BackgroundModification of nucleosides on transfer RNA (tRNA) is important either for correct mRNA decoding process or for tRNA structural stabilization. Nucleoside methylations catalyzed by MTase (methyltransferase) are the most common type among all tRNA nucleoside modifications. Although tRNA modified nucleosides and modification enzymes have been extensively studied in prokaryotic systems, similar research remains preliminary in higher plants, especially in crop species, such as rice (Oryza sativa). Rice is a monocot model plant as well as an important cereal crop, and stress tolerance and yield are of great importance for rice breeding.ResultsIn this study, we investigated how the composition and abundance of tRNA modified nucleosides could change in response to drought, salt and cold stress, as well as in different tissues during the whole growth season in two model plants–O. sativa and Arabidopsis thaliana. Twenty two and 20 MTase candidate genes were identified in rice and Arabidopsis, respectively, by protein sequence homology and conserved domain analysis. Four methylated nucleosides, Am, Cm, m1A and m7G, were found to be very important in stress response both in rice and Arabidopsis. Additionally, three nucleosides,Gm, m5U and m5C, were involved in plant development. Hierarchical clustering analysis revealed consistency on Am, Cm, m1A and m7G MTase candidate genes, and the abundance of the corresponding nucleoside under stress conditions. The same is true for Gm, m5U and m5C modifications and corresponding methylation genes in different tissues during different developmental stages.ConclusionsWe identified candidate genes for various tRNA modified nucleosides in rice and Arabidopsis, especially on MTases for methylated nucleosides. Based on bioinformatics analysis, nucleoside abundance assessments and gene expression profiling, we propose four methylated nucleosides (Am, Cm, m1A and m7G) that are critical for stress response in rice and Arabidopsis, and three methylated nucleosides (Gm, m5U and m5C) that might be important during development.Electronic supplementary materialThe online version of this article (10.1186/s12870-017-1206-0) contains supplementary material, which is available to authorized users.

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A combined high-quality manual annotation and deep-learning natural language processing study is reported to make accurate name entity recognition (NER) for biomedical literatures. A home-made version of entity annotation guidelines on biomedical literatures was constructed. Our manual annotations have an overall over 92% consistency for all the four entity types such as gene, variant, disease and species with the same publicly available annotated corpora from other experts previously. A total of 400 full biomedical articles from PubMed are annotated based on our home-made entity annotation guidelines. Both a BERT-based large model and a DistilBERT-based simplified model were constructed, trained and optimized for offline and online inference, respectively. The F1-scores of NER of gene, variant, disease and species for the BERT-based model are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those for the DistilBERT-based model are 95.14%, 86.26%, 91.37% and 89.92%, respectively. The F1 scores of the DistilBERT-based NER model retains 97.8%, 92.2%, 98.7% and 93.9% of those of BERT-based NER for gene, variant, disease and species, respectively. Moreover, the performance for both our BERT-based NER model and DistilBERT-based NER model outperforms that of the state-of-art model,BioBERT, indicating the significance to train an NER model on biomedical-domain literatures jointly with high-quality annotated datasets.