MassComp, a lossless compressor for mass spectrometry data

Background Mass Spectrometry (MS) is a widely used technique in biology research, and has become key in proteomics and metabolomics analyses. As a result, the amount of MS data has significantly increased in recent years. For example, the MS repository MassIVE contains more than 123TB of data. Someh...

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Published in:BMC bioinformatics Vol. 20; no. 1; pp. 368 - 10
Main Authors: Yang, Ruochen, Chen, Xi, Ochoa, Idoia
Format: Journal Article
Language:English
Published: London BioMed Central 01.07.2019
BioMed Central Ltd
BMC
Subjects:
Algorithms
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data Compression - methods
Data compression software
Health aspects
Life Sciences
Lossless compression
Mass Spectrometry
Metabolomics
Methodology
Methodology Article
Microarrays
Proteomics
Retirement benefits
Spectroscopy
Storage
China
Lossless compression
Mass spectrometry
Storage
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Mass Spectrometry (MS) is a widely used technique in biology research, and has become key in proteomics and metabolomics analyses. As a result, the amount of MS data has significantly increased in recent years. For example, the MS repository MassIVE contains more than 123TB of data. Somehow surprisingly, these data are stored uncompressed, hence incurring a significant storage cost. Efficient representation of these data is therefore paramount to lessen the burden of storage and facilitate its dissemination. Results We present MassComp , a lossless compressor optimized for the numerical (m/z)-intensity pairs that account for most of the MS data. We tested MassComp on several MS data and show that it delivers on average a 46% reduction on the size of the numerical data, and up to 89%. These results correspond to an average improvement of more than 27% when compared to the general compressor gzip and of 40% when compared to the state-of-the-art numerical compressor FPC . When tested on entire files retrieved from the MassIVE repository, MassComp achieves on average a 59% size reduction. MassComp is written in C++ and freely available at https://github.com/iochoa/MassComp . Conclusions The compression performance of MassComp demonstrates its potential to significantly reduce the footprint of MS data, and shows the benefits of designing specialized compression algorithms tailored to MS data. MassComp is an addition to the family of omics compression algorithms designed to lessen the storage burden and facilitate the exchange and dissemination of omics data.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-2962-7