Methods for peptide identification by spectral comparison.

Methods for peptide identification by spectral comparison.
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DOI:
10.1186/1477-5956-5-3
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发表时间:
2007-01-16
期刊:
影响因子:
2
通讯作者:
Kearney RE
Kearney RE
中科院分区:
生物学4区
文献类型:
--
作者:
Liu J;Bell AW;Bergeron JJ;Yanofsky CM;Carrillo B;Beaudrie CE;Kearney RE

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串联质谱分析和数据库搜索是目前鸟枪法蛋白质组学实验中肽测序的主要技术。大多数方法将实验观察到的光谱与根据蛋白质数据库中的序列预测的理论光谱进行比较。然而,人们越来越有兴趣将未知的实验光谱与先前识别的光谱库进行比较。该方法的优点是考虑了仪器相关因素和片段概率中肽特异性差异。对于高通量蛋白质组学研究来说,它的计算效率也更高。本文研究了与这种光谱比较方法相关的计算问题。不同的方法已经在几组大的光谱上进行了经验评估。首先,我们说明峰值强度遵循泊松分布。这意味着应用平方根变换将最佳地稳定峰值强度方差。我们的结果表明,平方根确实优于其他变换,从而提高了光谱匹配的准确性。其次,比较了不同的光谱相似性度量,结果表明相关系数是最稳健的。最后,我们研究如何组装与同一肽相关的多个光谱以生成合成参考光谱。事实证明,整体平均可以提供准确性和效率的最佳组合。我们的结果表明,结合使用这些方法可以提高光谱比较的灵敏度和特异性。因此,它们能够增强和补充现有工具,以实现一致和准确的肽鉴定。
Tandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies. This paper investigates computational issues related to this spectral comparison approach. Different methods have been empirically evaluated over several large sets of spectra. First, we illustrate that the peak intensities follow a Poisson distribution. This implies that applying a square root transform will optimally stabilize the peak intensity variance. Our results show that the square root did indeed outperform other transforms, resulting in improved accuracy of spectral matching. Second, different measures of spectral similarity were compared, and the results illustrated that the correlation coefficient was most robust. Finally, we examine how to assemble multiple spectra associated with the same peptide to generate a synthetic reference spectrum. Ensemble averaging is shown to provide the best combination of accuracy and efficiency. Our results demonstrate that when combined, these methods can boost the sensitivity and specificity of spectral comparison. Therefore they are capable of enhancing and complementing existing tools for consistent and accurate peptide identification.
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