Fast parallel tandem mass spectral library searching using GPU hardware acceleration

Abstract

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment.

Figure 1

Simplified FastPaSS workflow. Elements processed on the CPU are colored blue while elements processed on the GPU are colored red.

Figure 2

Effects of matrix multiplication batch size on performance. This matrix is an example of the dot product results matrix where the blue tiles correspond to relevant necessary calculations (where query mass and library mass match within the search tolerance) and the yellow tiles correspond to unnecessary calculations (where the query mass and library mass differ greater than the search tolerance). The two inset boxes A and B correspond to two different batch sizes, both of which are much smaller than the GPU memory. The fraction of unnecessary calculations is much smaller for the smaller batch size A.

Figure 3

Plot of FastPaSS (Tesla GPU) and SpectraST cached and un-cached run times. In the equations representing the regression lines, “n” is the number of queries and “t” is the runtime in seconds.