FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics.
FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics.
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FineFDR：元蛋白质组学中细粒度分类特异性错误发现率控制。
DOI：
10.1109/bibm55620.2022.9995401
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Guo,Xuan
中科院分区：
文献类型：
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作者：
Wang,Shengze;Feng,Shichao;Pan,Chongle;Guo,Xuan
微生物群落蛋白质组学，也称为元蛋白质组学，研究由微生物群表达的所有蛋白质。串联质谱（MS/MS）是元蛋白质组学中鉴定蛋白质的典型方法，其涉及根据蛋白质序列数据库搜索质谱。主要的后分析步骤是控制错误发现率（FDR），即，误报与注释总数的比率。目前流行的目标诱饵FDR估计方法平等对待所有的肽和蛋白质，忽略了它们可能具有不同的被识别的概率。在这项研究中，我们报告FineFDR，一个框架FDR评估在细粒度1级与分类信息考虑。FineFDR将识别的肽谱匹配、肽和来自不同分类单位的蛋白质分组，并分别估计每组中的FDR。在模拟和真实数据集上的经验实验表明，与Comet，Percolator，TIDD和Tailor等最先进的方法相比，我们的FineFDR实现了更高的精度和更多的肽和蛋白质识别。FineFDR在GNU GPL许可证下可在https://github.com/Biocomputing-Research-Group/FDR上免费获得。
Microbial community proteomics, also termed metaproteomics, investigates all proteins expressed by a microbiota. Tandem mass spectrometry (MS/MS) is the typical method for identifying proteins in metaproteomics, which involves searching the mass spectra against a protein sequence database. A major post-analysis step is controlling the false discovery rate (FDR), i.e., the ratio of false positives to the total number of annotations. The current popular target-decoy FDR estimation method treats all the peptides and proteins equally and overlooks that they could have varied probabilities of being identified. In this study, wer eport FineFDR, a framework for FDR assessment at fine-grained 1 evels with taxonomy information considered. FineFDR groups the identified peptide-spectrum matches, peptides, and proteins from different taxonomic units and estimates the FDR in each group separately. Empirical experiments on the simulated and real-world data sets demonstrate that our FineFDR achieved higher precision and more peptide and protein identifications when compared to the state-of-the-art methods, such as Comet, Percolator, TIDD, and Tailor. FineFDR is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/FDR.