Nucleos'ID: A New Search Engine Enabling the Untargeted Identification of RNA Post-transcriptional Modifications from Tandem Mass Spectrometry Analyses of Nucleosides.
Clarisse Gosset-ErardMévie DidierjeanJérome PansanelAntony LechnerPhilippe WolffLauriane KuhnFrédéric AubrietEmmanuelle Leize-WagnerPatrick ChaimbaultYannis Nicolas FrançoisPublished in: Analytical chemistry (2023)
As RNA post-transcriptional modifications are of growing interest, several methods were developed for their characterization. One of them established for their identification, at the nucleosidic level, is the hyphenation of separation methods, such as liquid chromatography or capillary electrophoresis, to tandem mass spectrometry. However, to our knowledge, no software is yet available for the untargeted identification of RNA post-transcriptional modifications from MS/MS data-dependent acquisitions. Thus, very long and tedious manual data interpretations are required. To meet the need of easier and faster data interpretation, a new user-friendly search engine, called Nucleos'ID, was developed for CE-MS/MS and LC-MS/MS users. Performances of this new software were evaluated on CE-MS/MS data from nucleoside analyses of already well-described Saccharomyces cerevisiae transfer RNA and Bos taurus total tRNA extract. All samples showed great true positive, true negative, and false discovery rates considering the database size containing all modified and unmodified nucleosides referenced in the literature. The true positive and true negative rates obtained were above 0.94, while the false discovery rates were between 0.09 and 0.17. To increase the level of sample complexity, untargeted identification of several RNA modifications from Pseudomonas aeruginosa 70S ribosome was achieved by the Nucleos'ID search following CE-MS/MS analysis.
Keyphrases
- liquid chromatography
- tandem mass spectrometry
- ultra high performance liquid chromatography
- mass spectrometry
- ms ms
- high resolution mass spectrometry
- high performance liquid chromatography
- capillary electrophoresis
- simultaneous determination
- gas chromatography
- solid phase extraction
- liquid chromatography tandem mass spectrometry
- electronic health record
- pseudomonas aeruginosa
- saccharomyces cerevisiae
- gene expression
- big data
- high resolution
- data analysis
- small molecule
- nucleic acid
- transcription factor
- systematic review
- gas chromatography mass spectrometry
- healthcare
- deep learning
- cystic fibrosis
- staphylococcus aureus
- machine learning
- oxidative stress
- artificial intelligence
- energy transfer