Quality control metrics for LC-MS feature detection tools demonstrated in yeast
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Fourier Transform Ion Cyclotron Resonance Mass Spectrometer, instrument model
ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast)
SUBMITTER: 
Attila Csordas
PROVIDER: PRD000464 | Pride | 2012-07-26
REPOSITORIES: Pride
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| PRIDE_Exp_Complete_Ac_17916.pride.mgf.gz | Mgf | |||
| PRIDE_Exp_Complete_Ac_17916.pride.mztab.gz | Mztab | |||
| PRIDE_Exp_Complete_Ac_17916.xml.gz | Xml |
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Publications
Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.
Piening Brian D BD Wang Pei P Bangur Chaitanya S CS Whiteaker Jeffrey J Zhang Heidi H Feng Li-Chia LC Keane John F JF Eng Jimmy K JK Tang Hua H Prakash Amol A McIntosh Martin W MW Paulovich Amanda A
Journal of proteome research 20060701 7
Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Beca ...[more]
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