Faster mass decomposition

TL;DR

This paper introduces three algorithmic improvements that significantly accelerate the process of determining molecular formulas from mass spectrometry data, achieving up to a 1000-fold speedup and greatly reducing memory use.

Contribution

The paper presents novel algorithm engineering techniques that optimize molecular formula decomposition, outperforming classical methods in speed and memory efficiency.

Findings

Four-fold reduction in runtime

Memory consumption reduced by up to 94%

Achieves 1000-fold speedup over classical algorithms

Abstract

Metabolomics complements investigation of the genome, transcriptome, and proteome of an organism. Today, the vast majority of metabolites remain unknown, in particular for non-model organisms. Mass spectrometry is one of the predominant techniques for analyzing small molecules such as metabolites. A fundamental step for identifying a small molecule is to determine its molecular formula. Here, we present and evaluate three algorithm engineering techniques that speed up the molecular formula determination. For that, we modify an existing algorithm for decomposing the monoisotopic mass of a molecule. These techniques lead to a four-fold reduction of running times, and reduce memory consumption by up to 94%. In comparison to the classical search tree algorithm, our algorithm reaches a 1000-fold speedup.