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For: Vowinckel J, Zelezniak A, Bruderer R, Mülleder M, Reiter L, Ralser M. Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition. Sci Rep 2018;8:4346. [PMID: 29531254 DOI: 10.1038/s41598-018-22610-4] [Cited by in Crossref: 32] [Cited by in F6Publishing: 27] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhang T, Gaffrey MJ, Monroe ME, Thomas DG, Weitz KK, Piehowski PD, Petyuk VA, Moore RJ, Thrall BD, Qian WJ. Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling. J Proteome Res 2020;19:2863-72. [PMID: 32407631 DOI: 10.1021/acs.jproteome.0c00310] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
2 Distler U, Łącki MK, Schumann S, Wanninger M, Tenzer S. Enhancing Sensitivity of Microflow-Based Bottom-Up Proteomics through Postcolumn Solvent Addition. Anal Chem 2019;91:7510-5. [PMID: 31117400 DOI: 10.1021/acs.analchem.9b00118] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
3 Orsburn BC, Miller SD, Jenkins CJ. Standard Flow Multiplexed Proteomics (SFloMPro)—An Accessible Alternative to NanoFlow Based Shotgun Proteomics. Proteomes 2022;10:3. [DOI: 10.3390/proteomes10010003] [Reference Citation Analysis]
4 Messner CB, Demichev V, Bloomfield N, Yu JSL, White M, Kreidl M, Egger AS, Freiwald A, Ivosev G, Wasim F, Zelezniak A, Jürgens L, Suttorp N, Sander LE, Kurth F, Lilley KS, Mülleder M, Tate S, Ralser M. Ultra-fast proteomics with Scanning SWATH. Nat Biotechnol 2021;39:846-54. [PMID: 33767396 DOI: 10.1038/s41587-021-00860-4] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 14.0] [Reference Citation Analysis]
5 Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 2020;17:41-4. [PMID: 31768060 DOI: 10.1038/s41592-019-0638-x] [Cited by in Crossref: 105] [Cited by in F6Publishing: 68] [Article Influence: 35.0] [Reference Citation Analysis]
6 Chen Y, Banerjee D, Mukhopadhyay A, Petzold CJ. Systems and synthetic biology tools for advanced bioproduction hosts. Curr Opin Biotechnol 2020;64:101-9. [PMID: 31927061 DOI: 10.1016/j.copbio.2019.12.007] [Cited by in Crossref: 21] [Cited by in F6Publishing: 14] [Article Influence: 10.5] [Reference Citation Analysis]
7 Álvarez-ruiz R, Picó Y. Sequential window acquisition of all theoretical fragments versus information dependent acquisition for suspected-screening of pharmaceuticals in sediments and mussels by ultra-high pressure liquid chromatography-quadrupole time-of-flight-mass spectrometry. Journal of Chromatography A 2019;1595:81-90. [DOI: 10.1016/j.chroma.2019.02.041] [Cited by in Crossref: 18] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
8 Zhu W, Cheng X, Ren C, Chen J, Zhang Y, Chen Y, Jia X, Wang S, Sun Z, Zhang R, Zhang Z. Proteomic characterization and comparison of ram (Ovis aries) and buck (Capra hircus) spermatozoa proteome using a data independent acquisition mass spectometry (DIA-MS) approach. PLoS One 2020;15:e0228656. [PMID: 32053710 DOI: 10.1371/journal.pone.0228656] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
9 Sun R, Hunter C, Chen C, Ge W, Morrice N, Liang S, Zhu T, Yuan C, Ruan G, Zhang Q, Cai X, Yu X, Chen L, Dai S, Luan Z, Aebersold R, Zhu Y, Guo T. Accelerated Protein Biomarker Discovery from FFPE Tissue Samples Using Single-Shot, Short Gradient Microflow SWATH MS. J Proteome Res 2020;19:2732-41. [PMID: 32053377 DOI: 10.1021/acs.jproteome.9b00671] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
10 Midha MK, Kusebauch U, Shteynberg D, Kapil C, Bader SL, Reddy PJ, Campbell DS, Baliga NS, Moritz RL. A comprehensive spectral assay library to quantify the Escherichia coli proteome by DIA/SWATH-MS. Sci Data 2020;7:389. [PMID: 33184295 DOI: 10.1038/s41597-020-00724-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
11 Vowinckel J, Hartl J, Marx H, Kerick M, Runggatscher K, Keller MA, Mülleder M, Day J, Weber M, Rinnerthaler M, Yu JSL, Aulakh SK, Lehmann A, Mattanovich D, Timmermann B, Zhang N, Dunn CD, MacRae JI, Breitenbach M, Ralser M. The metabolic growth limitations of petite cells lacking the mitochondrial genome. Nat Metab 2021;3:1521-35. [PMID: 34799698 DOI: 10.1038/s42255-021-00477-6] [Reference Citation Analysis]
12 Pries C, Razaghi-Moghadam Z, Kopka J, Nikoloski Z. Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism. Sci Rep 2021;11:4787. [PMID: 33637852 DOI: 10.1038/s41598-021-84114-y] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
13 Bian Y, Zheng R, Bayer FP, Wong C, Chang YC, Meng C, Zolg DP, Reinecke M, Zecha J, Wiechmann S, Heinzlmeir S, Scherr J, Hemmer B, Baynham M, Gingras AC, Boychenko O, Kuster B. Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC-MS/MS. Nat Commun 2020;11:157. [PMID: 31919466 DOI: 10.1038/s41467-019-13973-x] [Cited by in Crossref: 66] [Cited by in F6Publishing: 57] [Article Influence: 33.0] [Reference Citation Analysis]
14 Muntel J, Kirkpatrick J, Bruderer R, Huang T, Vitek O, Ori A, Reiter L. Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time. J Proteome Res 2019;18:1340-51. [DOI: 10.1021/acs.jproteome.8b00898] [Cited by in Crossref: 46] [Cited by in F6Publishing: 34] [Article Influence: 15.3] [Reference Citation Analysis]
15 Distler U, Sielaff M, Tenzer S. Label-Free Proteomics of Quantity-Limited Samples Using Ion Mobility-Assisted Data-Independent Acquisition Mass Spectrometry. Methods Mol Biol 2021;2228:327-39. [PMID: 33950501 DOI: 10.1007/978-1-0716-1024-4_23] [Reference Citation Analysis]
16 [DOI: 10.1101/656793] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
17 Sielaff M, Curella V, Neerukonda M, Afzal M, El Hassouni K, Distler U, Schuppan D, Longin CFH, Tenzer S. Hybrid QconCAT-Based Targeted Absolute and Data-Independent Acquisition-Based Label-Free Quantification Enables In-Depth Proteomic Characterization of Wheat Amylase/Trypsin Inhibitor Extracts. J Proteome Res 2021;20:1544-57. [PMID: 33507751 DOI: 10.1021/acs.jproteome.0c00752] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
18 Meyer JG, Schilling B. Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev Proteomics 2017;14:419-29. [PMID: 28436239 DOI: 10.1080/14789450.2017.1322904] [Cited by in Crossref: 65] [Cited by in F6Publishing: 58] [Article Influence: 16.3] [Reference Citation Analysis]
19 Sánchez BJ, Lahtvee PJ, Campbell K, Kasvandik S, Yu R, Domenzain I, Zelezniak A, Nielsen J. Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae. Proteomics 2021;21:e2000093. [PMID: 33452728 DOI: 10.1002/pmic.202000093] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
20 Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 2018;14:e8126. [PMID: 30104418 DOI: 10.15252/msb.20178126] [Cited by in Crossref: 305] [Cited by in F6Publishing: 260] [Article Influence: 76.3] [Reference Citation Analysis]
21 Fabre B, Korona D, Lees JG, Lazar I, Livneh I, Brunet M, Orengo CA, Russell S, Lilley KS. Comparison of Drosophila melanogaster Embryo and Adult Proteome by SWATH-MS Reveals Differential Regulation of Protein Synthesis, Degradation Machinery, and Metabolism Modules. J Proteome Res 2019;18:2525-34. [DOI: 10.1021/acs.jproteome.9b00076] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
22 Gerdes H, Casado P, Dokal A, Hijazi M, Akhtar N, Osuntola R, Rajeeve V, Fitzgibbon J, Travers J, Britton D, Khorsandi S, Cutillas PR. Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nat Commun 2021;12:1850. [PMID: 33767176 DOI: 10.1038/s41467-021-22170-8] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
23 Zelezniak A, Vowinckel J, Capuano F, Messner CB, Demichev V, Polowsky N, Mülleder M, Kamrad S, Klaus B, Keller MA, Ralser M. Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts. Cell Syst 2018;7:269-283.e6. [PMID: 30195436 DOI: 10.1016/j.cels.2018.08.001] [Cited by in Crossref: 40] [Cited by in F6Publishing: 35] [Article Influence: 10.0] [Reference Citation Analysis]
24 [DOI: 10.1101/2020.04.27.20081810] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 Bruderer R, Muntel J, Müller S, Bernhardt OM, Gandhi T, Cominetti O, Macron C, Carayol J, Rinner O, Astrup A, Saris WHM, Hager J, Valsesia A, Dayon L, Reiter L. Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance. Mol Cell Proteomics 2019;18:1242-54. [PMID: 30948622 DOI: 10.1074/mcp.RA118.001288] [Cited by in Crossref: 66] [Cited by in F6Publishing: 24] [Article Influence: 22.0] [Reference Citation Analysis]
26 Collins BC, Hunter CL, Liu Y, Schilling B, Rosenberger G, Bader SL, Chan DW, Gibson BW, Gingras AC, Held JM, Hirayama-Kurogi M, Hou G, Krisp C, Larsen B, Lin L, Liu S, Molloy MP, Moritz RL, Ohtsuki S, Schlapbach R, Selevsek N, Thomas SN, Tzeng SC, Zhang H, Aebersold R. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nat Commun 2017;8:291. [PMID: 28827567 DOI: 10.1038/s41467-017-00249-5] [Cited by in Crossref: 224] [Cited by in F6Publishing: 199] [Article Influence: 44.8] [Reference Citation Analysis]
27 Merkley ED, Burnum-Johnson KE, Anderson LN, Jenson SC, Wahl KL. Uniformly 15N-Labeled Recombinant Ricin A-Chain as an Internal Retention Time Standard for Increased Confidence in Forensic Identification of Ricin by Untargeted Nanoflow Liquid Chromatography-Tandem Mass Spectrometry. Anal Chem 2019;91:13372-6. [PMID: 31596564 DOI: 10.1021/acs.analchem.9b03389] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
28 Bian Y, Bayer FP, Chang Y, Meng C, Hoefer S, Deng N, Zheng R, Boychenko O, Kuster B. Robust Microflow LC-MS/MS for Proteome Analysis: 38 000 Runs and Counting. Anal Chem 2021;93:3686-90. [DOI: 10.1021/acs.analchem.1c00257] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
29 Messner CB, Demichev V, Wendisch D, Michalick L, White M, Freiwald A, Textoris-Taube K, Vernardis SI, Egger AS, Kreidl M, Ludwig D, Kilian C, Agostini F, Zelezniak A, Thibeault C, Pfeiffer M, Hippenstiel S, Hocke A, von Kalle C, Campbell A, Hayward C, Porteous DJ, Marioni RE, Langenberg C, Lilley KS, Kuebler WM, Mülleder M, Drosten C, Suttorp N, Witzenrath M, Kurth F, Sander LE, Ralser M. Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection. Cell Syst 2020;11:11-24.e4. [PMID: 32619549 DOI: 10.1016/j.cels.2020.05.012] [Cited by in Crossref: 152] [Cited by in F6Publishing: 124] [Article Influence: 76.0] [Reference Citation Analysis]