Agnostic Framework for the Classification/Identification of Organisms Based on RNA Post-Transcriptional Modifications.

ABSTRACT: We propose a novel approach for building a classification/identification framework based on the full complement of RNA post-transcriptional modifications (rPTMs) expressed by an organism at basal conditions. The approach relies on advanced mass spectrometry techniques to characterize the products of exonuclease digestion of total RNA extracts. Sample profiles comprising identities and relative abundances of all detected rPTM were used to train and test the capabilities of different machine learning (ML) algorithms. Each algorithm proved capable of identifying rigorous decision rules for differentiating closely related classes and correctly assigning unlabeled samples. The ML classifiers resolved different members of the Enterobacteriaceae family, alternative Escherichia coli

SUBMITTER: McIntyre WD 

PROVIDER: S-EPMC8351319 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature