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by xaa
3216 days ago
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Well the project arose like this. I got interested in aging halfway through the PhD. I started collaborating heavily with aging people at my institution. But there are so many papers and so much data to get informed about the area. So I wondered "is there some semi-empirical way to find out what is 'most important' in aging so I can focus my future efforts on that?" The solution I hit on was to take all the available gene expression data and to build a system to ask "what genes/pathways/systems change most strongly and consistently with age across species, experimental conditions, and tissues"? This would be a "core aging signature", if it exists. Obviously this is only one of many ways to answer my question and neglects epigenetics, proteomics, etc, although we're currently extending the system to DNA methylation. There is not enough high-throughput proteomics data to make it possible to do this with protein yet. We do not use sequencing for now because it is much more of a processing burden and human RNA is behind dbGaP embargo. And at the time I started this, there really wasn't that much of it compared to GEO. My boss's interests are much more general than aging, so he encouraged me to develop the system to be more generic while still answering my question, which I did. It became a general meta-analysis system for asking "what genes change expression with <arbitrary condition> across the available experiments in GEO?" We found other things we could do with such a huge amount of expression data, and some of them are in Chapter 5. I would say the system itself is 80-90% done. But sadly I did not get to a really detailed analysis of aging yet, although my findings so far on that are in Chapter 4. |
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