Smirnov Aleksandr, Liao Yunfei, Du Xiuxia
Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Metabolites. 2022 May 29;12(6):491. doi: 10.3390/metabo12060491.
The number of metabolomics studies and spectral libraries for compound annotation (i.e., assigning possible compound identities to a fragmentation spectrum) has been growing steadily in recent years. Accompanying this growth is the number of mass spectra available for searching through those libraries. As the size of spectral libraries grows, accurate and fast compound annotation becomes more challenging. We herein report a prescreening algorithm that was developed to address the speed of spectral search under the constraint of low memory requirements. This prescreening has been incorporated into the Automated Data Analysis Pipeline Spectral Knowledgebase (ADAP-KDB) and can be applied to compound annotation by searching other spectral libraries as well. Performance of the prescreening algorithm was evaluated for different sets of parameters and compared to the original ADAP-KDB spectral search and the MSSearch software. The comparison has demonstrated that the new algorithm is about four-times faster than the original when searching for low-resolution mass spectra, and about as fast as the original when searching for high-resolution mass spectra. However, the new algorithm is still slower than MSSearch due to the relational database design of the former. The new search workflow can be tried out at the ADAP-KDB web portal.
近年来，用于化合物注释（即给碎片谱图确定可能的化合物身份）的代谢组学研究数量和光谱库一直在稳步增长。随着这种增长，可用于在这些库中进行搜索的质谱数量也在增加。随着光谱库规模的扩大，准确且快速的化合物注释变得更具挑战性。我们在此报告一种预筛选算法，该算法是为在低内存需求的约束下解决光谱搜索速度问题而开发的。这种预筛选已被纳入自动数据分析管道光谱知识库（ADAP-KDB），并且通过搜索其他光谱库也可应用于化合物注释。针对不同参数集对预筛选算法的性能进行了评估，并与原始的ADAP-KDB光谱搜索和MSSearch软件进行了比较。比较结果表明，在搜索低分辨率质谱时，新算法比原始算法快约四倍，在搜索高分辨率质谱时与原始算法速度相当。然而，由于新算法的关系数据库设计，它仍然比MSSearch慢。新的搜索工作流程可在ADAP-KDB门户网站上试用。
