Deaminase-based RNA recording enables high throughput mutational profiling of protein-RNA interactions

Rachael A. Bakker, Oliver B. Nicholson, Heungwon Park, Yu-Lan Xiao, Weixin Tang, Arvind Rasi Subramaniam, and Christopher P. Lapointe

Preprint	bioRxiv 10.1101/2025.04.11.648485
Sequencing data	NCBI BioProject PRJNA1255650
Per-run accessions	sra_accessions.csv
Plasmid sequences	data/plasmid_maps
Per-panel data	source_data
Questions and reagent requests	open an issue
Licence	MIT for the code, CC BY 4.0 for the data

Figures

Panel	Figure	Code	Source data
1C	reporter_editing	plot_reporter_editing.py	figure_1c.csv
2C	recorder_edit_counts_enzyme_concentration	plot_enzyme_comparisons.py	figure_2c.csv
2D	recorder_site_distance_tada_lambdaN_250nM recorder_site_distance_tada_250nM	plot_recorder_site_distances.py	figure_2d.csv
2E	recorder_context_editing_A8	plot_recorder_contexts.py	figure_2e.csv
3B	boxb_all_variant_A8_vs_A2_250nM	plot_boxb_substitutions.py	figure_3b.csv
3C	boxb_mutation_distance_editing_mean_A8_A2_250nM	plot_boxb_mutation_distance.py	figure_3c.csv
3D	boxb_single_mutant_editing_mean_A8_A2_250nM	plot_boxb_substitutions.py	figure_3d.csv
3E	boxb_lambdan_structure	rendered from the Legault et al. 1998 coordinates, obtained from that paper's corresponding author	—
3F	boxb_multimutant_heatmaps_mean_A8_A2_lambdaN_250nM	plot_boxb_multimutant_heatmaps.py	figure_3f.csv
4B	recorder_edit_counts_pag_vs_nanobody_250nM	plot_enzyme_comparisons.py	figure_4b.csv
4C	recorder_site_editing_pag_components_A8_250nM	plot_enzyme_comparisons.py	figure_4c.csv
4D	recorder_site_editing_pag_conditions_A8	plot_enzyme_comparisons.py	figure_4d.csv
4E	boxb_all_variant_A8_vs_A2_editors	plot_boxb_substitutions.py	figure_4e.csv
4F	boxb_mutation_distance_editing_mean_A8_A2_editors	plot_boxb_mutation_distance.py	figure_4f.csv
4G	boxb_single_mutant_editing_mean_A8_A2_editors	plot_boxb_substitutions.py	figure_4g.csv
5B	boxb_read_coverage_tada_lambdaN_original	plot_boxb_read_coverage.py	figure_5b.csv
5C	recorder_edit_counts_editors_original	plot_recorder_edit_counts.py	figure_5c.csv
5D	recorder_sites_and_placements_tada_lambdaN_original	plot_recorder_site_placement.py	figure_5d.csv
5E	boxb_all_variant_A8_vs_A2_original	plot_boxb_site_correlation.py	figure_5e.csv
5F	boxb_mutation_distance_mean_A8_A2_original	plot_boxb_mutation_distance.py	figure_5f.csv
5G	boxb_single_mutant_heatmap_mean_A8_A2_original	plot_boxb_effect_heatmaps.py	figure_5g.csv
6B	variant_read_coverage	plot_variant_read_coverage.py	figure_6b.csv
6C	recorder_site_profile	plot_recorder_site_profile.py	figure_6c.csv
6D	amino_acid_site_correlation_A2_A15	plot_amino_acid_site_correlation.py	figure_6d.csv
6E	variant_class_distribution	plot_variant_class_distribution.py	figure_6e.csv
6F	position_editing_profile	plot_position_editing_profile.py	figure_6f.csv
6G	boxb_lambdan_structure_dms_positions	as above, with the λN positions in dms_position_summary.csv	—
6H	amino_acid_editing_logo	plot_amino_acid_editing_logo.py	figure_6h.csv
S1A	boxb_hairpin_site_editing_enzyme_concentration	plot_boxb_hairpin_sites.py	figure_s1a.csv
S1B	recorder_edit_counts_tada_lambdaN_time	plot_enzyme_comparisons.py	figure_s1b.csv
S2A	recorder_site_distance_tada_lambdaN_125nM recorder_site_distance_tada_125nM	plot_recorder_site_distances.py	figure_s2a.csv
S2B	recorder_site_distance_tada_lambdaN_500nM recorder_site_distance_tada_500nM	plot_recorder_site_distances.py	figure_s2b.csv
S3A	recorder_context_editing_sites	plot_recorder_contexts.py	figure_s3a.csv
S3B	rabe_rna_sequence_context	plot_context.r	figure_s3b.csv
S4A	boxb_all_variant_A8_vs_A2_125nM	plot_boxb_substitutions.py	figure_s4a.csv
S4B	boxb_mutation_distance_editing_mean_A8_A2_125nM	plot_boxb_mutation_distance.py	figure_s4b.csv
S4C	boxb_single_mutant_editing_mean_A8_A2_125nM	plot_boxb_substitutions.py	figure_s4c.csv
S4D	boxb_all_variant_A8_vs_A2_500nM	plot_boxb_substitutions.py	figure_s4d.csv
S4E	boxb_mutation_distance_editing_mean_A8_A2_500nM	plot_boxb_mutation_distance.py	figure_s4e.csv
S4F	boxb_single_mutant_editing_mean_A8_A2_500nM	plot_boxb_substitutions.py	figure_s4f.csv
S5A	boxb_multimutant_heatmaps_mean_A8_A2_lambdaN_125nM	plot_boxb_multimutant_heatmaps.py	figure_s5a.csv
S5B	boxb_multimutant_heatmaps_mean_A8_A2_lambdaN_500nM	plot_boxb_multimutant_heatmaps.py	figure_s5b.csv
S6A	boxb_single_mutant_editing_mean_A8_A2_time	plot_boxb_substitutions.py	figure_s6a.csv
S6B	boxb_mutation_distance_editing_mean_A8_A2_time	plot_boxb_mutation_distance.py	figure_s6b.csv
S6C	boxb_all_variant_A8_vs_A2_time	plot_boxb_substitutions.py	figure_s6c.csv
S6D	boxb_single_mutant_A8_vs_A2_time	plot_boxb_substitutions.py	figure_s6d.csv
S7B	recorder_site_editing_pag_components_A2_250nM	plot_enzyme_comparisons.py	figure_s7b.csv
S7C	recorder_site_editing_editors_A8_250nM	plot_enzyme_comparisons.py	figure_s7c.csv
S7D	recorder_site_editing_editors_A2_250nM	plot_enzyme_comparisons.py	figure_s7d.csv
S7E	recorder_site_editing_pag_conditions_A2	plot_enzyme_comparisons.py	figure_s7e.csv
S7F	recorder_edit_counts_pag_conditions	plot_enzyme_comparisons.py	figure_s7f.csv
S7G	recorder_edit_counts_pag_components_250nM	plot_enzyme_comparisons.py	figure_s7g.csv
S7H	boxb_direct_vs_split_recruitment_mean_A8_A2	plot_boxb_recruitment_comparison.py	figure_s7h.csv
S8A	recorder_edit_counts_tada_lambdaN_vs_no_editor_original	plot_recorder_edit_counts.py	figure_s8a.csv
S8B	boxb_in_vitro_vs_in_vivo_mean_A8_A2	plot_in_vitro_comparison.py	figure_s8b.csv

Reproduce

The analyses are Snakemake workflows that run in Singularity containers, launched by run_everything.sh from the repository root:

./run_everything.sh

./run_everything.sh --plots

The first command counts reads and then redraws; the second redraws figures from count tables that are already present. --only <name> restricts either to one analysis, and a trailing rule name runs one figure. Set SNAKEMAKE, SINGULARITY_PREFIX, SINGULARITY_ARGS and CORES to suit your machine.

Count tables for --plots are attached to the count-tables-v1 release, fetched by download_count_tables.sh:

./data/download_count_tables.sh

That is 67 MB, and covers every figure except 1C, whose counts are versioned in the repository.

Raw reads come from the BioProject, fetched by download_from_sra.sh:

./data/download_from_sra.sh

This fetches each run by accession and names the files as the workflows expect. Counting from FASTQ needs a compute allocation and several hours, and writes about 130 GB into the ignored data directory of each analysis folder; redrawing takes minutes.

Containers are ghcr.io/rasilab/r_python:2.4.2 throughout, except the in vitro read counter, which uses ghcr.io/rasilab/python:1.0.0, and the published-data reanalysis, which uses ghcr.io/rasilab/hisat2_3n:1.0.1.

Layout

Folder	Contents
nanopore_reporter_editing	Low-throughput reporter editing, Figure 1C
boxb_in_vitro_sequencing	Purified-enzyme boxB library, Figures 2-4 and Supplementary 1-7
boxb_in_vivo_sequencing	Cellular boxB library, Figure 5 and Supplementary 8
ln_dms_sequencing	λN deep mutational scan, Figure 6
published_rabe_contexts	Reanalysis of published rABE RNA sequencing, Supplementary 3B
boxb_structure_views	λN-boxB structure panels, Figures 3E and 6G

Within each analysis folder, annotations holds the versioned CSVs that define samples, read layouts, coordinates and figure selections; scripts holds the workflows and the plotting code; figures holds the output; tables holds summary tables; and data holds reads and count tables, which are not versioned.

Summary table	Contents
figure_1c_statistics.csv	Fold changes and Fisher exact tests behind Figure 1C
wildtype_statistics.csv	λN wild-type dispersion, depth contrast, test calibration and robustness series
rabe_rna_sequence_context.csv	Per-context editing in the published rABE samples