Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data
Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data
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DOI：
10.1093/bioinformatics/bth242
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发表时间：
2004-09-22
期刊：
影响因子：
5.8
通讯作者：
Jarman, KH
中科院分区：
文献类型：
--
作者：
Heredia-Langner, A;Cannon, WR;Jarman, KH
动机：串联质谱法（MS/MS）后的肽鉴定通常通过搜索未鉴定肽的质谱与序列数据库中肽产生的模型谱之间的最佳匹配来实现。只有当所研究的肽属于可用的数据库时，这种方法才是成功的。我们的目标是开发和测试一种启发式优化算法的性能，该算法能够处理实际MS/MS谱中常见的一些特征，这些特征往往会阻止更简单的确定性解决方案。我们提出了一种遗传算法（GA）的实现在重建的氨基酸序列，只使用光谱特征，讨论与这种方法相关的一些问题，并将其性能与从头测序方法进行比较。GA可以潜在地克服与真实的MS/MS数据的从头分析相关的一些最有问题的方面，例如缺失或不清楚定义的峰，并且可能被证明是蛋白质组学领域中的有价值的工具。我们评估我们的算法的性能，在完美的光谱信息的条件下，在关键的光谱特征缺失的情况下，并使用真实的MS/MS光谱数据。
Motivation: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches.Results: We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.