Locus-specific Retention Predictor (LsRP)
Ontology highlight
ABSTRACT:
INSTRUMENT(S): TripleTOF 5600
ORGANISM(S): Homo Sapiens (human)
TISSUE(S): Hela Cell
SUBMITTER: 
Wenyuan Lu
LAB HEAD: Pengyuan Yang
PROVIDER: PXD005572 | Pride | 2017-04-10
REPOSITORIES: Pride
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Code.rar | Other | |||
| ExampleData.rar | Other | |||
| LocusVectors.rar | Other | |||
| hela-1.5ug-120.dat | Other | |||
| hela-1.5ug-120.wiff | Wiff |
Items per page: 5 1 - 5 of 27 |
Publications
Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics.
Lu Wenyuan W Liu Xiaohui X Liu Shanshan S Cao Weiqian W Zhang Yang Y Yang Pengyuan P
Scientific reports 20170317
The precision prediction of peptide retention time (RT) plays an increasingly important role in liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics. Owing to the high reproducibility of liquid chromatography, RT prediction provides promising information for both identification and quantification experiment design. In this work, we present a Locus-specific Retention Predictor (LsRP) for precise prediction of peptide RT, which is based on amino acid locus information and Sup ...[more]
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Project description:The precision prediction of peptide retention time (RT) plays an increasingly important role in liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics. Owing to the high reproducibility of liquid chromatography, RT prediction provides promising information for both identification and quantification experiment design. In this work, we present a Locus-specific Retention Predictor (LsRP) for precise prediction of peptide RT, which is based on amino acid locus information and Support Vector Regression (SVR) algorithm. Corresponding to amino acid locus, each peptide sequence was converted to a featured locus vector consisting of zeros and ones. With locus vector information from LC-MS/MS data sets, an SVR computational process was trained and evaluated. LsRP finally provided a prediction correlation coefficient of 0.95~0.99. We compared our method with two common predictors. Results showed that LsRP outperforms these methods and tracked up to 30% extra peptides in an extraction RT window of 2 min. A new strategy by combining LsRP and calibration peptide approach was then proposed, which open up new opportunities for precision proteomics.
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