An adaptive classification model for peptide identification

Abstract

BackgroundPeptide 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.ResultsIn 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.ConclusionsCompared 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.

A cost-sensitive online learning method for peptide identification

“…CRanker [14] cast identification of target PSM as a classification problem. Let = {x i , y i } n i=1 ⊆ R q × {−1, 1} be a set of n PSMs, where x i ∈ R q represents its i-th PSM record with q attributes, and y i ∈ {1, −1} is the corresponding label indicating a target or decoy PSM.…”

“…While class labels in a standard classification problem are all trustworthy, a large number of "+1" labels in PSM identification are not correct. CRanker [14] introduced weight θ i ∈[ 0, 1] for each PSM sample (x i , y i ) to indicate the degree of the reliability of the label y i . Particularly, θ i = 1 indicates that label y i is definitely correct, θ i = 0 indicates that it is definitely incorrect, and θ i ∈ (0, 1) indicates that label y i is probably correct.…”

“…After discriminant functionf :f (x) = j∈S α j k(x j , x), where k(•) is the selected kernel function, is trained, we calculate the scores of all PSMs on both training and test sets. The score of PSM (x i , y i ) is defined in [14]:…”

“…In supervised learning, kernel functions have been widely used to map data from the sample space to high dimensional spaces where data with non-linear relationships can be classified by linear models. With the kernelbased support vector machine (SVM), CRanker [14] has shown significantly better performance than linear models. Although kernel-based post-database searching approaches have improved the accuracy of peptide identification, two big challenges remain in practical implementation of kernel-based methods:…”

“…In this work, we develop an online classification algorithm to tackle the two challenges in kernel-based methods. For the challenge of "hard dataset", we extend CRanker [14] model to a cost-sensitive Ranker (CS-Ranker) by using different loss functions for decoy and target PSMs respectively. The CS-Ranker model gives a larger penalty for wrongly selecting decoy PSMs than that for target PSMs, which reduces the model's false discovery rate while increases its true positive rate.…”