High-accuracy peak picking of proteomics data using wavelet techniques.
High-accuracy peak picking of proteomics data using wavelet techniques.
复制标题
DOI：
10.1142/9789812701626_0023
复制
发表时间：
2006-01-01
影响因子：
--
通讯作者：
Hildebrandt, Andreas
中科院分区：
文献类型：
--
作者：
Lange, Eva;Gropl, Clemens;Hildebrandt, Andreas
提出了一种新的质谱（MS）数据分析的选峰算法。它独立于潜在的机器或电离方法，并且能够解决高度复杂和不对称的信号。该方法利用光谱数据的多尺度特性，首先检测小波变换信号中的质量峰，然后将给定的非对称峰函数拟合到原始数据中。在可选的第三阶段，可以使用非线性优化技术进一步改进拟合结果。与现有的技术（如SNAP， Apex）相比，我们的算法能够在低分辨率的ESI-MS数据中分离出多个带电肽的重叠峰。相对于峰值位置的精度提高使其成为一种有价值的基于质谱的鉴定和定量实验的预处理方法。该方法已经在许多不同的带注释的测试用例上进行了验证，在运行时间和准确性方面，它与当前建立的技术相比都具有优势。该算法的实现可以在我们的开源框架OpenMS中免费获得。
A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. It is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks of multiply charged peptides in ESI-MS data of low resolution. Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification and quantification experiments. The method has been validated on a number of different annotated test cases, where it compares favorably in both runtime and accuracy with currently established techniques. An implementation of the algorithm is freely available in our open source framework OpenMS.