PXD005005 is an
original dataset announced via ProteomeXchange.
Dataset Summary
Title | Split-BioID - Split-BioID: a conditional proteomics approach for high resolution proteomics |
Description | Split-BioID: a conditional proteomics approach for high resolution proteomics |
HostingRepository | PRIDE |
AnnounceDate | 2020-03-12 |
AnnouncementXML | Submission_2020-03-12_03:41:20.xml |
DigitalObjectIdentifier | |
ReviewLevel | Peer-reviewed dataset |
DatasetOrigin | Original dataset |
RepositorySupport | Unsupported dataset by repository |
PrimarySubmitter | Julien B??thune |
SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: 9606; |
ModificationList | biotinylated residue |
Instrument | LTQ Orbitrap Velos |
Dataset History
Revision | Datetime | Status | ChangeLog Entry |
0 | 2016-09-20 04:51:14 | ID requested | |
1 | 2017-06-01 06:11:33 | announced | |
⏵ 2 | 2020-03-12 03:41:22 | announced | 2020-03-12: Updated project metadata. |
Publication List
Dataset with its publication pending |
Keyword List
curator keyword: Technical |
submitter keyword: HeLa cells, LTQ Orbitrap |
Contact List
Julien B??thune |
contact affiliation | Heidelberg University Biochemistry Center |
contact email | julien.bethune@bzh.uni-heidelberg.de |
lab head | |
Julien B??thune |
contact affiliation | Heidelberg University Biochemistry Center |
contact email | julien.bethune@bzh.uni-heidelberg.de |
dataset submitter | |
Full Dataset Link List
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/2017/06/PXD005005 |
PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD005005
- Label: PRIDE project
- Name: Split-BioID - Split-BioID: a conditional proteomics approach for high resolution proteomics