Improving Spectral Similarity and Molecular Network Reliability through Noise Signal Filtering in MS/MS Spectra.

In mass spectrometry, fragmentation spectra play a central role in compound identification. However, noise in MS/MS spectra can significantly impact similarity scores and molecular network (MN) reliability, leading to inaccurate compound annotation in untargeted metabolomics. This work investigates the influence of noise on MS/MS similarity scores and molecular network structure. Noise elimination increased similarity scores for homologous spectra, enhancing match affordability. In MNs, effective noise management improved network structure, resulting in more interpretable networks with fewer edges and enhanced clustering, decreasing false-positive connections. To quantitatively assess these improvements, a minimum spanning tree (MST) analysis was performed, revealing denser regions in the denoised MNs. An increasing cutoff of noise threshold can lead to an overlay between two or more different compound spectra. A data-specific workflow was developed to identify the optimal threshold for denoising, balancing spectra quality and network integrity during noise elimination, by incorporating statistics calculated on the distribution of the MST distances and the number of fragment ions, which could be explained by an in-silico fragmentation algorithm. Finally, a faster-tailored denoising method, based solely on the intensity of individual spectral ions, demonstrated performance comparable to the previously cited fixed threshold approaches.