School of Informatics and Computing, Indiana University, Bloomington, IN, 47405, USA.
Proteomics. 2020 Nov;20(21-22):e2000002. doi: 10.1002/pmic.202000002. Epub 2020 Jun 29.
With the accumulation of MS/MS spectra collected in spectral libraries, the spectral library searching approach emerges as an important approach for peptide identification in proteomics, complementary to the commonly used protein database searching approach, in particular for the proteomic analyses of well-studied model organisms, such as human. Existing spectral library searching algorithms compare a query MS/MS spectrum with each spectrum in the library with matched precursor mass and charge state, which may become computationally intensive with the rapidly growing library size. Here, the software msSLASH, which implements a fast spectral library searching algorithm based on the Locality-Sensitive Hashing (LSH) technique, is presented. The algorithm first converts the library and query spectra into bit-strings using LSH functions, and then computes the similarity between the spectra with highly similar bit-string. Using the spectral library searching of large real-world MS/MS spectra datasets, it is demonstrated that the algorithm significantly reduced the number of spectral comparisons, and as a result, achieved 2-9X speedup in comparison with existing spectral library searching algorithm SpectraST. The spectral searching algorithm is implemented in C/C++, and is ready to be used in proteomic data analyses.
随着在光谱库中积累的 MS/MS 光谱数量的增加，光谱库搜索方法作为蛋白质组学中肽鉴定的一种重要方法，与常用的蛋白质数据库搜索方法相辅相成，特别是对于研究良好的模式生物（如人类）的蛋白质组学分析。现有的光谱库搜索算法将查询 MS/MS 光谱与库中每个具有匹配前体质量和电荷状态的光谱进行比较，随着库规模的快速增长，这可能会变得计算密集。这里介绍了一种名为 msSLASH 的软件，它实现了一种基于局部敏感哈希（LSH）技术的快速光谱库搜索算法。该算法首先使用 LSH 函数将库和查询光谱转换为位字符串，然后使用高度相似的位字符串计算光谱之间的相似性。通过对大型真实世界 MS/MS 光谱数据集的光谱搜索，证明该算法显著减少了光谱比较的数量，与现有的光谱库搜索算法 SpectraST 相比，速度提高了 2-9 倍。光谱搜索算法是用 C/C++ 实现的，准备用于蛋白质组学数据分析。
