Dr Jing Li's Lab

·CURRENT STATUS:  Still learning

·DESCRIPTION:  TCGA has characterized the genomic features of human cancers and this has presented a new challenge of explaining how genomic alterations drive cancers. As proteins link genotypes to phenotypes, the Clinical Proteomic Tumour Analysis Consortium (CPTAC) is performing proteomic analyses of TCGA tumour specimens for selected cancer types. And here, inspired by CPTAC, we followed it's pipeline to do more disease proteomics research.

·PARTICIPATION:  samples clustering, protein coexpression network

Fig. multi-feature supervised/unsupervised clustering

Fig. protein coexpression network

·SUMMARY:  During the observation and participation, I've learned a lot in this big-data experiment. That includes advanced bioinformatics skill:

1. multi-omics analyze pipieline
2. proteomics analyze methods
3. supervised/unsupervised clustering
4. differential expression analyze
5. basic survival analyze
6. basic genomic (SNP, CNV) analyze

Besides from the skill, what benifits me most will be the elaborate design of big-data. Tring to give a comprehensive inference or valid prediction, the initial design needs multipartite cooperation to come up with a thorough design. And researchers shall keep adjusting their plan to keep up with the more rapidly changed situation

·REFERENCE:  

1. Proteogenomic characterization of human colon and rectal cancer. Zhang B, Wang J, Wang X. Nature. 2014 Sep
2. Proteogenomics connects somatic mutations to signalling in breast cancer. Mertins P, Mani DR, Ruggles KV. Nature. 2016 Jun
3. Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Zhang H, Liu T, Zhang Z. Cell. 2016 Jul
4. Protein Analysis by Shotgun/Bottom-up Proteomics. Zhang Y, Fonslow BR, Shan B. Chem Rev. 2013 Apr
5. Mass-spectrometric exploration of proteome structure and function. Aebersold R, Mann M. Nature. 2016 Sep
6. Quantitative, high-resolution proteomics for data-driven systems biology. Cox J, Mann M. Annu Rev Biochem. 2011