The Experts below are selected from a list of 7317 Experts worldwide ranked by ideXlab platform

Gong Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Binomial Probability Distribution model based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information
    Journal of Proteome Research, 2013
    Co-Authors: Chuanle Xiao, Xiaozhou Chen, Xuesong Sun, Gong Zhang
    Abstract:

    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 ...

Xiaozhou Chen - One of the best experts on this subject based on the ideXlab platform.

  • a protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mrna into a Binomial Probability scoring model
    Journal of Proteomics, 2019
    Co-Authors: Zhaoyu Liu, Xiaozhou Chen, Zhenliang Lin, Zhongbing Zheng, Weiguo Miao, Shangqian Xie
    Abstract:

    Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior Probability of protein identification and integrated to re-score in PSM using the Binomial Probability Distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.

  • Binomial Probability Distribution model based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information
    Journal of Proteome Research, 2013
    Co-Authors: Chuanle Xiao, Xiaozhou Chen, Xuesong Sun, Gong Zhang
    Abstract:

    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 ...

Shangqian Xie - One of the best experts on this subject based on the ideXlab platform.

  • a protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mrna into a Binomial Probability scoring model
    Journal of Proteomics, 2019
    Co-Authors: Zhaoyu Liu, Xiaozhou Chen, Zhenliang Lin, Zhongbing Zheng, Weiguo Miao, Shangqian Xie
    Abstract:

    Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior Probability of protein identification and integrated to re-score in PSM using the Binomial Probability Distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.

Chuanle Xiao - One of the best experts on this subject based on the ideXlab platform.

  • Binomial Probability Distribution model based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information
    Journal of Proteome Research, 2013
    Co-Authors: Chuanle Xiao, Xiaozhou Chen, Xuesong Sun, Gong Zhang
    Abstract:

    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 ...

Zhaoyu Liu - One of the best experts on this subject based on the ideXlab platform.

  • a protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mrna into a Binomial Probability scoring model
    Journal of Proteomics, 2019
    Co-Authors: Zhaoyu Liu, Xiaozhou Chen, Zhenliang Lin, Zhongbing Zheng, Weiguo Miao, Shangqian Xie
    Abstract:

    Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior Probability of protein identification and integrated to re-score in PSM using the Binomial Probability Distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.