NDLI: An Out-of-Core GPU based Dimensionality Reduction Algorithm for Big Mass Spectrometry Data and Its Application in Bottom-up Proteomics

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An Out-of-Core GPU based Dimensionality Reduction Algorithm for Big Mass Spectrometry Data and Its Application in Bottom-up Proteomics

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An Out-of-Core GPU based Dimensionality Reduction Algorithm for Big Mass Spectrometry Data and Its Application in Bottom-up Proteomics

Content Provider	ACM Digital Library
Author	Saeed, Fahad Awan, Muaaz Gul
Abstract	Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS). The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.
Starting Page	550
Ending Page	555
Page Count	6
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3107466
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Parallel computing Algorithms Out-of-core Proteomics Bigdata Gpu Mass spectrometry Data reduction Cuda
Content Type	Text
Resource Type	Article

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