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Improving SWATH-MS analysis by Deep-learning

ABSTRACT: To be able to reliably generate theoretical libraries that can be used in SWATH experiments, we developed a prediction framework, deep-learning for SWATH analysis (dpSWATH), to improve the sensitivity and specificity of data generated by Q-TOF mass spectrometers. The theoretical library built by dpSWATH allowed us to increase the identification rate of proteins and peptides compared to traditional or library-free methods. Especially, the in-silico library built based on the transcriptome scale identified the most proteins while kept a similar FDR as DDA library. Based on our analysis we conclude that dpSWATH is superior in predicting libraries that can be used for SWATH-MS measurements compared to other algorithms that are based on Orbitrap data.

INSTRUMENT(S): TripleTOF 6600

ORGANISM(S): Drosophila Melanogaster (fruit Fly)

TISSUE(S): Embryo

SUBMITTER: Bo Sun

LAB HEAD: Prof. Dr. Axel Imhof

PROVIDER: PXD038407 | Pride | 2023-01-16

REPOSITORIES: Pride

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			Action	DRS
	2018-08-28_TT_AS01_DMel_nuclear_lysate_hpH_01.wiff	Wiff
	2018-08-28_TT_AS01_DMel_nuclear_lysate_hpH_01.wiff.scan	Wiff
	2018-08-28_TT_AS01_DMel_nuclear_lysate_hpH_31.wiff	Wiff
	2018-08-28_TT_AS01_DMel_nuclear_lysate_hpH_31.wiff.scan	Wiff
	2018-08-28_TT_AS02_DMel_nuclear_lysate_hpH_02.wiff	Wiff

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