Department of Biomolecular Engineering, Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.
Nucleic Acids Res. 2021 Sep 20;49(16):9077-9096. doi: 10.1093/nar/gkab688.
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.
tRNAscan-SE 是一款用于转移 RNA(tRNA) 基因预测的专业软件,自第一个基因组被解码以来,已经被广泛应用了二十多年。随着基因组数量和系统发育多样性的大量增加,准确检测和功能预测 tRNA 变得更加具有挑战性。我们利用一个更大的训练集,创建了近百个专门的同工型和进化枝特异性模型,极大地提高了 tRNAscan-SE 识别和分类典型和非典型 tRNA 的能力。我们采用了一种新的比较多模型策略,根据反密码子和得分最高的同工型模型对预测的 tRNA 进行评分,从而可以基于反密码子和得分最高的同工型模型进行功能预测。比较模型评分还提高了程序检测 tRNA 衍生的 SINEs 和其他可能的假基因的能力。tRNAscan-SE 首次还包括使用新开发的模型快速、高度准确地检测线粒体 tRNA。总的来说,所有同工型的 tRNA 检测灵敏度和特异性都得到了提高,特别是那些利用硒代半胱氨酸和编码 CAU 反密码子的三种 tRNA 基因亚型的专门模型的同工型。这些增强功能将为研究人员提供更准确、更详细的各种 tRNA 的注释,并可能引起对具有新特征的 tRNA 的关注。