PXD010382-1

PXD010382 is an original dataset announced via ProteomeXchange.

Title	High quality MS/MS spectrum prediction significantly improves data-dependent and data-independent acquisition data analysis
Description	Peptide fragmentation spectra are routinely predicted in the interpretation of mass spectrometry-based proteomics data. Unfortunately, the generation of fragment ions is not well enough understood to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of the measurements. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a significant increase in the total number of peptide identifications at fixed false discovery rate. In the latter case we demonstrate that the use of predicted MS/MS spectra is equivalent to the use of spectra from experimentallibraries, indicating that fragmentation libraries for proteomics are becoming obsolete.
HostingRepository	PRIDE
AnnounceDate	2019-03-13
AnnouncementXML	Submission_2019-03-13_03:01:19.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Shivani Tiwary
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	monohydroxylated residue; iodoacetamide derivatized residue
Instrument	Q Exactive

Revision	Datetime	Status	ChangeLog Entry
0	2018-07-11 06:49:48	ID requested
⏵ 1	2019-03-13 03:01:20	announced
2	2019-04-23 08:38:28	announced	Updated project metadata.
3	2019-05-29 04:06:27	announced	Updated project metadata.
4	2024-10-22 04:52:04	announced	2024-10-22: Updated project metadata.

Dataset with its publication pending

curator keyword: Technical, Biological

submitter keyword: Deep learning, Machine learning , Intensity prediction, DIA, Andromeda

Juergen Cox; Peter Cimermancic
contact affiliation	Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany; Verily Life Sciences, 269 E Grand Ave, South San Francisco, CA 94080, USA
contact email	cox@biochem.mpg.de
lab head
Shivani Tiwary
contact affiliation	Max Planck Institute of Biochemistry
contact email	shivani@biochem.mpg.de
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/2019/03/PXD010382

PRIDE project URI

• PRIDE
  1. PXD010382
     1. Label: PRIDE project
     2. Name: High quality MS/MS spectrum prediction significantly improves data-dependent and data-independent acquisition data analysis