Abstract
One of the stranger phenomena that can occur during gene translation is where, as a ribosome reads along the mRNA, various cellular and molecular properties contribute to stalling the ribosome on a slippery sequence, shifting the ribosome into one of the other two alternate reading frames. The alternate frame has different codons, so different amino acids are added to the peptide chain, but more importantly, the original stop codon is no longer in-frame, so the ribosome can bypass the stop codon and continue to translate the codons past it. This produces a longer version of the protein, a fusion of the original in-frame amino acids, followed by all the alternate frame amino acids. There is currently no automated software to predict the occurrence of these programmed ribosomal frameshifts (PRF), and they are currently only identified by manual curation. Here we present the first machine-learning based method to detect and predict the presence of PRFs in all types of coding genes and taxa with an accuracy exceding 90%.
Competing Interest Statement: The authors have declared no competing interest.
Original language | English |
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Publisher | bioRxiv, Cold Spring Harbor Laboratory |
Number of pages | 35 |
DOIs | |
Publication status | Published - 12 Apr 2023 |
Keywords
- gene translation
- genetic sequencing
- programmed ribosomal frameshifts (PRF)