Benchmarking mass spectrometry based proteomics algorithms using a simulated database

被引：0
作者：
Muaaz Gul Awan
Abdullah Gul Awan
Fahad Saeed
机构：
[1] Lawrence Berkeley National Laboratory,Al
[2] University of Engineering & Technology (UET),Khwarizmi Institute of Computer Science (KICS)
[3] Florida International University,School of Computing and Information Sciences
来源：
Network Modeling Analysis in Health Informatics and Bioinformatics | 2021年 / 10卷
关键词：
Benchmarking; Peptide search algorithms; Proteomics; Mass-spectrometry;
D O I：
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学科分类号：
摘要：
Protein sequencing algorithms process data from a variety of instruments that has been generated under diverse experimental conditions. Currently there is no way to predict the accuracy of an algorithm for a given data set. Most of the published algorithms and associated software has been evaluated on limited number of experimental data sets. However, these performance evaluations do not cover the complete search space the algorithm and the software might encounter in real-world. To this end, we present a database of simulated spectra that can be used to benchmark any spectra to peptide search engine. We demonstrate the usability of this database by bench marking two popular peptide sequencing engines. We show wide variation in the accuracy of peptide deductions and a complete quality profile of a given algorithm can be useful for practitioners and algorithm developers. All benchmarking data is available at https://users.cs.fiu.edu/~fsaeed/Benchmark.html
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