Accelerating the scoring module of mass spectrometry-based peptide identification using GPUs

You Li, Hao Chi, Leihao Xia, Xiaowen Chu
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
BMC Bioinformatics, 15:121, 2014


   title={Accelerating the scoring module of mass spectrometry-based peptide identification using GPUs},

   author={Li, You and Chi, Hao and Xia, Leihao and Chu, Xiaowen},

   journal={BMC bioinformatics},





   publisher={BioMed Central Ltd}


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BACKGROUND: Tandem mass spectrometry-based database searching is currently the main method for protein identification in shotgun proteomics. The explosive growth of protein and peptide databases, which is a result of genome translations, enzymatic digestions, and post-translational modifications (PTMs), is making computational efficiency in database searching a serious challenge. Profile analysis shows that most search engines spend 50%-90% of their total time on the scoring module, and that the spectrum dot product (SDP) based scoring module is the most widely used. As a general purpose and high performance parallel hardware, graphics processing units (GPUs) are promising platforms for speeding up database searches in the protein identification process. RESULTS: We designed and implemented a parallel SDP-based scoring module on GPUs that exploits the efficient use of GPU registers, constant memory and shared memory. Compared with the CPU-based version, we achieved a 30 to 60 times speedup using a single GPU. We also implemented our algorithm on a GPU cluster and achieved an approximately favorable speedup. CONCLUSIONS: Our GPU-based SDP algorithm can significantly improve the speed of the scoring module in mass spectrometry-based protein identification. The algorithm can be easily implemented in many database search engines such as X!Tandem, SEQUEST, and pFind. A software tool implementing this algorithm is available at http://www.comp.hkbu.edu.hk/~youli/ProteinByGPU.html
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