title: MS-REDUCE: an ultrafast technique for reduction of big mass spectrometry data for high-throughput processing.
abstract: Modern proteomics studies utilize high-throughput mass spectrometers which can produce data at an astonishing rate. These big mass spectrometry (MS) datasets can easily reach peta-scale level creating storage and analytic problems for large-scale systems biology studies. Each spectrum consists of thousands of peaks which have to be processed to deduce the peptide. However, only a small percentage of peaks in a spectrum are useful for peptide deduction as most of the peaks are either noise or not useful for a given spectrum. This redundant processing of non-useful peaks is a bottleneck for streaming high-throughput processing of big MS data. One way to reduce the amount of computation required in a high-throughput environment is to eliminate non-useful peaks. Existing noise removing algorithms are limited in their data-reduction capability and are compute intensive making them unsuitable for big data and high-throughput environments. In this paper we introduce a novel low-complexity technique based on classification, quantization and sampling of MS peaks.
  
  
  
  We present a novel data-reductive strategy for analysis of Big MS data. Our algorithm, called MS-REDUCE, is capable of eliminating noisy peaks as well as peaks that do not contribute to peptide deduction before any peptide deduction is attempted. Our experiments have shown up to 100× speed up over existing state of the art noise elimination algorithms while maintaining comparable high quality matches. Using our approach we were able to process a million spectra in just under an hour on a moderate server.
  
  
  
  The developed tool and strategy has been made available to wider proteomics and parallel computing community and the code can be found at https://github.com/pcdslab/MSREDUCE CONTACT: : fahad.saeed@wmich.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Electrical and Computer Engineering and.
authors.forename: Muaaz Gul
authors.initials: MG
authors.lastname: Awan
authors.affiliation: Department of Electrical and Computer Engineering and Department of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USA.
authors.forename: Fahad
authors.initials: F
authors.lastname: Saeed
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D013058:Mass Spectrometry; D010455:Peptides; D040901:Proteomics
chemical_list: D010455:Peptides