Dataset Information

Drosophila melanogaster exp 132

ABSTRACT: PeptideRank is an approach that uses a rank-based algorithm for peptide detectability prediction from shotgun proteomics data. The best performance is achieved when it is trained on organism-specific shotgun proteomics datasets. The Drosophila shotgun proteomics data presented here have been used amongst others to train and validate PeptideRank.

INSTRUMENT(S): LTQ

ORGANISM(S): Drosophila Melanogaster (fruit Fly)

TISSUE(S): Cell Culture

SUBMITTER: Sandra Goetze

LAB HEAD: Erich Brunner

PROVIDER: PXD000730 | Pride | 2019-06-04

REPOSITORIES: pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20070903_KcNuc_Subfr_2_14_16.RAW	Raw
	20070903_KcNuc_Subfr_2_17_18.RAW	Raw
	20070903_KcNuc_Subfr_2_19_20.RAW	Raw
	20070903_KcNuc_Subfr_2_21.RAW	Raw
	20070903_KcNuc_Subfr_2_22.RAW	Raw

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Publications

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

Qeli Ermir E Omasits Ulrich U Goetze Sandra S Stekhoven Daniel J DJ Frey Juerg E JE Basler Konrad K Wollscheid Bernd B Brunner Erich E Ahrens Christian H CH

Journal of proteomics 20140527

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with ...[more]

PMID: 24878426

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