A New Method for Gene Identification in Comparative Genomic Analysis

Ching-Wei Chang ； Wen Zou ； James J. Chen

《Journal of Data Science》 6卷3期 (2008/07) Pp. 415-427

https://doi.org/10.6339/JDS.2008.06(3).505

並列摘要

Microarray technology has been used to characterize intraspecies genetic diversity in bacteria at the genome level and to rapidly determine genetic profiles of pathogenic microorganisms for high-throughput screening in food safety research. In the use of microarray technology for bacterial identification and characterization in comparative genomic analysis, the primary objective is to determine the present (conserved) and absent (divergent) genes for each bacterial sample tested. The goal of the analysis is to estimate an optimal cutoff in which genes with intensity above the cutoff are classified as conserved and below the cutoff are classified as divergent. Standard statistical procedures developed for identifying differentially expressed genes are not appropriate. These procedures use the significance testing approach and often require a sufficient number of biological replicates. This paper proposes an analytic method to determine a cutoff based on the change-point estimation using the multivariate adaptive regression splines (MARS). The proposed estimation method is applied to two public datasets to compare with an existing classification Genotyping Analysis by Charlie Kim (GACK) algorithm. The proposed method performs consistently better than the GACK algorithm with respect to the specificity and accuracy.

並列關鍵字

Bacterial identification and characterization ； change-point ； conserved and divergent genes ； dynamic cutoff ； food safety research ； multivariateadaptive regression splines (MARS)