| 초록 |
Detecting gene transcripts and investigating gene functions at the genomic scale is essential to multiple areas of biotechnological research and applications. To this end, differential transcription profiles in Escherichia coli were detected by DNA microarrays. To deal with noise of the data, a rigorous statistical method to analyze the data was developed. This method consists of rank invariant, Lowess method, and a Markov Chain Monte Carlo computation. The method considers many sources of error and takes them into account. With the DNA microarray and statistical tool, the expression profiles of E. coli grown in two different carbon sources, acetate and glucose, were monitored. The result was compared with metabolic fluxes computed in both conditions. The result was used to verify a gene function predicted with sequence comparison. |
