Antilope -一种新的肽序列排序问题的拉格朗日松弛方法
Antilope - A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem
摘要：
从质谱数据中进行肽段测序是蛋白质组研究中的关键步骤。尤其是从头测序，即仅从其光谱中识别肽，即使对于最先进的算法方法也仍然是一个挑战。在本文中，我们提出了羚羊，一个新的快速和灵活的方法的基础上数学规划。它建立在频谱图模型的基础上，并与各种评分方案一起工作。安蒂洛普结合拉格朗日松弛求解整数线性规划公式与适应严的k最短路径算法。与混合整数优化相比，它在运行时间上有了显着的改进，并且与其他最先进的工具具有相同的速度。我们还实现了一个通用的概率评分方案，可以自动训练的数据集的注释谱，是独立的质谱仪类型。对基准数据的评估表明，Antilope在运行时间和准确性方面与流行的最先进的程序PepNovo和NovoHMM都具有竞争力。此外，它在所考虑的离子类型的数量方面提供了更大的灵活性。Antilope将作为开源蛋白质组学图书馆OpenMS的一部分免费提供。
英文摘要：
Peptide sequencing from mass spectrometry data is a key step in proteome research. Especially de novo sequencing, the identification of a peptide from its spectrum alone, is still a challenge even for state-of-the-art algorithmic approaches. In this paper we present Antilope, a new fast and flexible approach based on mathematical programming. It builds on the spectrum graph model and works with a variety of scoring schemes. Antilope combines Lagrangian relaxation for solving an integer linear programming formulation with an adaptation of Yen's k shortest paths algorithm. It shows a significant improvement in running time compared to mixed integer optimization and performs at the same speed like other state-of-the-art tools. We also implemented a generic probabilistic scoring scheme that can be trained automatically for a dataset of annotated spectra and is independent of the mass spectrometer type. Evaluations on benchmark data show that Antilope is competitive to the popular state-of-the-art programs PepNovo and NovoHMM both in terms of run time and accuracy. Furthermore, it offers increased flexibility in the number of considered ion types. Antilope will be freely available as part of the open source proteomics library OpenMS.
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