ClusterSheep: A Graphics Processing Unit-Accelerated Software Tool for Large-Scale Clustering of Tandem Mass Spectra from Shotgun Proteomics

• Published in: Journal of proteome research 2021-12, Vol.20 (12), p.5359-5367
• Main Authors: To, Paul Ka Po, Wu, Long, Chan, Chak Ming, Hoque, Ayman, Lam, Henry
• Format: Article
• Language: English
• Subjects: Algorithms; Cluster Analysis; Databases, Protein; Proteomics - methods; Software; Tandem Mass Spectrometry - methods
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.1c00485

Modern shotgun proteomics experiments generate gigabytes of spectra every hour, only a fraction of which were utilized to form biological conclusions. Instead of being stored as flat files in public data repositories, this large amount of data can be better organized to facilitate data reuse. Clustering these spectra by similarity can be helpful in building high-quality spectral libraries, correcting identification errors, and highlighting frequently observed but unidentified spectra. However, large-scale clustering is time-consuming. Here, we present ClusterSheep, a method utilizing Graphics Processing Units (GPUs) to accelerate the process. Unlike previously proposed algorithms for this purpose, our method performs true pairwise comparison of all spectra within a precursor mass-to-charge ratio tolerance, thereby preserving the full cluster structures. ClusterSheep was benchmarked against previously reported clustering tools, MS-Cluster, MaRaCluster, and msCRUSH. The software tool also functions as an interactive visualization tool with a persistent state, enabling the user to explore the resulting clusters visually and retrieve the clustering results as desired.