Hi everyone, I have the list of 442 genes of breast cancer data that have been investigated based of the gene expression measurement. I want to identify the most promising candidates among such large lists of genes by using different strategies which have been implemented into web applications, such as BioGraph, Endeavour, Toppgene, etc. now, how to select best gene prioritization tool for finding less than 10 confident candidate genes that are more effective than rest genes for analysis on breast cancer data? Thanks in advance!
2 answers
Hi leila
You could use Endeavour, VAAST, GenRank, OMIM Explorer, PITA, SPRING tool, Target mine and meta ranker.
I heard VAAST algorithm used for UK 100K genome project, which might be useful for you too. Please take look at this article, it might be useful for you
http://massgenomics.org/2013/03/breast-cancer-susceptibility-rare-variants.html
Please tell me if you need more information.
Bests
You can find a long list of prioritization tools here. As already mentioned, what's best depends on which property you're looking for. Also keep in mind that, in a data integration approach, not every data set is actually worth using. For prioritization based on gene function prediction, I would suggest GeneMANIA (without using gene expression data) or FUN-L.
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Prioritization for What? You didn't say what you're trying to accomplish. The best gene will depend on how you define best!
Dear Leila, Hi. may be you can use this resource : http://lbb.ut.ac.ir/?p=1450