Fig. 2: Flow-seq toehold-switch library characterization and trigger ontology. | Nature Communications

Fig. 2: Flow-seq toehold-switch library characterization and trigger ontology.

From: A deep learning approach to programmable RNA switches

Fig. 2

The distribution of recovered toeholds for (a) ON-state signals, (b) OFF-state signals, and (c) calculated ON/OFF ratios are shown. d Validation results for toehold switches expressed in a PURExpress cell-free system with un-fused-trigger RNA, including eight low-performing (poor, ON/OFF < 0.05) and eight high-performing (good, ON/OFF > 0.97) samples. Obtained in vivo flow-seq data show competency in classifying switch performance for this in vitro cell-free biological context (P < 0.0001 between high and low switches, two-tailed t test) with n = 3 biologically independent samples each for both ON and OFF measurements. e Tested switch/trigger variants from each origin category, including randomly generated sequences, 906 human transcription factor transcripts, and 23 pathogenic viral genomes. f Experimental ON/OFF ratios for all triggers tiled across the transcripts of two clinically relevant human transcription factors (stat3 and kmt2a) upregulated in cancerous phenotypes51,52, as well as all triggers tiled across the genomes of two pathogenic viruses: West Nile Virus (WNV) and human immunodeficiency virus (HIV). GFP    green fluorescent protein, Seq sequence, HPV   human papillomavirus. All ON, OFF, and ON/OFF values shown were selected from quality control process #3, QC3 in Supplementary Fig. S13 and Supplementary Table 1. All source data are provided as a Source Data file.

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