Department of Electronics and Computer Engineering and.
Department of Computer Science and Engineering, Hanyang University, Seoul, Korea.
Bioinformatics. 2015 Dec 15;31(24):4026-8. doi: 10.1093/bioinformatics/btv490. Epub 2015 Aug 26.
Peptide identification is an important problem in proteomics. One of the most popular scoring schemes for peptide identification is XCorr (cross-correlation). Since calculating XCorr is computationally intensive, a lot of efforts have been made to develop fast XCorr engines. However, the existing XCorr engines are not suitable for high-resolution MS/MS spectrometry because they are either slow or require a specific type of CPU. We present a portable high-speed XCorr engine for high-resolution tandem mass spectrometry by developing a novel algorithm for calculating XCorr. The algorithm enables XCorr calculation 1.25-49 times faster than previous algorithms for 0.01 Da fragment tolerance. Furthermore, our engine is easily portable to any machine with different types of CPU because it is developed in C language. Hence, our XCorr engine will expedite peptide identification by high-resolution tandem mass spectrometry.
Available at http://isa.hanyang.ac.kr/HiXCorr/HiXCorr.html.
肽鉴定是蛋白质组学中的一个重要问题。肽鉴定最受欢迎的评分方案之一是 XCorr（互相关）。由于计算 XCorr 的计算量很大，因此已经做出了很多努力来开发快速的 XCorr 引擎。然而，现有的 XCorr 引擎不适合高分辨率 MS/MS 光谱，因为它们要么速度慢，要么需要特定类型的 CPU。我们通过开发一种新的计算 XCorr 的算法，为高分辨率串联质谱提供了一种便携式高速 XCorr 引擎。对于 0.01 Da 片段容限，该算法使 XCorr 的计算速度比以前的算法快 1.25-49 倍。此外，由于我们的引擎是用 C 语言开发的，因此可以轻松移植到任何具有不同类型 CPU 的机器上。因此，我们的 XCorr 引擎将加快通过高分辨率串联质谱进行肽鉴定的速度。
