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10.1101/gr.198911.115 http://scihub22266oqcxt.onion/10.1101/gr.198911.115 C4864458!4864458 !26912414     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26912414 &cmd=llinks): Failed to open stream: HTTP request failed! 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Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes |pdf=|usr=}}{{26912414 }} gab.com Text Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes JO: Genome Res http://www.kidney.de/pget.php?&tw=1&pmid=26912414 #FOAvip 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. Twit Text FOAVip Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes ????Genome Res http://www.kidney.de/pget.php?&tw=1&pmid=26912414 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26912414 #FOAvip English Wikipedia <ref>{{Cite pmid|26912414 }}</ref> Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes #MMPMID26912414 Zhao L ; Chen Y ; Bajaj AO ; Eblimit A ; Xu M ; Soens ZT ; Wang F ; Ge Z ; Jung SY ; He F ; Li Y ; Wensel TG ; Qin J ; Chen R Genome Res 2016[May]; 26 (5 ): 660-9 PMID26912414 show ga 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. |*Eye Proteins/genetics/metabolism [MESH] |*Gene Expression Profiling [MESH] |*Proteomics [MESH] |*Retinal Diseases/genetics/metabolism [MESH] |*Transcriptome [MESH] |Animals [MESH] |Humans [MESH] |Mice [MESH]Integrative subcellular proteomic analysis allows accurate prediction (epmc).pdf DeepDyve Pubget Overpricing