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tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes

tRNAscan-SE has been widely used for transfer RNA (tRNA) gene prediction for over twenty years, developed just as the first genomes were decoded. With the massive increase in quantity and phylogenetic diversity of genomes, the accurate detection and functional prediction of tRNAs has become more cha...

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Autores principales: Chan, Patricia P, Lin, Brian Y, Mak, Allysia J, Lowe, Todd M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450103/
https://www.ncbi.nlm.nih.gov/pubmed/34417604
http://dx.doi.org/10.1093/nar/gkab688
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author Chan, Patricia P
Lin, Brian Y
Mak, Allysia J
Lowe, Todd M
author_facet Chan, Patricia P
Lin, Brian Y
Mak, Allysia J
Lowe, Todd M
author_sort Chan, Patricia P
collection PubMed
description tRNAscan-SE has been widely used for transfer RNA (tRNA) gene prediction for over twenty years, developed just as the first genomes were decoded. With the massive increase in quantity and phylogenetic diversity of genomes, the accurate detection and functional prediction of tRNAs has become more challenging. Utilizing a vastly larger training set, we created nearly one hundred specialized isotype- and clade-specific models, greatly improving tRNAscan-SE’s ability to identify and classify both typical and atypical tRNAs. We employ a new comparative multi-model strategy where predicted tRNAs are scored against a full set of isotype-specific covariance models, allowing functional prediction based on both the anticodon and the highest-scoring isotype model. Comparative model scoring has also enhanced the program's ability to detect tRNA-derived SINEs and other likely pseudogenes. For the first time, tRNAscan-SE also includes fast and highly accurate detection of mitochondrial tRNAs using newly developed models. Overall, tRNA detection sensitivity and specificity is improved for all isotypes, particularly those utilizing specialized models for selenocysteine and the three subtypes of tRNA genes encoding a CAU anticodon. These enhancements will provide researchers with more accurate and detailed tRNA annotation for a wider variety of tRNAs, and may direct attention to tRNAs with novel traits.
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spelling pubmed-84501032021-09-20 tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes Chan, Patricia P Lin, Brian Y Mak, Allysia J Lowe, Todd M Nucleic Acids Res Computational Biology tRNAscan-SE has been widely used for transfer RNA (tRNA) gene prediction for over twenty years, developed just as the first genomes were decoded. With the massive increase in quantity and phylogenetic diversity of genomes, the accurate detection and functional prediction of tRNAs has become more challenging. Utilizing a vastly larger training set, we created nearly one hundred specialized isotype- and clade-specific models, greatly improving tRNAscan-SE’s ability to identify and classify both typical and atypical tRNAs. We employ a new comparative multi-model strategy where predicted tRNAs are scored against a full set of isotype-specific covariance models, allowing functional prediction based on both the anticodon and the highest-scoring isotype model. Comparative model scoring has also enhanced the program's ability to detect tRNA-derived SINEs and other likely pseudogenes. For the first time, tRNAscan-SE also includes fast and highly accurate detection of mitochondrial tRNAs using newly developed models. Overall, tRNA detection sensitivity and specificity is improved for all isotypes, particularly those utilizing specialized models for selenocysteine and the three subtypes of tRNA genes encoding a CAU anticodon. These enhancements will provide researchers with more accurate and detailed tRNA annotation for a wider variety of tRNAs, and may direct attention to tRNAs with novel traits. Oxford University Press 2021-08-20 /pmc/articles/PMC8450103/ /pubmed/34417604 http://dx.doi.org/10.1093/nar/gkab688 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Chan, Patricia P
Lin, Brian Y
Mak, Allysia J
Lowe, Todd M
tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes
title tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes
title_full tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes
title_fullStr tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes
title_full_unstemmed tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes
title_short tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes
title_sort trnascan-se 2.0: improved detection and functional classification of transfer rna genes
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450103/
https://www.ncbi.nlm.nih.gov/pubmed/34417604
http://dx.doi.org/10.1093/nar/gkab688
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