ArticleBMC bioinformatics2019
MassComp, a lossless compressor for mass spectrometry data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Clinical Peptidomics in Acute Leukemias: Current Advances and Future Perspectives.Journal of proteome research · 2024Review
- StackZDPD: a novel encoding scheme for mass spectrometry data optimized for speed and compression ratio.Scientific reports · 2022Article
- Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time.BMC bioinformatics · 2022Article
- Software tools, databases and resources in metabolomics: updates from 2018 to 2019.Metabolomics : Official journal of the Metabolomic Society · 2020Review
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Authors and funding
3 authors.
Funding
Abstract
backgroundMass 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.
resultsWe 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 .
conclusionsThe 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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Registered trials
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