The Digital Ageing Atlas: integrating the diversity of age-related changes into a unified resource

Multiple studies characterizing the human ageing phenotype have been conducted for decades. However, there is no centralized resource in which data on multiple age-related changes are collated. Currently, researchers must consult several sources, including primary publications, in order to obtain age-related data at various levels. To address this and facilitate integrative, system-level studies of ageing we developed the Digital Ageing Atlas (DAA). The DAA is a one-stop collection of human age-related data covering different biological levels (molecular, cellular, physiological, psychological and pathological) that is freely available online (http://ageing-map.org/). Each of the >3000 age-related changes is associated with a specific tissue and has its own page displaying a variety of information, including at least one reference. Age-related changes can also be linked to each other in hierarchical trees to represent different types of relationships. In addition, we developed an intuitive and user-friendly interface that allows searching, browsing and retrieving information in an integrated and interactive fashion. Overall, the DAA offers a new approach to systemizing ageing resources, providing a manually-curated and readily accessible source of age-related changes.

[1]  Stephen Welle,et al.  Gene expression profile of aging in human muscle. , 2003, Physiological genomics.

[2]  Carol Friedman,et al.  Visualizing information across multidimensional post-genomic structured and textual databases , 2005, Bioinform..

[3]  Andrzej Bartke,et al.  Physiological Basis of Aging and Geriatrics, Fourth Edition, Paola S. Timiras (Ed.). Informa Healthcare USA, Inc. (2007) , 2008 .

[4]  Robert Applebaum,et al.  The Aging Body: Physiological Changes and Psychological Consequences , 1988 .

[5]  Oliver Hofmann,et al.  Simplified ontologies allowing comparison of developmental mammalian gene expression , 2007, Genome Biology.

[6]  João Pedro de Magalhães,et al.  Meta-analysis of age-related gene expression profiles identifies common signatures of aging , 2009, Bioinform..

[7]  M. Mangel,et al.  Messages from mortality: the evolution of death rates in the old. , 1999, Trends in ecology & evolution.

[8]  T. Kirkwood,et al.  Systems biology of ageing and longevity , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[9]  Antje Chang,et al.  The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources , 2010, Nucleic Acids Res..

[10]  João Pedro de Magalhães,et al.  Human Ageing Genomic Resources: Integrated databases and tools for the biology and genetics of ageing , 2012, Nucleic Acids Res..

[11]  Robert Arking,et al.  The biology of aging : observations and principles , 1991 .

[12]  I. Kohane,et al.  Gene regulation and DNA damage in the ageing human brain , 2004, Nature.

[13]  Alexander R. Pico,et al.  Finding the Right Questions: Exploratory Pathway Analysis to Enhance Biological Discovery in Large Datasets , 2010, PLoS biology.

[14]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[15]  Lydia Ng,et al.  Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system , 2012, Nucleic Acids Res..

[16]  Jürgen Sühnel,et al.  AgeFactDB—the JenAge Ageing Factor Database—towards data integration in ageing research , 2013, Nucleic Acids Res..

[17]  G. Castellani,et al.  Systems biology and longevity: an emerging approach to identify innovative anti-aging targets and strategies. , 2010, Current pharmaceutical design.

[18]  A. Owen,et al.  AGEMAP: A Gene Expression Database for Aging in Mice , 2007, PLoS genetics.