ClusterSheep

ClusterSheep clusters tandem mass spectra from shotgun proteomics using GPU-accelerated pairwise comparisons to construct high-quality spectral libraries, correct identification errors, and identify recurrent unidentified spectra within a specified precursor mass-to-charge ratio tolerance.

Key Features:

• GPU-accelerated computation (CUDA): Uses Graphics Processing Units with CUDA to accelerate clustering and reduce runtime compared to CPU-based approaches.
• True pairwise comparisons: Performs exhaustive pairwise similarity comparisons across all tandem mass spectra to preserve cluster structural integrity.
• Precursor m/z tolerance clustering: Groups spectra by similarity within a specified precursor mass-to-charge ratio tolerance.
• Scalability for large datasets: Handles gigabytes-scale tandem mass spectra typical of shotgun proteomics experiments.
• Spectral library construction: Enables construction of high-quality spectral libraries from clustered spectra.
• Identification error correction: Supports correction of peptide-spectrum identification errors through cluster consensus.
• Detection of recurrent unidentified spectra: Identifies frequently observed but previously unidentified spectra for downstream analysis.
• Benchmarking against existing tools: Demonstrated superior speed and accuracy in benchmarks against MS-Cluster, MaRaCluster, and msCRUSH.

Scientific Applications:

• Spectral library generation: Producing curated spectral libraries for peptide identification in proteomics workflows.
• Peptide identification quality control: Detecting and correcting identification errors across large-scale datasets.
• Unidentified spectrum discovery: Flagging recurrent unknown spectra for further characterization.
• Large-scale shotgun proteomics analysis: Enabling scalable clustering to improve spectral analysis and interpretation in high-throughput proteomics.

Methodology:

Performs true pairwise comparisons across all tandem mass spectra and clusters them by similarity within a specified precursor mass-to-charge ratio tolerance using GPU-accelerated computations via CUDA, with benchmarking against MS-Cluster, MaRaCluster, and msCRUSH.