MS-REDUCE: an ultrafast technique for reduction of big mass spectrometry data for high-throughput processing

Bioinformatics
Muaaz Gul Awan, Fahad Saeed

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 cont...Continue Reading

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Citations

Aug 28, 2019·Journal of the American Society for Mass Spectrometry·Amol PrakashBen Orsburn
Aug 27, 2018·Computers in Biology and Medicine·Muaaz Gul AwanFahad Saeed
May 21, 2021·Network Modeling and Analysis in Health Informatics and Bioinformatics·Muaaz Gul AwanFahad Saeed

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