I am so inspired by the words: “The launch of this dataset supports the Biden-Harris Administration’s Cancer Moonshot℠ goal of accelerating cancer research by improved sharing of data.”
Wen-Wei Liang et al. (wustl.edu) 這一篇，提及 HNSC tumor cell 致癌過程中有一個” immunosuppression switch”，文章中說是STAT5A。第二個重點，proteogenomic analysis把kinase又了連回來，好比FGFR2, EGFR 治療靶點有了更強的依據。第三點，Graphic abstract清楚劃出 hypomethylation 導致 FGFR2 濃度上升; hypermethylation 導致 STAT5A 濃度下降。
08/23 今天讀完總論這一篇：真是妙極、絕極! 原來一直以來使用TCGA檢體還需考慮缺血 (因會導致MAP signaling爆衝問題)。
- The first challenge: isoform issues. Remember to
- (1) Using the same versions of genome assembly and gene annotation for the processing of data from all omics platforms and all cancer types. (2) Reporting gene-level quantification when isoform level analysis is unrealistic. (3) applying a consistent and transparent rule for representative isoform selection when representative isoform selection is needed but the data are isoform agnostic, e.g., phosphosite localization annotation.
- A second challenge: embracing the full proteogenomic landscape as the molecular characterization of cells and tissues becomes more complete.
- Finally, PTMs. Both experimental and computational approaches are being developed to improve PTM peptide identification, which will help alleviate the missing value problem in PTM proteomics.
Web portals for data visualization and analysis
- PepQuery: http://www.pepquery.org
- LinkedOmics http://www.linkedomics.org
- LinkedOmicsKB: https://kb.linkedomics.org
- PTMcosmos: https://ptmcosmos.wustl.edu/
- ProTrackPath (pan-cancer portal): http://pancan.cptac-data-view.org/
- NGlycositeAtlas portal: https://www.biomarkercenter.org/nglycositeatlas