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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fast and Memory-Efficient Searching of Large-Scale Mass Spectrometry Data Using Tide

Frank Lawrence Nii Adoquaye Acquaye, В. А. Остапенко, Attila Kertész‐Farkas, William Stafford Noble et al.
1 citations
Journal of Proteome Research
Advanced Proteomics Techniques and Applications
0.76
article

Fast and Memory-Efficient Searching of Large-Scale Mass Spectrometry Data Using Tide

Frank Lawrence Nii Adoquaye Acquaye, В. А. Остапенко, Attila Kertész‐Farkas, William Stafford Noble, Yang Young Lu, Charles E. Grant, Rufino Haroldo Locon
article en
1 citations

Abstract

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.

Journal of Proteome ResearchVol. 24(9)
National Research University Higher School of Economics (RU), University of Waterloo (CA), University of Washington (US), Artificial Intelligence in Medicine (Canada) (CA)
National Research University Higher School of Economics, Directorate for Biological Sciences
Openalex Percentile: Top 27%
Advanced Proteomics Techniques and Applications
0.76
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Fast and Memory-Efficient Searching of Large-Scale Mass Spectrometry Data Using Tide — Frank Lawrence Nii Adoquaye Acquaye, В. А. Остапенко, et al. · Journal of Proteome Research (2025) | TGRS Research Map | TGRS