A Compositional Model to Predict the Aggregated Isotope Distribution for Average DNA and RNA Oligonucleotides
| Autor: | Youzhong Liu, Melvin Geubbelmans, Dirk Valkenborg, Piotr Prostko, Thomas De Vijlder, Annelies Agten |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
oligonucleotide Mean squared error Endocrinology Diabetes and Metabolism 01 natural sciences Biochemistry Microbiology Article DNA RNA prediction isotope distribution mass 03 medical and health sciences chemistry.chemical_compound Molecular Biology mass spectrometry Isotope Oligonucleotide software 010401 analytical chemistry spectrometry QR1-502 0104 chemical sciences 030104 developmental biology chemistry Nucleic acid Mass spectrum Monoisotopic mass Biological system |
| Zdroj: | Metabolites, Vol 11, Iss 400, p 400 (2021) Metabolites Volume 11 Issue 6 |
| Popis: | 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 chi(2) 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. Special Research Fund [BOF19DOC33]; Hasselt University; Janssen Pharmaceutical Companies of Johnson and Johnson |
| Databáze: | OpenAIRE |
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