintosh platforms under the overall name MiTimeS. MiTimeS was validated in the context of real transcriptomic data. It enables the extraction of biologically relevant information from the dynamic transcriptomic profiles currently unnoticed from the available algorithms. The applicability of MiTimeS is not limited to transcriptomic data, but it could be accordingly used for the analysis of dynamic data from other cellular fingerprints.
Dutta
Bhaskar
B
Metabolic Engineering and Systems Biology Laboratory, Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland, USA.
Snyder
Robert
R
Klapa
Maria I
MI
D000465:Algorithms; D017360:Arabidopsis / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D018506:Gene Expression Regulation, Plant / Q000502:physiology; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology; D013997:Time Factors; D014157:Transcription Factors / Q000378:metabolism*; D014158:Transcription, Genetic / Q000502:physiology
D020543:Proteome; D014157:Transcription Factors