Testing and Validation of Computational Methods for Mass Spectrometry.
Testing and Validation of Computational Methods for Mass Spectrometry.
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DOI：
10.1021/acs.jproteome.5b00852
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发表时间：
2016-03-04
影响因子：
4.4
通讯作者：
Beyer A
中科院分区：
文献类型：
--
作者：
Gatto L;Hansen KD;Hoopmann MR;Hermjakob H;Kohlbacher O;Beyer A
基于质谱的高通量方法（蛋白质组学、代谢组学、脂质组学等）产生大量数据，如果没有计算方法就无法进行分析。方法选择对生物学研究总体结果的影响常常被低估，但不同的方法可能会导致截然不同的生物学发现。因此，评估和比较计算方法的正确性和相对性能至关重要。数据量以及算法的复杂性使得公正的比较具有挑战性。本文讨论了计算方法测试和验证中的一些问题和挑战。我们讨论不同类型的数据（模拟和实验验证数据）以及用于比较方法的不同指标。我们还引入了一个新的质谱参考数据集公共存储库 (http://compms.org/RefData)，其中包含一系列公开可用的数据集，用于对各种不同方法进行性能评估。
High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets (http://compms.org/RefData) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.