RT Journal
A1 Zhao, Li
A1 Chen, Yiyun
A1 Bajaj, Amol Onkar
A1 Eblimit, Aiden
A1 Xu, Mingchu
A1 Soens, Zachry T.
A1 Wang, Feng
A1 Ge, Zhongqi
A1 Jung, Sung Yun
A1 He, Feng
A1 Li, Yumei
A1 Wensel, Theodore G.
A1 Qin, Jun
A1 Chen, Rui
T1 Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes
JF Genome Research 
JO Genome Research 
YR 2016 
FD May 01 
VO 26 
IS 5 
SP 660 
OP 669 
DO 10.1101/gr.198911.115 
UL http://genome.cshlp.org/content/26/5/660.abstract 
AB Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other data sets, it can greatly enhance our ability to predict gene function genome-wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization, and 84% accuracy is achieved compared with experimental data. By integrating the protein OS enrichment score, the protein abundance, and the retina transcriptome, the probability of a gene playing an essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrates the synergy of combining subcellular fractionation proteomics with other omics data sets and is generally applicable to other tissues and diseases.