An adaptive classification model for peptide identification.
BMC Genomics
; 16 Suppl 11: S1, 2015.
Article
em En
| MEDLINE
| ID: mdl-26578406
ABSTRACT
BACKGROUND:
Peptide sequence assignment is the central task in protein identification with MS/MS-based strategies. Although a number of post-database search algorithms for filtering target peptide spectrum matches (PSMs) have been developed, the discrepancy among the output PSMs is usually significant, remaining a few disputable PSMs. Current studies show that a number of target PSMs which are close to decoy PSMs can hardly be separated from those decoys by only using the discrimination function.RESULTS:
In this paper, we assign each target PSM a weight showing its possibility of being correct. We employ a SVM-based learning model to search the optimal weight for each target PSM and develop a new score system, CRanker, to rank all target PSMs. Due to the large PSM datasets generated in routine database searches, we use the Cholesky factorization technique for storing a kernel matrix to reduce the memory requirement.CONCLUSIONS:
Compared with PeptideProphet and Percolator, CRanker has identified more PSMs under similar false discover rates over different datasets. CRanker has shown consistent performance on different test sets, validated the reasonability the proposed model.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Peptídeos
/
Biologia Computacional
/
Máquina de Vetores de Suporte
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article