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PRFect: A tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes
BACKGROUND: 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 frame...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10274946/ https://www.ncbi.nlm.nih.gov/pubmed/37333268 http://dx.doi.org/10.21203/rs.3.rs-2997217/v1 |
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author | McNair, Katelyn Salamon, Peter Edwards, Robert A. Segall, Anca M. |
author_facet | McNair, Katelyn Salamon, Peter Edwards, Robert A. Segall, Anca M. |
author_sort | McNair, Katelyn |
collection | PubMed |
description | BACKGROUND: 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. RESULTS: Here we present PRFect, an innovative machine-learning method for the detection and prediction of PRFs in coding genes of various types. PRFect combines advanced machine learning techniques with the integration of multiple complex cellular properties, such as secondary structure, codon usage, ribosomal binding site interference, direction, and slippery site motif. Calculating and incorporating these diverse properties posed significant challenges, but through extensive research and development, we have achieved a user-friendly approach. The code for PRFect is freely available, open-source, and can be easily installed via a single command in the terminal. Our comprehensive evaluations on diverse organisms, including bacteria, archaea, and phages, demonstrate PRFect’s strong performance, achieving high sensitivity, specificity, and an accuracy exceeding 90%. CONCLUSION: PRFect represents a significant advancement in the field of PRF detection and prediction, offering a powerful tool for researchers and scientists to unravel the intricacies of programmed ribosomal frameshifting in coding genes. |
format | Online Article Text |
id | pubmed-10274946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-102749462023-06-17 PRFect: A tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes McNair, Katelyn Salamon, Peter Edwards, Robert A. Segall, Anca M. Res Sq Article BACKGROUND: 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. RESULTS: Here we present PRFect, an innovative machine-learning method for the detection and prediction of PRFs in coding genes of various types. PRFect combines advanced machine learning techniques with the integration of multiple complex cellular properties, such as secondary structure, codon usage, ribosomal binding site interference, direction, and slippery site motif. Calculating and incorporating these diverse properties posed significant challenges, but through extensive research and development, we have achieved a user-friendly approach. The code for PRFect is freely available, open-source, and can be easily installed via a single command in the terminal. Our comprehensive evaluations on diverse organisms, including bacteria, archaea, and phages, demonstrate PRFect’s strong performance, achieving high sensitivity, specificity, and an accuracy exceeding 90%. CONCLUSION: PRFect represents a significant advancement in the field of PRF detection and prediction, offering a powerful tool for researchers and scientists to unravel the intricacies of programmed ribosomal frameshifting in coding genes. American Journal Experts 2023-06-06 /pmc/articles/PMC10274946/ /pubmed/37333268 http://dx.doi.org/10.21203/rs.3.rs-2997217/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. https://creativecommons.org/licenses/by/4.0/License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Article McNair, Katelyn Salamon, Peter Edwards, Robert A. Segall, Anca M. PRFect: A tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes |
title | PRFect: A tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes |
title_full | PRFect: A tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes |
title_fullStr | PRFect: A tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes |
title_full_unstemmed | PRFect: A tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes |
title_short | PRFect: A tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes |
title_sort | prfect: a tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10274946/ https://www.ncbi.nlm.nih.gov/pubmed/37333268 http://dx.doi.org/10.21203/rs.3.rs-2997217/v1 |
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