Massively Parallel Open Modification Spectral Library Searching with Hyperdimensional Computing

TL;DR

HyperOMS leverages hyperdimensional computing to significantly accelerate open modification spectral library searching on GPUs, achieving up to 17 times faster performance and greater energy efficiency while maintaining comparable accuracy.

Contribution

This work introduces HyperOMS, a novel hyperdimensional computing approach for open modification spectral search, enabling efficient, parallel, and scalable protein identification.

Findings

HyperOMS is up to 17x faster on GPU compared to existing tools.

HyperOMS is 6.4x more energy efficient than state-of-the-art GPU methods.

HyperOMS maintains comparable search quality to existing spectral matching tools.

Abstract

Mass spectrometry, commonly used for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most of them remain unidentified or mismatched due to unexpected post-translational modifications. Open modification search (OMS) has been proposed as a strategy to improve the identification rate by considering every possible change in spectra, but it expands the search space exponentially. In this work, we propose HyperOMS, which redesigns OMS based on hyperdimensional computing to cope with such challenges. Unlike existing algorithms that represent spectral data with floating point numbers, HyperOMS encodes them with high dimensional binary vectors and performs the efficient OMS in high-dimensional space. With the massive parallelism and simple boolean operations, HyperOMS can be efficiently handled on parallel computing platforms. Experimental results show that HyperOMS on GPU is up to $17\times$ faster and $6.4\times$ more energy efficient than the state-of-the-art GPU-based OMS tool while providing comparable search quality to competing search tools.