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Accelerating a cross-correlation score function to search modifications using a single GPU.

Hyunwoo Kim1, Sunggeun Han2, Jung-Ho Um3

  • 1Research Data Hub Center, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea. pardess@kisti.re.kr.

BMC Bioinformatics
|December 14, 2018
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Summary

A new graphics processing unit (GPU) accelerated score function speeds up peptide identification searches, especially when considering post-translational modifications (PTMs). This method enhances the efficiency of cross-correlation (XCorr) score functions like Tide and HiXCorr for faster proteomic data analysis.

Keywords:
Cross-correlation score functionHigh performance computingPTM searchPeptide identificationTide

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Area of Science:

  • Proteomics
  • Computational Biology
  • Bioinformatics

Background:

  • Cross-correlation (XCorr) score functions are widely used in peptide identification software (e.g., SEQUEST, Comet, Tide).
  • The HiXCorr algorithm improved tandem mass spectra preprocessing for faster XCorr calculations.
  • Searching peptides with post-translational modifications (PTMs) significantly increases candidate peptides, slowing down existing algorithms.

Purpose of the Study:

  • To develop an accelerated score function for searching peptide modifications using a graphics processing unit (GPU).
  • To improve the speed of peptide identification when PTMs are considered.

Main Methods:

  • Combined the Tide XCorr score function with the HiXCorr algorithm.
  • Implemented the combined algorithm on a GPU for accelerated computation.
  • Evaluated performance against CPU-based Tide and Tide-Hi methods.

Main Results:

  • The GPU-based method achieved 2.7x and 5.8x speed improvements over Tide and Tide-Hi, respectively (for 50 Da precursor tolerance).
  • The GPU implementation produced identical scores to the CPU-based methods, ensuring accuracy.
  • Demonstrated the feasibility of using a single GPU for accelerated PTM searches.

Conclusions:

  • An accelerating score function utilizing a single GPU has been developed for efficient peptide modification searching.
  • The developed software is publicly available for use in proteomic research.
  • This approach significantly enhances the speed of peptide identification in the presence of PTMs.