Fast and Memory-Efficient Searching of Large-Scale Mass Spectrometry Data Using Tide
Over the past 30 years, software for searching tandem mass spectrometry data against a protein database has improved dramatically in speed and statistical power. However, existing tools can still struggle to analyze truly massive data sets when either the number of spectra or the number of proteins being analyzed grows too large. Here, we describe enhancements to the Tide search engine that allow it to handle data sets containing >10 million spectra and databases containing >7 billion peptides on commodity hardware. We demonstrate that the new Tide architecture is around 2-7 times faster than the previous version and is now comparable to MSFragger and Sage in speed while requiring much less memory. Tide is open source and is publicly available as precompiled binaries for Windows, Linux, and Mac.
Authors
- Frank Lawrence Nii Adoquaye Acquaye (ORCID: https://orcid.org/0000-0002-9465-4018)
- В. А. Остапенко (ORCID: https://orcid.org/0000-0001-5918-4703)
- Attila Kertész‐Farkas (ORCID: https://orcid.org/0000-0001-8110-7253)
- William Stafford Noble (ORCID: https://orcid.org/0000-0001-7283-4715)
- Yang Young Lu (ORCID: https://orcid.org/0000-0003-1686-5917)
- Charles E. Grant
- Rufino Haroldo Locon
Institutions
- National Research University Higher School of Economics (RU)
- University of Waterloo (CA)
- University of Washington (US)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- Journal of Proteome Research
- Published
- 2025-08-12
- DOI
- https://doi.org/10.1021/acs.jproteome.5c00297
- Citations
- 1
- Primary Topic
- Advanced Proteomics Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.76
Funders
- National Research University Higher School of Economics
- Directorate for Biological Sciences