Binomial probability distribution model-based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information.

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .

Dispec: a novel peptide scoring algorithm based on peptide matching discriminability.

Identifying peptides from the fragmentation spectra is a fundamental step in mass spectrometry (MS) data processing. The significance (discriminability) of every peak varies, providing additional information for potentially enhancing the identification sensitivity and the correct match rate. However this important information was not considered in previous algorithms. Here we presented a novel method based on Peptide Matching Discriminability (PMD), in which the PMD information of every peak reflects the discriminability of candidate peptides. In addition, we developed a novel peptide scoring algorithm Dispec based on PMD, by taking three aspects of discriminability into consideration: PMD, intensity discriminability and m/z error discriminability. Compared with Mascot and Sequest, Dispec identified remarkably more peptides from three experimental datasets with the same confidence at 1% PSM-level FDR. Dispec is also robust and versatile for various datasets obtained on different instruments. The concept of discriminability enhances the peptide identification and thus may contribute largely to the proteome studies. As an open-source program, Dispec is freely available at http://bioinformatics.jnu.edu.cn/software/dispec/.