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001-es BibID:	BIBFORM121570
035-os BibID:	(scopus)85179616172 (wos)001137562900001
Első szerző:	Bouyssié, David
Cím:	WOMBAT-P : Benchmarking Label-Free Proteomics Data Analysis Workflows / Bouyssié David, Altner Pnar, Capella-Gutierrez Salvador, Fernández José M., Hagemeijer Yanick Paco, Horvatovich Peter, Hubálek Martin, Levander Fredrik, Mauri Pierluigi, Palmblad Magnus, Raffelsberger Wolfgang, Rodríguez-Navas Laura, Di Silvestre Dario, Kunkli Balázs Tibor, Uszkoreit Julian, Vandenbrouck Yves, Vizcaíno Juan Antonio, Winkelhardt Dirk, Schwämmle Veit
Dátum:	2024
ISSN:	1535-3893
Megjegyzések:	The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines. ? 2023 American Chemical Society
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk benchmarking data analysis label-free proteomics quality metrics workflow
Megjelenés:	Journal Of Proteome Research. - 23 : 1 (2024), p. 418-429. -
További szerzők:	Altner, Pnar Capella-Gutierrez, Salvador Fernández, José M. Hagemeijer, Yanick Paco Horvatovich, Peter Hubálek, Martin Levander, Fredrik Mauri, Pierluigi Palmblad, Magnus Raffelsberger, Wolfgang Rodríguez-Navas, Laura Di Silvestre, Dario Kunkli Balázs (1990-) (bioinformatikus, biokémikus) Uszkoreit, Julian Vandenbrouck, Yves Vizcaíno, Juan Antonio Winkelhardt, Dirk Schwämmle, Veit
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001-es BibID:	BIBFORM090030
Első szerző:	Révész Ágnes
Cím:	Tailoring to Search Engines : bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence / Ágnes Révész, Márton Gyula Milley, Kinga Nagy, Dániel Szabó, Gergő Kalló, Éva Csősz, Károly Vékey, László Drahos
Dátum:	2021
ISSN:	1535-3893
Megjegyzések:	Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide-spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography-tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10-40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).
Tárgyszavak:	Természettudományok Kémiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk bottom-up proteomics collision energy optimization database search
Megjelenés:	Journal Of Proteome Research. - 20 : 1 (2021), p. 474-484. -
További szerzők:	Milley Márton Gyula Nagy Kinga (1988-) (agrár) Szabó Dániel (1986-) (gépészmérnök) Kalló Gergő (1989-) (molekuláris biológus) Csősz Éva (1977-) (biokémikus, molekuláris biológus) Vékey Károly Drahos László
Pályázati támogatás:	GINOP-2.3.3-15-2016-00020 GINOP NKFIH PD-132135 Egyéb NKFIH K 131762 Egyéb
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