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      Gerontome: a web-based database server for aging-related genes and analysis pipelines

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      Author(s): 1 , 2 , 1 , , 2 , 3 ,
      Publication date (Electronic):
      Journal: BMC Genomics
      Publisher: BioMed Central
      Conference name: Asia Pacific Bioinformatics Network (APBioNet) Ninth International Conference on Bioinformatics (InCoB2010)
      Conference date: 26–28 September 2010

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          Abstract

          Background

          Aging is a complex and challenging phenomenon that requires interdisciplinary efforts to unravel its mystery. Insight into genes relevant to the aging process would offer the chance to delay and avoid some of deteriorative aspects of aging through the use of preventive methods. To assist basic research on aging, a comprehensive database and analysis platform for aging-related genes is required.

          Results

          We developed a web-based database server, called Gerontome that contains aging-related gene information and user-friendly analysis pipelines. To construct the Gerontome database, we integrated aging-related genes and their annotation data. The aging-related genes were categorized by a set of structural terms from Gene Ontology (GO). Analysis pipelines for promoter analysis and protein-ligand docking were developed. The promoter analysis pipeline allows users to investigate the age-dependent regulation of gene expression. The protein-ligand docking pipeline provides information on the position and orientation of a ligand in an age-related protein surface.

          Conclusion

          Gerontome can be accessed through web interfaces for querying and browsing. The server provides comprehensive age-related gene information and analysis pipelines. Gerontome is available free at http://gerontome.kobic.re.kr.

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          Meta-analysis of age-related gene expression profiles identifies common signatures of aging.

          João Pedro de Magalhães, Joao Curado, George Church (2009)
          Numerous microarray studies of aging have been conducted, yet given the noisy nature of gene expression changes with age, elucidating the transcriptional features of aging and how these relate to physiological, biochemical and pathological changes remains a critical problem. We performed a meta-analysis of age-related gene expression profiles using 27 datasets from mice, rats and humans. Our results reveal several common signatures of aging, including 56 genes consistently overexpressed with age, the most significant of which was APOD, and 17 genes underexpressed with age. We characterized the biological processes associated with these signatures and found that age-related gene expression changes most notably involve an overexpression of inflammation and immune response genes and of genes associated with the lysosome. An underexpression of collagen genes and of genes associated with energy metabolism, particularly mitochondrial genes, as well as alterations in the expression of genes related to apoptosis, cell cycle and cellular senescence biomarkers, were also observed. By employing a new method that emphasizes sensitivity, our work further reveals previously unknown transcriptional changes with age in many genes, processes and functions. We suggest these molecular signatures reflect a combination of degenerative processes but also transcriptional responses to the process of aging. Overall, our results help to understand how transcriptional changes relate to the process of aging and could serve as targets for future studies. http://genomics.senescence.info/uarrays/signatures.html. Supplementary data are available at Bioinformatics online.
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            NCBI Reference Sequences: current status, policy and new initiatives

            Kim D. Pruitt, Tatiana Tatusova, William Klimke … (2009)
            NCBI's Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. RefSeq records integrate information from multiple sources and represent a current description of the sequence, the gene and sequence features. The database includes over 5300 organisms spanning prokaryotes, eukaryotes and viruses, with records for more than 5.5 × 106 proteins (RefSeq release 30). Feature annotation is applied by a combination of curation, collaboration, propagation from other sources and computation. We report here on the recent growth of the database, recent changes to feature annotations and record types for eukaryotic (primarily vertebrate) species and policies regarding species inclusion and genome annotation. In addition, we introduce RefSeqGene, a new initiative to support reporting variation data on a stable genomic coordinate system.
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              GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis

              Qi Zheng, Xiu-Jie Wang (2008)
              Gene Ontology (GO) analysis has become a commonly used approach for functional studies of large-scale genomic or transcriptomic data. Although there have been a lot of software with GO-related analysis functions, new tools are still needed to meet the requirements for data generated by newly developed technologies or for advanced analysis purpose. Here, we present a Gene Ontology Enrichment Analysis Software Toolkit (GOEAST), an easy-to-use web-based toolkit that identifies statistically overrepresented GO terms within given gene sets. Compared with available GO analysis tools, GOEAST has the following improved features: (i) GOEAST displays enriched GO terms in graphical format according to their relationships in the hierarchical tree of each GO category (biological process, molecular function and cellular component), therefore, provides better understanding of the correlations among enriched GO terms; (ii) GOEAST supports analysis for data from various sources (probe or probe set IDs of Affymetrix, Illumina, Agilent or customized microarrays, as well as different gene identifiers) and multiple species (about 60 prokaryote and eukaryote species); (iii) One unique feature of GOEAST is to allow cross comparison of the GO enrichment status of multiple experiments to identify functional correlations among them. GOEAST also provides rigorous statistical tests to enhance the reliability of analysis results. GOEAST is freely accessible at http://omicslab.genetics.ac.cn/GOEAST/
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                Author and article information

                Conference
                Journal ID (nlm-ta): BMC Genomics
                Title: BMC Genomics
                Publisher: BioMed Central
                ISSN (Electronic): 1471-2164
                Publication date Collection: 2010
                Publication date (Electronic): 2 December 2010
                Volume: 11
                Issue: Suppl 4
                Page: S20
                Affiliations
                [1 ]Korean BioInformation Center (KOBIC), KRIBB, Daejeon 305-806, Korea
                [2 ]Interdisciplinary Research Program of Bioinformatics, Busan National University, Busan 609-735, Korea
                [3 ]Aging Tissue Bank, College of Pharmacy, Busan National University, Busan 609-735, Korea
                Article
                Publisher ID: 1471-2164-11-S4-S20
                DOI: 10.1186/1471-2164-11-S4-S20
                PMC ID: 3005931
                PubMed ID: 21143804
                SO-VID: ec9cfcf0-de6d-4b02-93d9-c55f73fa0691
                Copyright © Copyright ©2010 Kwon et al; licensee BioMed Central Ltd.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Conference name: Asia Pacific Bioinformatics Network (APBioNet) Ninth International Conference on Bioinformatics (InCoB2010)
                Conference location: Tokyo, Japan
                Conference date: 26–28 September 2010
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                Subject: Proceedings

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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