Data sources
Aging-related gene information was obtained from HAGR (http://genomics.senescence.info/), AGEID (http://uwaging.org/genesdb/index.php), the meta-analysis of age-related gene expression Profiles [3], and aging-related yeast2hybrid experiment [8]. From the downloaded data, we created a non-redundant gene set by removing the redundancy in the three databases. As of April 1, 2010, the Gerontome database had 848 non-redundant aging-related genes.
Gerontome uses data from a number of other databases. Human homologs were downloaded from NCBI’s Homologene [9]. Promoter sequences of human genes were obtained from the UCSC genome browser (hg18) [10]. Transcriptional profiles and protein-protein interactions were taken from the Transcription Factor Binding Site (TFBS) conserved track in the UCSC genome browser [10] and HPRD [11] databases, respectively. We used the LOCATE database [12] to identify localization information and the Funcoup database [13] to obtain confidence scores of protein-protein interactions. These data were mapped into aging-related genes and integrated into the Gerontome database (Fig. 1).
We used Gene Ontology (GO) annotation, which describes how gene products behave in a cellular context [14]. GO is composed of three subdivisions covering basic areas of biological research: molecular function, biological process, and cellular function. To identify GO categories that tend to be associated with aging genes, we used files downloaded from Entrez Gene database [15]. Through the categorization, we were able to assign 848 genes to GO accession numbers.
Analysis pipelines
Gerontome provides information regarding the molecular features of aging-related genes such as transcription factor binding sites and protein-ligand docking. To provide this information, we developed two analysis pipelines: promoter analysis and protein-ligand docking.
The promoter analysis pipeline allows users to investigate the age-dependent regulation of gene expression through the identification of transcription factors and their binding sequences (Fig. 2). Identification of transcriptional regulation of age-related genes is generally the most important step in aging research. In the pipeline, homologous genes to the query identifiers were first extracted from NCBI's Homologene. Second, upstream sequences of the extracted homologous genes were obtained. The default length of upstream sequences was set at 1000 bases. Third, the server scanned transcription factor binding sites in the upstream sequences using the TFBS conserved information from the UCSC genome browser. Lastly the server provided comparative visualization of homologous genes, TFBSs information, and known genes. We used Gbrowse [16] to visualize the results. From the pipeline, users can find a correlation between age-related genes and transcription factor binding sites [17–19].
The identification of protein structure is a key step to understanding the biological function and biomolecular interactions of proteins. Docking between proteins and ligands is important in the development of anti-aging drugs. Docking is the identification of the low-energy binding modes of a small molecule or ligand within the active site of a macromolecule or receptor whose structure is known. In the protein-ligand docking pipeline, the positions and orientations of ligands in protein surfaces were predicted using a geometric matching algorithm in the Dock Program [20] (Fig. 3). Users can dock their ligands to surfaces of protein structures. To view ligand positions on protein structures, we used a JMol program [21]. The protein-ligand docking pipeline enables users to simulate interaction affinity without ligand information or a specific protein structure.
Web-based server
We developed a web-based server to provide a back-end pipeline for aging analysis and to allow users to compare their genes and proteins with the Gerontome database. The Gerontome database server is composed of a wiki-based web interface and a MySQL 5.0 database management system. The web interface is implemented in static HTML pages, PHP, and JavaScript under an Apache 2.2 web server. MySQL is used to store the age-related gene information and their annotations and analysis data.
