Structural modifications of DNA and RNA molecules play a pivotal role in epigenetic and posttranscriptional regulation. To characterise these modifications, more and more MS and MS/MS- based tools for the analysis of nucleic acids are being developed. To identify an oligonucleotide in a mass spectrum, it is useful to compare the obtained isotope pattern of the molecule of interest to the one that is theoretically expected based on its elemental composition. However, this is not straightforward when the identity of the molecule under investigation is unknown. Here, we present a modelling approach for the prediction of the aggregated isotope distribution of an average DNA or RNA molecule when a particular (monoisotopic) mass is available. For this purpose, a theoretical database of all possible DNA/RNA oligonucleotides up to a mass of 25 kDa is created, and the aggregated isotope distribution for the entire database of oligonucleotides is generated using the BRAIN algorithm. Since this isotope information is compositional in nature, the modelling method is based on the additive log-ratio analysis of Aitchison. As a result, a univariate weighted polynomial regression model of order 10 is fitted to predict the first 20 isotope peaks for DNA and RNA molecules. The performance of the prediction model is assessed by using a mean squared error approach and a modified Pearson’s χ² goodness-of-fit measure on experimental data. Our analysis has indicated that the variability in spectral accuracy contributed more to the errors than the approximation of the theoretical isotope distribution by our proposed average DNA/RNA model. The prediction model is implemented as an online tool. An R function can be downloaded to incorporate the method in custom analysis workflows to process mass spectral data.

The continuous advancements in LC-MS/MS proteomics over the past decades have paved the way for transformative changes in the field of medicine, particularly in the realms of preventive and personalized healthcare. Many new algorithms are evaluated on unknown proteomes and using databases with annotated MS2-spectra. When the research is focused on MS1-spectra, such databases are not available yet. Specifically, we propose a comprehensive workflow to extract MS1 isotope distributions from spectra, which we validated using a proteomics standard kit comprising known proteins at varying concentrations in duplicate. Our workflow incorporated a database search utilizing a state-of-the-art algorithm at 1% FDR. Through this approach, we investigated the impact of protein concentration on the probability of protein identification. Confidently identified PSMs were used to extract the MS1 isotope distributions through the proposed workflow. A total of 138.111 MS1 isotope distributions were extracted. Isotope distributions with 2 or more peaks were compared with their theoretical isotope distributions using the spectral angle. A median spectral angle of 0,101 and 0,0992 was observed in both samples indicating a high similarity. The findings from this study were compiled into a dataset which can potentially facilitate the development of novel tools with a focus on MS1 data.