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

By Patricia P. Chan, Brian Y. Lin, Allysia J. Mak, Todd M. Lowe. bioRxiv, 2019

1,596 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f51947bc44c0f0ad05fcbf765a3cc8ad0647aa10

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities (2020, methodology): “6 (9), ribosomal RNAs, non-coding RNAs and RNA regulatory features are predicted using Rfam covariance models and Infernal tools (10–12), and protein-coding genes are called by Prodigal v2.”
RNAcentral 2021: secondary structure integration, improved sequence search and new member databases (2020, background): “0 (11) software, and are visualized using Traveler (12).”
Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification (2021, methodology): “Furthermore, Bakta is available as a cloud-based web version for the convenient annotation of single genomes. tRNA and transfer- messenger-RNA (tmRNA) genes are predicted and annotated by tRNAscan-SE [18] and Aragorn [19], respectively.”