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Gives us an idea of what genes are more likely related to the trait. It's a bit sparse to have the Gene Section with just Phen2Gene, in honesty. May be good to add sources like this that use more data; no licensing information and code and data are online for free download so I'm guessing with citation we can use it. Results: https://www.dropbox.com/sh/dz4haeo48s34sex/AADT_nwA3HCl_VFEScGN-kWXa/results?dl=0&subfolder_nav_tracking=1 Paper https://www.medrxiv.org/content/10.1101/2020.09.08.20190561v1.full.pdf+html Data format is pretty straightforward, like this:
trait cohort ensgid gene chromosome start end tss pops_score Alzheimer PASS ENSG00000079974 RABL2B 22 51205929 51222091 51222091 0.2660925297737443 Anorexia PASS ENSG00000186092 OR4F5 1 69091 70008 69091 -0.006756145993567379
Easy Elasticsearch parse. Can be limited somewhat in results, but still something new using some powerful data.
The text was updated successfully, but these errors were encountered:
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Gives us an idea of what genes are more likely related to the trait.
It's a bit sparse to have the Gene Section with just Phen2Gene, in honesty. May be good to add sources like this that use more data; no licensing information and code and data are online for free download so I'm guessing with citation we can use it.
Results:
https://www.dropbox.com/sh/dz4haeo48s34sex/AADT_nwA3HCl_VFEScGN-kWXa/results?dl=0&subfolder_nav_tracking=1
Paper
https://www.medrxiv.org/content/10.1101/2020.09.08.20190561v1.full.pdf+html
Data format is pretty straightforward, like this:
Easy Elasticsearch parse. Can be limited somewhat in results, but still something new using some powerful data.
The text was updated successfully, but these errors were encountered: