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Agnostic Framework for the Classification/Identification of Organisms Based on RNA Post-Transcriptional
William D McIntyre1, Reza Nemati2, Mehraveh Salehi3
1Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States.
Analytical Chemistry
|May 27, 2021
Summary
This study introduces a new machine learning framework using RNA post-transcriptional modifications (rPTMs) to classify organisms and cells. This approach accurately identifies even closely related species and cell types, advancing epitranscriptomics.
Area of Science:
- Biochemistry and Molecular Biology
- Bioinformatics and Computational Biology
- Genomics and Proteomics
Background:
- RNA molecules undergo numerous post-transcriptional modifications (rPTMs) that influence their function and stability.
- Characterizing the complete landscape of rPTMs is crucial for understanding gene regulation and cellular processes.
- Existing methods for classifying organisms and cells often struggle with closely related entities sharing similar genetic backgrounds.
Purpose of the Study:
- To develop a novel classification and identification framework utilizing the full complement of organism-wide rPTMs.
- To leverage advanced mass spectrometry and machine learning (ML) for analyzing rPTM profiles.
- To establish a method for deciphering the epitranscriptomics code and its role in biological systems.
Main Methods:
- Utilized advanced mass spectrometry to characterize rPTMs from total RNA after exonuclease digestion.
- Generated sample profiles detailing the identity and relative abundance of detected rPTMs.
- Trained and tested various machine learning algorithms on rPTM profiles for classification tasks.
Main Results:
- Machine learning algorithms successfully identified decision rules for differentiating closely related biological classes.
- Classifiers accurately assigned unlabeled samples, including members of the *Enterobacteriaceae* family, *Escherichia coli* serotypes, *Saccharomyces cerevisiae* mutants, and *Homo sapiens* neural cells.
- High accuracy and resolution were maintained even with a significant increase in the number of classes.
- Generated dendrograms based on ML data mirrored established taxonomic systems, revealing gene deletion effects.
Conclusions:
- The proposed rPTM-based ML framework offers a powerful tool for biological classification and identification.
- This approach provides insights into the regulatory roles of rPTMs and aids in deciphering the epitranscriptomics code.
- The method holds broad applicability in diagnostics for RNA-related diseases due to the ubiquity of RNA.
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