Massively Parallel Open Modification Spectral Library Searching with Hyperdimensional Computing

Jaeyoung Kang, Weihong Xu, Wout Bittremieux, Tajana Rosing

Abstract: Mass spectrometry, for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most spectra remain mismatched due to unexpected post-translational modifications. Open modification search (OMS) improves the identification rate by considering every possible change in spectra, but it expands the search space exponentially. We propose HyperOMS, which redesigns OMS based on hyperdimensional computing to cope with such challenges. HyperOMS encodes floating-point spectral data with high-dimensional binary vectors, enabling the massive parallelism in OMS. Experimental results show that HyperOMS on GPU is up to 17× faster and 6.4× more energy efficient than the state-of-the-art GPU-based OMS tool [2] while providing comparable search quality.