PXD038407

PXD038407 is an original dataset announced via ProteomeXchange.

Title	Improving SWATH-MS analysis by Deep-learning
Description	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.
HostingRepository	PRIDE
AnnounceDate	2023-11-14
AnnouncementXML	Submission_2023-11-14_08:58:31.339.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Bo Sun
SpeciesList	scientific name: Drosophila melanogaster (Fruit fly); NCBI TaxID: 7227;
ModificationList	No PTMs are included in the dataset
Instrument	TripleTOF 6600

Revision	Datetime	Status	ChangeLog Entry
0	2022-11-26 05:11:55	ID requested
1	2023-01-16 03:51:46	announced
⏵ 2	2023-11-14 08:58:31	announced	2023-11-14: Updated project metadata.

Dataset with its publication pending

submitter keyword: TripleTOF 6600, Embryo, D.melanogaster

Prof. Dr. Axel Imhof
contact affiliation	Histone Modifications Group Zentrallabor für Proteinanalytik BioMedical Center Faculty of Medicine Ludwig-Maximilians-University of Munich
contact email	imhof@lmu.de
lab head
Bo Sun
contact affiliation	LMU
contact email	dpSWATH.sun@gmail.com
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2023/01/PXD038407

PRIDE project URI

• PRIDE
  1. PXD038407
     1. Label: PRIDE project
     2. Name: Improving SWATH-MS analysis by Deep-learning