PARALLEL COMPUTING ALGORITHMS FOR TANDEM

Tandem mass spectrometry, also known as MS/MS, is an analytical technique to measure the mass-to-charge ratio of charged ions and widely used in genomics, proteomics and metabolomics areas. There are two types of automatic ways to interpret tandem mass spectra: de novo methods and database searching methods. Both of them need to use massive computational resources and complicated comparison algorithms. The real-time peptide-spectrum matching (RT-PSM) algorithm is a database searching method to interpret tandem mass spectra with strict time constraints. Restricted by the hardware and architecture of an individual workstation the RT-PSM algorithm has to sacrifice the level of accuracy in order to provide prerequisite processing speed. The peptide-spectrum similarity scoring module is the most time-consuming part out of four modules in the RT-PSM algorithm, which is also the core of the algorithm. In this study, a multi-core computing algorithm is developed for individual workstations. Moreover, a distributed computing algorithm is designed for a cluster. The improved algorithms can achieve the speed requirement of RT-PSM without sacrificing the accuracy. With some expansion, this distributed computing algorithm can also support different PSM algorithms. Simulation results show that compared with the original RT-PSM, the parallelization version achieves 25 to 34 times speed-up based on different individual workstations. A cluster with 240 CPU cores could accelerate the similarity score module 210 times compare with the single-thread similarity score module and the whole peptide identification process 85 times compare with the single-thread peptide identification process