My interest in TCGA analyses began at UVA when I would witness lab members misuse cBioPortal to show that their genes were involved in cancer. I began to wonder why we study genes and then retroactively try to show that they may be involved in cancer. Why not just study the genes that are most likely to be involved in cancer? Surprisingly, you could only find lists of the genes that were most commonly mutated in cancer and not genes whose expression correlated with survival. I was also curious if the same genes were implicated in the different cancers and ended up publishing the largest pan-cancer analylsis of prognostic genes. I realized there wasn't a good data portal for survival data, especially for miRNAs and lncRNAs, so I created OncoLnc. I also was interested in using the RNA-SEQ data from TCGA to identify novel lncRNAs and with Brian Reon developed a pipeline for finding lncRNAs in brain cancer. I am still very much involved in TCGA data analyses. OncoLnc receives over 50K hits per month and I constantly answer questions concerning OncoLnc. I am currently working on applying machine learning strategies to TCGA data.
Related Publications
OncoRank: A pan-cancer method of combining survival correlations and its application to mRNAs, miRNAs, and lncRNAs PeerJ Preprints
A Pan-cancer analysis of prognostic genes PeerJ
Expression of lncRNAs in Low-Grade Gliomas and Glioblastoma Multiforme: An In Silico Analysis PLOS Medicine
OncoLnc: Linking TCGA survival data to lncRNAs, mRNAs, and miRNAs PeerJ Computer Science
The lncRNA DRAIC/PCAT29 locus constitutes a tumor-suppressive nexus Molecular Cancer Research
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