DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery
Tài liệu tham khảo
Schubert, 2015, Building high-quality assay libraries for targeted analysis of SWATH MS data, Nat Protoc, 10, 426, 10.1038/nprot.2015.015
Sandhu, 2018, Panomics for precision medicine, Trends Mol Med, 24, 85, 10.1016/j.molmed.2017.11.001
Aronson, 2015, Building the foundation for genomics in precision medicine, Nature, 526, 336, 10.1038/nature15816
Yang, 2017, Transplant genetics and genomics, Nat Rev Genet, 18, 309, 10.1038/nrg.2017.12
Zhang, 2014, Proteogenomic characterization of human colon and rectal cancer, Nature, 513, 382, 10.1038/nature13438
Bosch, 2017, Novel stool-based protein biomarkers for improved colorectal cancer screening: a case-control study, Ann Intern Med, 167, 855, 10.7326/M17-1068
Mertins, 2016, Proteogenomics connects somatic mutations to signalling in breast cancer, Nature, 534, 55, 10.1038/nature18003
Zhang, 2016, Integrated proteogenomic characterization of human high-grade serous ovarian cancer, Cell, 166, 755, 10.1016/j.cell.2016.05.069
Ge, 2018, A proteomic landscape of diffuse-type gastric cancer, Nat Commun, 9, 1012, 10.1038/s41467-018-03121-2
Zhu, 2018, Towards a one-stop solution for large-scale proteomics data analysis, Sci China Life Sci, 61, 351, 10.1007/s11427-017-9113-5
Cominetti, 2018, Obesity shows preserved plasma proteome in large independent clinical cohorts, Sci Rep, 8, 16981, 10.1038/s41598-018-35321-7
Gillet, 2012, Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis, Mol Cell Proteomics, 11, 10.1074/mcp.O111.016717
Guo, 2015, Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps, Nat Med, 21, 407, 10.1038/nm.3807
Rost, 2014, OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data, Nat Biotechnol, 32, 219, 10.1038/nbt.2841
Navarro, 2016, A multicenter study benchmarks software tools for label-free proteome quantification, Nat Biotechnol, 34, 1130, 10.1038/nbt.3685
Tsou, 2015, DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics, Nat Methods, 12, 258, 10.1038/nmeth.3255
Li, 2015, Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files, Nat Methods, 12, 1105, 10.1038/nmeth.3593
MacLean, 2010, Skyline: an open source document editor for creating and analyzing targeted proteomics experiments, Bioinformatics, 26, 966, 10.1093/bioinformatics/btq054
Bruderer, 2015, Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues, Mol Cell Proteomics, 14, 1400, 10.1074/mcp.M114.044305
Rosenberger, 2014, A repository of assays to quantify 10,000 human proteins by SWATH-MS, Sci Data, 1, 140031, 10.1038/sdata.2014.31
Rosenberger, 2017, Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses, Nat Methods, 14, 921, 10.1038/nmeth.4398
Liu, 2015, Quantitative variability of 342 plasma proteins in a human twin population, Mol Syst Biol, 11, 786, 10.15252/msb.20145728
Guo, 2018, Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers, Life Sci Alliance, 1, e201800042, 10.26508/lsa.201800042
Zhu, 2018, Identification of protein abundance changes in hepatocellular carcinoma tissues using PCT-SWATH, Proteomics Clin Appl, e1700179
Muntel, 2015, Advancing urinary protein biomarker discovery by data-independent acquisition on a quadrupole-orbitrap mass spectrometer, J Proteome Res, 14, 4752, 10.1021/acs.jproteome.5b00826
Meyer, 2017, Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques, Expert Rev Proteomics, 14, 419, 10.1080/14789450.2017.1322904
Chambers, 2012, A cross-platform toolkit for mass spectrometry and proteomics, Nat Biotechnol, 30, 918, 10.1038/nbt.2377
Li, 2005, pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry, Bioinformatics, 21, 3049, 10.1093/bioinformatics/bti439
Lam, 2007, Development and validation of a spectral library searching method for peptide identification from MS/MS, Proteomics, 7, 655, 10.1002/pmic.200600625
Parker, 2015, Identification of a set of conserved eukaryotic internal retention time standards for data-independent acquisition mass spectrometry, Mol Cell Proteomics, 14, 2800, 10.1074/mcp.O114.042267
Escher, 2012, Using iRT, a normalized retention time for more targeted measurement of peptides, Proteomics, 12, 1111, 10.1002/pmic.201100463
Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat Biotechnol, 26, 1367, 10.1038/nbt.1511
Eid, 2017, KinMap: a web-based tool for interactive navigation through human kinome data, BMC Bioinformatics, 18, 16, 10.1186/s12859-016-1433-7
Lambert, 2018, The human transcription factors, Cell, 172, 650, 10.1016/j.cell.2018.01.029
van der Maaten, 2014, Accelerating t-SNE using tree-based algorithms, J Mach Learn Res, 15, 3221
Tripathi, 2015, Meta- and orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virus budding, Cell Host Microbe, 18, 723, 10.1016/j.chom.2015.11.002
Shurbaji, 1996, Immunohistochemical detection of a fatty acid synthase (OA-519) as a predictor of progression of prostate cancer, Hum Pathol, 27, 917, 10.1016/S0046-8177(96)90218-X
Xin, 2007, TPP1 is a homologue of ciliate TEBP-beta and interacts with POT1 to recruit telomerase, Nature, 445, 559, 10.1038/nature05469
Nandakumar, 2012, The TEL patch of telomere protein TPP1 mediates telomerase recruitment and processivity, Nature, 492, 285, 10.1038/nature11648
Sexton, 2014, Genetic and molecular identification of three human TPP1 functions in telomerase action: recruitment, activation, and homeostasis set point regulation, Genes Dev, 28, 1885, 10.1101/gad.246819.114
Mocellin, 2013, Telomerase and the search for the end of cancer, Trends Mol Med, 19, 125, 10.1016/j.molmed.2012.11.006
Heaphy, 2011, The potential utility of telomere-related markers for cancer diagnosis, J Cell Mol Med, 15, 1227, 10.1111/j.1582-4934.2011.01284.x
Qian, 2012, Spondin-2 (SPON2), a more prostate-cancer-specific diagnostic biomarker, PLoS One, 7, e37225, 10.1371/journal.pone.0037225
Lucarelli, 2013, Spondin-2, a secreted extracellular matrix protein, is a novel diagnostic biomarker for prostate cancer, J Urol, 190, 2271, 10.1016/j.juro.2013.05.004
Steuber, 2008, Serum markers for prostate cancer: a rational approach to the literature, Eur Urol, 54, 31, 10.1016/j.eururo.2008.01.034
Cao, 2012, Serum C-reactive protein as an important prognostic variable in patients with diffuse large B cell lymphoma, Tumour Biol, 33, 1039, 10.1007/s13277-012-0337-z
Tzankov, 2003, Prognostic significance of CD44 expression in diffuse large B cell lymphoma of activated and germinal centre B cell-like types: a tissue microarray analysis of 90 cases, J Clin Pathol, 56, 747, 10.1136/jcp.56.10.747
Ling, 2008, Dynamic changes of serum proteomic spectra in patients with non-Hodgkin's lymphoma (NHL) before and after chemotherapy and screening of candidate biomarkers for NHL, Chin J Cancer, 27, 1065
Jimenez, 2018, The cancer proteomic landscape and the HUPO Cancer Proteome Project, Clin Proteom, 15, 4, 10.1186/s12014-018-9180-6
Zhu, 2019, High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification, Mol Oncol, 13, 2305, 10.1002/1878-0261.12570
Zhu, 2018, High-throughput proteomic analysis of fresh-frozen biopsy tissue samples using pressure cycling technology coupled with SWATH mass spectrometry, Methods Mol Biol, 1788, 279, 10.1007/7651_2017_87
Rost, 2016, TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics, Nat Methods, 13, 777, 10.1038/nmeth.3954