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Highly accelerated feature detection in proteomics data sets using modern graphics processing units

Rene Hussong, Barbara Gregorius, Andreas Tholey, Andreas Hildebrandt
Center for Bioinformatics and Computer Science Department, Saarland University, 66041 Saarbrucken
Bioinformatics (Oxford, England), (2009) 25 (15): 1937-1943.

@article{hussong2009highly,

   title={Highly accelerated feature detection in proteomics data sets using modern graphics processing units},

   author={Hussong, R. and Gregorius, B. and Tholey, A. and Hildebrandt, A.},

   journal={Bioinformatics},

   volume={25},

   number={15},

   pages={1937},

   issn={1367-4803},

   year={2009},

   publisher={Oxford Univ Press}

}

MOTIVATION: Mass spectrometry (MS) is one of the most important techniques for high-throughput analysis in proteomics research. Due to the large number of different proteins and their post-translationally modified variants, the amount of data generated by a single wetlab MS experiment can easily exceed several gigabytes. Hence, the time necessary to analyze and interpret the measured data is often significantly larger than the time spent on sample preparation and the wet-lab experiment itself. Since the automated analysis of this data is hampered by noise and baseline artifacts, more sophisticated computational techniques are required to handle the recorded mass spectra. Obviously, there is a clear trade-off between performance and quality of the analysis, which is currently one of the most challenging problems in computational proteomics. RESULTS: Using modern graphics processing units (GPUs) we implemented a feature finding algorithm based on a handtailored adaptive wavelet transform that drastically reduces the computation time. A further speedup can be achieved exploiting the multicore architecture of current computing devices, which leads to up to an approximately two hundredfold speedup in our computational experiments. In addition, we will demonstrate that several approximations necessary on the CPU to keep run times bearable, become obsolete on the GPU, yielding not only faster, but also improved results. AVAILABILITY: An open-source implementation of the CUDA-based algorithm is available via the software framework OpenMS (Sturm et al., 2008) (http://www.openms.de).
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