VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.
Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium.
J Proteome Res. 2022 May 6;21(5):1365-1370. doi: 10.1021/acs.jproteome.2c00075. Epub 2022 Apr 21.
Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we investigate the effects of integrating the machine learning-based postprocessor Percolator into our spectral library searching tool COSS (CompOmics Spectral library Searching tool). To evaluate the effects of this postprocessing, we have used 40 data sets from 2 different projects and have searched these against the NIST and MassIVE spectral libraries. The searching is carried out using 2 spectral library search tools, COSS and MSPepSearch with and without Percolator postprocessing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step to COSS is effective and results in a substantial improvement in sensitivity and specificity of the identifications. COSS is freely available as open source under the permissive Apache2 license, and binaries and source code are found at https://github.com/compomics/COSS.
在从质谱数据中鉴定肽时，保持高灵敏度同时限制假阳性是一个关键挑战。在这里，我们研究了将基于机器学习的后处理器 Percolator 集成到我们的光谱库搜索工具 COSS（Compo mics 光谱库搜索工具）中的效果。为了评估这种后处理的效果，我们使用了来自 2 个不同项目的 40 个数据集，并将这些数据集与 NIST 和 MassIVE 光谱库进行了搜索。搜索使用了 2 个光谱库搜索工具 COSS 和 MSPepSearch，以及带有和不带有 Percolator 后处理的工具，并使用序列数据库搜索引擎 MS-GF+作为基线比较器。将 Percolator 重新评分步骤添加到 COSS 中是有效的，并且导致鉴定的灵敏度和特异性有了实质性的提高。COSS 是作为 Apache2 许可下的免费开源软件提供的，二进制文件和源代码可在 https://github.com/compomics/COSS 上找到。
