ArticleFrontiers in plant science2025
Deep learning-based investigation of chloroplast translation regulatory sequences.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Understanding the architecture of translational regulatory sequences in diverse chloroplasts is critical for advancing synthetic biology and genetic engineering. In this study, a hybrid deep learning model combining convolutional neural network (CNN), long short-term memory (LSTM), Attention, and Residual architectures was developed to classify and analyse two datasets: 5' untranslated region sequences from plants and algae, and the sequences with and without Shine-Dalgarno (SD) motifs from both groups. Using 300-nucleotide leader sequences upstream of the start codon as input, the model achieved strong prediction performance for both taxonomic origin and the presence or absence of SD motifs. However, a small subset of plant and algal sequences exhibited algal-like and plant-like patterns, respectively-an encouraging finding for identifying functional heterologous sequences from one group for use in the other group's genome. The results further revealed significant differences in the plastid leader sequences between the datasets (Plants
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