The Functional Analysis Of Divergently Paired Genes (DPGs)and The Screening Of Aging Genes Based On Transcriptome Data

With the development of transcriptome hybridization technique, researchers could scan expression pattern of genes in specific cells or at a specific time point in large-scale. In this paper, except the general method of differentially expressed genes analysis, we also combinedly use the absolute pathway analysis (APA) and relative pathway analysis (RPA) to analyze the expression fluctuations of a particular gene set as a whole and the difference between the gene set and the background genes due to the accumulation of trace amounts changes. The linear regression method is also used to obtain the genes changed in time series, and then one combined method by local polynomial fitting and correlation calculation is developed by us to cluster these genes with time. Based on these methods above, this paper studies in two parts, one is for divergently paired genes (DPGs),and the other for the aging genes. Divergently paired genes (DPGs), also known as bidirectional genes or head-to-head genes, are conserved across species and lineages, and thus thought to be special in genomic organization. Despite numerous previous investigations on their conservation and organization, the functional relationship among DPGs in a given species or lineage has not been thoroughly clarified. Based on the comprehensive analysis on human DPGs, here we indicate that, comparing to randomly paired genes, the two members of the DPGs tend to cluster in similar cellular components, involved in similar molecular functions, and participate in different but related biological processes while enforcing related functions as modules. And then one functional network is constructed and bridged by DPGs which consists of three major modules. The largest module is proved to include many house-keeping genes and also involve in core cellular activities. This module also shows low variation of expression in both CNS (central nervous system) and non-CNS tissues. Furthermore, we also suggest that this particular module may play crucial roles in HIV infection and its disease mechanism. From the other part, we found277genes changed with aging process in brain tissues and cluster them into11clusters. Most of these clusters tend to change their expression level in reverse direction between development and aging. Considering the time point and order of these changes, we believed that these clusters might to have regulation relationship with each other especially cluster5and10, and15,25,55and60years of age might be changing point of aging process. We also screen and get39differential regulation genes (DRGs) which might be related with the regulation of aging, but need more provement.