191 related articles for article (PubMed ID: 31235637)
1. MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins.
Ammar C; Gruber M; Csaba G; Zimmer R
Mol Cell Proteomics; 2019 Sep; 18(9):1880-1892. PubMed ID: 31235637
[TBL] [Abstract][Full Text] [Related]
2. MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics.
Prianichnikov N; Koch H; Koch S; Lubeck M; Heilig R; Brehmer S; Fischer R; Cox J
Mol Cell Proteomics; 2020 Jun; 19(6):1058-1069. PubMed ID: 32156793
[TBL] [Abstract][Full Text] [Related]
3. Targeted Feature Detection for Data-Dependent Shotgun Proteomics.
Weisser H; Choudhary JS
J Proteome Res; 2017 Aug; 16(8):2964-2974. PubMed ID: 28673088
[TBL] [Abstract][Full Text] [Related]
4. EBprot: Statistical analysis of labeling-based quantitative proteomics data.
Koh HW; Swa HL; Fermin D; Ler SG; Gunaratne J; Choi H
Proteomics; 2015 Aug; 15(15):2580-91. PubMed ID: 25913743
[TBL] [Abstract][Full Text] [Related]
5. IDEAL-Q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation.
Tsou CC; Tsai CF; Tsui YH; Sudhir PR; Wang YT; Chen YJ; Chen JY; Sung TY; Hsu WL
Mol Cell Proteomics; 2010 Jan; 9(1):131-44. PubMed ID: 19752006
[TBL] [Abstract][Full Text] [Related]
6. A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry.
Aoshima K; Takahashi K; Ikawa M; Kimura T; Fukuda M; Tanaka S; Parry HE; Fujita Y; Yoshizawa AC; Utsunomiya S; Kajihara S; Tanaka K; Oda Y
BMC Bioinformatics; 2014 Nov; 15(1):376. PubMed ID: 25420746
[TBL] [Abstract][Full Text] [Related]
7. Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins.
Griss J; Stanek F; Hudecz O; Dürnberger G; Perez-Riverol Y; Vizcaíno JA; Mechtler K
J Proteome Res; 2019 Apr; 18(4):1477-1485. PubMed ID: 30859831
[TBL] [Abstract][Full Text] [Related]
8. StatsPro: Systematic integration and evaluation of statistical approaches for detecting differential expression in label-free quantitative proteomics.
Yang Y; Cheng J; Wang S; Yang H
J Proteomics; 2022 Jan; 250():104386. PubMed ID: 34600153
[TBL] [Abstract][Full Text] [Related]
9. Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob.
Goeminne LJE; Gevaert K; Clement L
J Proteomics; 2018 Jan; 171():23-36. PubMed ID: 28391044
[TBL] [Abstract][Full Text] [Related]
10. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.
Brusniak MY; Bodenmiller B; Campbell D; Cooke K; Eddes J; Garbutt A; Lau H; Letarte S; Mueller LN; Sharma V; Vitek O; Zhang N; Aebersold R; Watts JD
BMC Bioinformatics; 2008 Dec; 9():542. PubMed ID: 19087345
[TBL] [Abstract][Full Text] [Related]
11. Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.
Ramus C; Hovasse A; Marcellin M; Hesse AM; Mouton-Barbosa E; Bouyssié D; Vaca S; Carapito C; Chaoui K; Bruley C; Garin J; Cianférani S; Ferro M; Van Dorssaeler A; Burlet-Schiltz O; Schaeffer C; Couté Y; Gonzalez de Peredo A
J Proteomics; 2016 Jan; 132():51-62. PubMed ID: 26585461
[TBL] [Abstract][Full Text] [Related]
12. A peptide-retrieval strategy enables significant improvement of quantitative performance without compromising confidence of identification.
Tu C; Shen S; Sheng Q; Shyr Y; Qu J
J Proteomics; 2017 Jan; 152():276-282. PubMed ID: 27903464
[TBL] [Abstract][Full Text] [Related]
13. Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics.
Langley SR; Mayr M
J Proteomics; 2015 Nov; 129():83-92. PubMed ID: 26193490
[TBL] [Abstract][Full Text] [Related]
14. MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics.
Isaksson M; Karlsson C; Laurell T; Kirkeby A; Heusel M
J Proteome Res; 2022 Feb; 21(2):535-546. PubMed ID: 35042333
[TBL] [Abstract][Full Text] [Related]
15. DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis.
Zhu Y; Orre LM; Zhou Tran Y; Mermelekas G; Johansson HJ; Malyutina A; Anders S; Lehtiö J
Mol Cell Proteomics; 2020 Jun; 19(6):1047-1057. PubMed ID: 32205417
[TBL] [Abstract][Full Text] [Related]
16. SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling.
Mueller LN; Rinner O; Schmidt A; Letarte S; Bodenmiller B; Brusniak MY; Vitek O; Aebersold R; Müller M
Proteomics; 2007 Oct; 7(19):3470-80. PubMed ID: 17726677
[TBL] [Abstract][Full Text] [Related]
17. Robust Summarization and Inference in Proteome-wide Label-free Quantification.
Sticker A; Goeminne L; Martens L; Clement L
Mol Cell Proteomics; 2020 Jul; 19(7):1209-1219. PubMed ID: 32321741
[TBL] [Abstract][Full Text] [Related]
18. ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC-MS/MS Experiments.
Choi M; Eren-Dogu ZF; Colangelo C; Cottrell J; Hoopmann MR; Kapp EA; Kim S; Lam H; Neubert TA; Palmblad M; Phinney BS; Weintraub ST; MacLean B; Vitek O
J Proteome Res; 2017 Feb; 16(2):945-957. PubMed ID: 27990823
[TBL] [Abstract][Full Text] [Related]
19. Proteome-Wide Evaluation of Two Common Protein Quantification Methods.
O'Connell JD; Paulo JA; O'Brien JJ; Gygi SP
J Proteome Res; 2018 May; 17(5):1934-1942. PubMed ID: 29635916
[TBL] [Abstract][Full Text] [Related]
20. The APEX Quantitative Proteomics Tool: generating protein quantitation estimates from LC-MS/MS proteomics results.
Braisted JC; Kuntumalla S; Vogel C; Marcotte EM; Rodrigues AR; Wang R; Huang ST; Ferlanti ES; Saeed AI; Fleischmann RD; Peterson SN; Pieper R
BMC Bioinformatics; 2008 Dec; 9():529. PubMed ID: 19068132
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
