I would like to conduct a gene expression meta-analysis between age and particular ageing-related phenotypes in human, mouse and rat.
To construct my data set, I only want to extract gene expression data from healthy controls from human, mouse and rat studies that have expression data at different ages/multiple timepoints (i.e. so I have a set of normal gene expression levels for mouse, rat and human at different ages and can correlate this to various phenotypes).
When i search "age" in GEO data set browser to identify suitable studies: https://www.ncbi.nlm.nih.gov/sites/GDSbrowser; there are 1,214 results.
However, if I select one result at random, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51843; I can see this is just a case-control expression study for cancer, with no age/time aspect to it. Thus, clearly I am not properly selecting the subset of studies that has expression data for different aged controls in the analysis.
How would you advise to specifically extract, from GEO, the data sets in human, mouse and rat that will have gene expression information for healthy control at different ages.
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written
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Tom •
1 answer
See this article:
Meta-analysis of age-related gene expression profiles identifies common signatures of aging
http://bioinformatics.oxfordjournals.org/content/25/7/875.full
F paragraph from the article:
"Changes in gene expression are associated with numerous biological processes, cellular responses and disease states. The availability of microarrays has made it possible to study gene expression in a high-throughput fashion and gather insights about biology and disease. In recent years, a massive amount of microarray studies have been conducted. To compile and organize the numerous datasets generated, resources like the Gene Expression Omnibus (GEO) (Barrett et al., 2007) have been established. The availability of microarray data from multiple experiments opens up new research opportunities. By eliminating idiosyncrasies of individual platforms and enhancing the signal-to-noise ratio, comparing profiles across platforms and species may reveal conserved molecular signatures that would otherwise be obscure in single datasets (Moreau et al., 2003; Ramasamy et al., 2008). Indeed, meta-analyses of gene expression profiles integrating multiple microarray studies have been particularly useful to identify conserved genetic signatures of cancer (Rhodes et al., 2004). "
It has a lot of citetion, so there should be some modern version.
https://scholar.google.ru/scholar?cites=13432054524359261673&as_sdt=2005&sciodt=0,5&hl=ru
Do you insist on GEO only?
There are a lot of other sites related to aging, in this paper for example:
Computational biology for ageing
http://rstb.royalsocietypublishing.org/content/royptb/366/1561/51.full.pdf
Thank you for trying to help, unfortunately I'm aware of this paper, and I'm unclear how it answers my question; it does not seem specifically to tell me how to download age-related data sets (I can try to email the author, but since the paper is already 8 years old...and so the author probably accessed the database 9/10 years ago, they may not remember). Yes, I think GEO is quite comprehensive, so I plan on downloading the data sets from GEO.
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While you are at the mercy of metadata submitted with datasets have you tried an advanced search with "atrribute name" set to age (add any additional terms that you need to refine)?
Thank you. Unfortunately this will not work I do not think. When I set the attribute name to age, one of the first results that I obtain is: https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS5649, which is just another case-control cancer study.