PXD040721-1

PXD040721 is an original dataset announced via ProteomeXchange.

Title	Merging full-spectrum and fragment ion intensity predictions from deep learning for high quality spectral libraries
Description	In this work, we present a deep learning based full-spectrum prediction method and demonstrate the merits of using full-spectrum predicted approaches for library searching. Our proposed model provides flexibility to accommodate post-translational modifications and fills the current gap for long peptide predictions.
HostingRepository	PRIDE
AnnounceDate	2023-06-28
AnnouncementXML	Submission_2023-06-28_12:19:16.600.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Chak Ming JerryChan
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	LTQ Orbitrap Velos

Revision	Datetime	Status	ChangeLog Entry
0	2023-03-09 06:10:02	ID requested
⏵ 1	2023-06-28 12:19:16	announced

Dataset with its publication pending

submitter keyword: Spectral library searching, deep learning

HenryLam
contact affiliation	Chemical and Biological Engineering Department, Hong Kong University of Science and Technology
contact email	kehlam@ust.hk
lab head
Chak Ming JerryChan
contact affiliation	Hong Kong University of Science and Technology
contact email	jerrychancm@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/2023/06/PXD040721

PRIDE project URI

• PRIDE
  1. PXD040721
     1. Label: PRIDE project
     2. Name: Merging full-spectrum and fragment ion intensity predictions from deep learning for high quality spectral libraries