A Compositional Model to Predict the Aggregated Isotope Distribution for Average DNA and RNA Oligonucleotides.

Annelies Agten,Youzhong Liu,Melvin Geubbelmans,Piotr Prostko,Thomas De Vijlder,Dirk Valkenborg

doi:10.3390/metabo11060400

Abstract

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.

Highlights

Nucleic acids play a pivotal role in the regulation of numerous cellular processes and act as carriers in the storage and processing of genetic information
‘soft’ ionisation techniques like electrospray ionisation (ESI) and matrix-assisted laser desorption ionisation (MALDI), the mass spectrometry (MS)-based analysis of nucleic acids and oligonucleotides has received a considerable amount of attention
We have proposed a model that predicts the aggregated isotope distribution of an average DNA or RNA molecule given the monoisotopic mass as an input variable

Summary

Introduction

Nucleic acids play a pivotal role in the regulation of numerous cellular processes and act as carriers in the storage and processing of genetic information. They probably are the most intensively studied biopolymers. Several reviews have described the progress in this field over the years [1,2,3]. Despite this progress, the field has matured slowly compared to that of MS-based proteomics. One particular reason for this might be the lack of dedicated and suitable bioinformatics solutions, which has hampered analysis of large amounts of complex data [4,5,6]

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