Grossmann Jonas, Roos Franz F, Cieliebak Mark, Lipták Zsuzsanna, Mathis Lucas K, Müller Matthias, Gruissem Wilhelm, Baginsky Sacha
Institute of Plant Sciences, Swiss Federal Institute of Technology, ETH Zentrum, CH-8092 Zürich, Switzerland.
J Proteome Res. 2005 Sep-Oct;4(5):1768-74. doi: 10.1021/pr050070a.
We present AUDENS, a new platform-independent open source tool for automated de novo sequencing of peptides from MS/MS data. We implemented a dynamic programming algorithm and combined it with a flexible preprocessing module which is designed to distinguish between signal and other peaks. By applying a user-defined set of heuristics, AUDENS screens through the spectrum and assigns high relevance values to putative signal peaks. The algorithm constructs a sequence path through the MS/MS spectrum using the peak relevances to score each suggested sequence path, i.e., the corresponding amino acid sequence. At present, we consider AUDENS a prototype that unfolds its biggest potential if used in parallel with other de novo sequencing tools. AUDENS is available open source and can be downloaded with further documentation at http://www.ti.inf.ethz.ch/pw/software/audens/ .
我们展示了AUDENS，这是一种全新的、独立于平台的开源工具，用于从MS/MS数据中自动进行肽段的从头测序。我们实现了一种动态规划算法，并将其与一个灵活的预处理模块相结合，该模块旨在区分信号峰和其他峰。通过应用一组用户定义的启发式方法，AUDENS对质谱图进行筛选，并为假定的信号峰赋予高相关性值。该算法利用峰相关性构建一条贯穿MS/MS质谱图的序列路径，以对每个建议的序列路径（即相应的氨基酸序列）进行评分。目前，我们认为AUDENS是一个原型，如果与其他从头测序工具并行使用，它将发挥出最大潜力。AUDENS是开源的，可在http://www.ti.inf.ethz.ch/pw/software/audens/ 下载，并附有更多文档。
