PXD005206

PXD005206 is an original dataset announced via ProteomeXchange.

Title	Stop trashing your spectra! Use a “Quantify then Identify” pipeline based on machine learning to maximize your isobaric tagging data
Description	Being inspired by metabolomic data processing, we have developed a bioinformatic pipeline that optimizes the processing of mass spectral data obtained from isobaric Tandem Mass Tag (TMT) experiments. Our method focuses on the tandem mass spectral level by first quantifying and then identifying (QtI), while preserving unidentified spectra for further investigations. The raw datasets were previously generated [1, 2]. Two-proteome model experiments were considered where identical pools of human CSF or plasma samples were mixed with E. coli samples at different concentrations. E. coli protein extract was spiked in 400 µL of CSF at amounts of 0, 2, 3, 5, 6.25, and 7.5 µg. Such sets of 6 spiked CSF samples were prepared in triplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on two independent but identical LC MS/MS, for a total of 18 raw files [1]; this experiment is called “CSF-E.coli”. E. coli protein extract was spiked at 0, 2.5, 5, 6.25, 12.5, and 25 µg in 30 µL human plasma. Such sets of 6 spiked plasma samples were prepared in quadruplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on one LC MS/MS, for a total of 12 raw files [2]; this experiment is referred as “Plasma-E.coli”.The so-called “96samples-CSF” experiment consists of 16 replicate TMT sixplex experiments measuring identical CSF samples from the pool described above [2], analyzed in triplicates on one LCMS/MS for a total of 48 raw files. The so-called “96samples-plasma” experiment consists of 16 replicate TMT sixplex experiments measuring identical plasma samples from the pool described before, analyzed in triplicates on one LC MS/MS for a total of 48 raw files [1]. References: [1] Dayon, L., Núñez Galindo, A., Corthésy, J., Cominetti, O. & Kussmann, M. Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J. Proteome Res. 13, 3837-3845 (2014). [2] Núñez Galindo, A., Kussmann, M. & Dayon, L. Proteomics of Cerebrospinal Fluid: Throughput and Robustness Using a Scalable Automated Analysis Pipeline for Biomarker Discovery. Anal. Chem. 87, 10755-10761 (2015).
HostingRepository	PRIDE
AnnounceDate	2018-05-01
AnnouncementXML	Submission_2018-05-01_02:48:51.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	John Corthésy
SpeciesList	scientific name: Escherichia coli; NCBI TaxID: 562; scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	TMT6plex-126 reporter+balance reagent acylated residue; iodoacetamide derivatized residue
Instrument	LTQ Orbitrap Elite

Revision	Datetime	Status	ChangeLog Entry
0	2016-10-24 00:50:02	ID requested
⏵ 1	2018-05-01 02:48:52	announced

Corth, é, sy J, Theofilatos K, Mavroudi S, Macron C, Cominetti O, Remlawi M, Ferraro F, N, ú, ñ, ez Galindo A, Kussmann M, Likothanassis S, Dayon L, An Adaptive Pipeline To Maximize Isobaric Tagging Data in Large-Scale MS-Based Proteomics. J Proteome Res, 17(6):2165-2173(2018) [pubmed]

curator keyword: Technical

submitter keyword: Plasma, CSF, Isobaric tagging, Human, TMT, 6-plex, Orbitrap, LC-MS

Loïc Dayon
contact affiliation	Proteomics Team, Nestlé Institute of Health Sciences, Lausanne, Switzerland
contact email	loic.dayon@rd.nestle.com
lab head
John Corthésy
contact affiliation	Nestlé Institute of Health Sciences
contact email	john.corthesy@rd.nestle.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/2018/05/PXD005206

PRIDE project URI

• PRIDE
  1. PXD005206
     1. Label: PRIDE project
     2. Name: Stop trashing your spectra! Use a “Quantify then Identify” pipeline based on machine learning to maximize your isobaric tagging data