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Zhou, Jian-Ying; Chen, Lijun; Zhang, Bai; Tian, Yuan; Liu, Tao; Thomas, Stefani N; Chen, Li; Schnaubelt, Michael; Boja, Emily; Hiltke, Tara; Kinsinger, Christopher R; Rodriguez, Henry; Davies, Sherri R; Li, Shunqiang; Snider, Jacqueline E; Erdmann-Gilmore, Petra; Tabb, David L; Townsend, R. Reid; Ellis, Matthew J; Rodland, Karin D; Smith, Richard D; Carr, Steven A; Zhang, Zhen; Chan, Daniel W; Zhang, Hui
Journal of proteome research, 12/2017, Letnik: 16, Številka: 12Journal Article
Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC–MS/MS) analyses were completed, generating six 2D LC–MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC–MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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