4.1 Article

OrbiFragsNets. A tool for automatic annotation of orbitrap MS2 spectra using networks grade as selection criteria

Journal

METHODSX
Volume 11, Issue -, Pages -

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ELSEVIER
DOI: 10.1016/j.mex.2023.102257

Keywords

Chemical consistency; Mass spectrometry; Orbitrap; Fragments networks

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Introducing OrbiFragsNets, a tool for automatic annotation of MS2 spectra generated by Orbi-trap instruments, as well as the concepts of chemical consistency and fragments networks. OrbiFragsNets utilizes the specific confidence interval for each peak in every MS2 spectrum, which is unclear in high-resolution mass spectrometry literature. The annotations are expressed as fragments networks, a set of networks with combinations of annotations for the fragments. This new approach proved to perform as well as established tools like RMassBank and SIRIUS for automatic annotation of Orbitrap MS2 spectra.
We introduce OrbiFragsNets, a tool for automatic annotation of MS2 spectra generated by Orbi-trap instruments, as well as the concepts of chemical consistency and fragments networks. Orb-iFragsNets takes advantage of the specific confidence interval for each peak in every MS2 spectrum, which is an unclear idea across the high-resolution mass spectrometry literature. The spectrum annotations are expressed as fragments networks, a set of networks with the possible combina-tions of annotations for the fragments. The model behind OrbiFragsNets is briefly described here and explained in detail in the constantly updated manual available in the GitHub repository. This new approach in MS2 spectrum de novo automatic annotation proved to perform as good as well established tools such as RMassBank and SIRIUS.& BULL; A new approach on automatic annotation of Orbitrap MS2 spectra is introduced.& BULL; Possible spectrum annotation are described as independent consistent networks, with annota-tions for each fragment as nodes, and annotations for the mass difference between fragments as edges. & BULL; Annotation process is described as the selection of the most connected fragments network.

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