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

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Tandem mass spectrometry (MS/MS) data analysis software has advanced significantly over 30 years.
• Existing tools face limitations with extremely large datasets (millions of spectra or billions of peptides).

Purpose of the Study:

• To enhance the Tide search engine for analyzing massive MS/MS datasets.
• To improve the speed and memory efficiency of large-scale proteomic data processing.

Main Methods:

• Architectural enhancements to the Tide search engine.
• Testing on datasets exceeding 10 million spectra and 7 billion peptides.
• Benchmarking against existing tools like MSFragger and Sage.

Main Results:

• The enhanced Tide architecture handles >10 million spectra and >7 billion peptides on commodity hardware.
• Performance is 2-7 times faster than the previous Tide version.
• Achieves speed comparable to MSFragger and Sage with substantially reduced memory requirements.

Conclusions:

• The improved Tide engine offers a scalable and efficient solution for large-scale proteomic data analysis.
• Open-source availability and multi-platform binaries (Windows, Linux, Mac) enhance accessibility.
• Addresses current limitations in handling massive MS/MS datasets.