Figure S11-13:Full mutant library grids
E. coli ORBIT 2022
Scott H. Saunders
Notes
This figure contains data from many different experiments that were used to optimize the protocol for ORBIT.
Setup packages and plotting for the notebook:
# Check packages
source("../tools/package_setup.R")
# Load packages
library(tidyverse)
library(cowplot)
library(kableExtra)
library(ggrastr)
# Code display options
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=FALSE, echo = TRUE, message=FALSE, warning=FALSE, fig.align="center", fig.retina = 2)
# Load plotting tools
source("../tools/plotting_tools.R")
#Modify the plot theme
theme_set(theme_figure())df_read_nearest_down <- read_csv('../../data/seq_data/left_side_TO_libs/df_read_nearest_left.csv')
df_read_nearest_up <- read_csv('../../data/seq_data/right_side_TO_libs/df_read_nearest_right.csv')
df_read_nearest_up_down <- bind_rows(df_read_nearest_up %>% mutate(up_down = 'up'),df_read_nearest_down %>% mutate(up_down = 'down')) %>%
mutate(off_target = ifelse(min_val >500, T, F)) %>%
mutate(perfect = ifelse(min_val == 0, T, F))
df_read_nearest_up_down## # A tibble: 536,686 × 15
## seqnames strand cigar qwidth start end width njunc lib read_…¹ gene_id
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 U00096.3 - 79M2S 81 2978946 2.98e6 79 0 0 2979024 lysR
## 2 U00096.3 - 6S47M 53 2095079 2.10e6 47 0 0 2095125 hisA
## 3 U00096.3 + 81M 81 4277913 4.28e6 81 0 0 4277913 soxR
## 4 U00096.3 - 76M 76 3535810 3.54e6 76 0 0 3535885 ompR
## 5 U00096.3 + 2S78M 80 317133 3.17e5 78 0 0 317133 ykgA
## 6 U00096.3 - 51M 51 3059708 3.06e6 51 0 0 3059758 argP
## 7 U00096.3 - 2S30M 32 2095096 2.10e6 30 0 0 2095125 hisA
## 8 U00096.3 - 81M 81 3535805 3.54e6 81 0 0 3535885 ompR
## 9 U00096.3 + 75M 75 4099474 4.10e6 75 0 0 4099474 rhaR
## 10 U00096.3 + 34M2S 36 417811 4.18e5 34 0 0 417811 phoB
## # … with 536,676 more rows, 4 more variables: min_val <dbl>, up_down <chr>,
## # off_target <lgl>, perfect <lgl>, and abbreviated variable name
## # ¹read_start_posdf_read_nearest_up_down_org <- df_read_nearest_up_down %>%
arrange(lib,gene_id, up_down, perfect, width ) %>%
group_by(lib,gene_id, up_down, perfect) %>%
mutate(read_num = row_number())
df_read_nearest_up_down_org## # A tibble: 536,686 × 16
## # Groups: lib, gene_id, up_down, perfect [1,485]
## seqnames strand cigar qwidth start end width njunc lib read_s…¹ gene_id
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 2 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 3 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 4 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 5 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 6 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 7 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 8 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 9 U00096.3 - 22M 22 568000 568021 22 0 0 568021 acrR
## 10 U00096.3 - 23M 23 508201 508223 23 0 0 508223 acrR
## # … with 536,676 more rows, 5 more variables: min_val <dbl>, up_down <chr>,
## # off_target <lgl>, perfect <lgl>, read_num <int>, and abbreviated variable
## # name ¹read_start_pos## # A tibble: 420 × 7
## lib gene left_oligo_pos right_oligo_pos oligo product go_term
## <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 1 alpA 2758649 2758836 CTCTCTGCAACCAAAGT… CP4-57… DNA bi…
## 2 1 argR 3384708 3385153 GCACAATAATGTTGTAT… DNA-bi… DNA bi…
## 3 1 ariR 1216374 1216615 AAACTTATACTTAATAA… putati… protei…
## 4 1 arsR 3648533 3648861 TCGAAGAGAGACACTAC… DNA-bi… transc…
## 5 1 asnC 3926565 3926998 TAAAATAGAATGAATCA… DNA-bi… protei…
## 6 1 bolA 454477 454769 GCGCCGACAGTTGCAAA… DNA-bi… protei…
## 7 1 caiF 34305 34675 ACGCCCGGTATCAACGA… DNA-bi… DNA-bi…
## 8 1 cedA 1813441 1813658 GCAAAACCGGGAGGTAT… cell d… DNA bi…
## 9 1 cspA 3720054 3720241 TCGCCGAAAGGCACACT… cold s… nuclei…
## 10 1 cspC 1907246 1907430 CACACTTCAGATCAGTG… stress… nuclei…
## # … with 410 more rowsdf_genes <- read_delim("../../data/seq_data/All_instances_of_Genes_in_Escherichia_coli_K-12_substr._MG1655.txt") %>%
mutate(left_pos = `Left-End-Position`, right_pos = `Right-End-Position`) %>%
select(-`Left-End-Position`, -`Right-End-Position`) %>%
mutate(fwd = ifelse(Direction =='+', T, F))
df_genes_lib <- left_join(df_TOs %>% select(lib, 'gene_id'='gene'), df_genes, by = c('gene_id'='Genes'))
df_genes_lib## # A tibble: 420 × 7
## lib gene_id Direction Product left_…¹ right…² fwd
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <lgl>
## 1 1 alpA + CP4-57 prophage; DNA-binding t… 2758644 2758856 TRUE
## 2 1 argR + DNA-binding transcriptional du… 3384703 3385173 TRUE
## 3 1 ariR + putative two-component system … 1216369 1216635 TRUE
## 4 1 arsR + DNA-binding transcriptional re… 3648528 3648881 TRUE
## 5 1 asnC - DNA-binding transcriptional du… 3926545 3927003 FALSE
## 6 1 bolA + DNA-binding transcriptional du… 454472 454789 TRUE
## 7 1 caiF + DNA-binding transcriptional ac… 34300 34695 TRUE
## 8 1 cedA - cell division modulator 1813421 1813663 FALSE
## 9 1 cspA + cold shock protein CspA 3720049 3720261 TRUE
## 10 1 cspC - stress protein, member of the … 1907226 1907435 FALSE
## # … with 410 more rows, and abbreviated variable names ¹left_pos, ²right_posdf_poly <- df_genes_lib %>%
#filter(right_pos<20000) %>%
pivot_longer(c(left_pos, right_pos), values_to = 'x_pos', names_to = 'left_right') %>%
mutate( y_pos_bottom = 10,y_pos_top = 100, y_pos_center=31.623) %>%
pivot_longer(c(y_pos_bottom,y_pos_top, y_pos_center), values_to = 'y_pos', names_to = 'top_bottom') %>%
mutate(x_pos = ifelse(left_right == 'left_pos' & Direction == '-' & top_bottom !='y_pos_center', x_pos + 50, x_pos)) %>%
mutate(x_pos = ifelse(left_right == 'right_pos' & Direction == '+' & top_bottom !='y_pos_center', x_pos - 50, x_pos)) %>%
mutate(point_num = case_when(left_right == 'left_pos' & top_bottom == 'y_pos_bottom' ~ 1,
left_right == 'left_pos' & top_bottom == 'y_pos_center' ~ 2,
left_right == 'left_pos' & top_bottom == 'y_pos_top' ~ 3,
left_right == 'right_pos' & top_bottom == 'y_pos_top' ~ 4,
left_right == 'right_pos' & top_bottom == 'y_pos_center' ~ 5,
left_right == 'right_pos' & top_bottom == 'y_pos_bottom' ~ 6)) %>%
arrange(gene_id, point_num)
df_poly## # A tibble: 2,520 × 10
## lib gene_id Direc…¹ Product fwd left_…² x_pos top_b…³ y_pos point…⁴
## <dbl> <chr> <chr> <chr> <lgl> <chr> <dbl> <chr> <dbl> <dbl>
## 1 3 3'ETS-<i>le… - small … FALSE left_p… 1.99e6 y_pos_… 10 1
## 2 3 3'ETS-<i>le… - small … FALSE left_p… 1.99e6 y_pos_… 31.6 2
## 3 3 3'ETS-<i>le… - small … FALSE left_p… 1.99e6 y_pos_… 100 3
## 4 3 3'ETS-<i>le… - small … FALSE right_… 1.99e6 y_pos_… 100 4
## 5 3 3'ETS-<i>le… - small … FALSE right_… 1.99e6 y_pos_… 31.6 5
## 6 3 3'ETS-<i>le… - small … FALSE right_… 1.99e6 y_pos_… 10 6
## 7 3 C0293 + small … TRUE left_p… 1.20e6 y_pos_… 10 1
## 8 3 C0293 + small … TRUE left_p… 1.20e6 y_pos_… 31.6 2
## 9 3 C0293 + small … TRUE left_p… 1.20e6 y_pos_… 100 3
## 10 3 C0293 + small … TRUE right_… 1.20e6 y_pos_… 100 4
## # … with 2,510 more rows, and abbreviated variable names ¹Direction,
## # ²left_right, ³top_bottom, ⁴point_num- gene arrows
- coordinate labels
- y axis counts
- different color or axis for off target
kb_label <- function(x){
# from s to ns
lab <- paste(x / 1000)
}
plot_all <- ggplot()+
geom_rect(data = df_TOs %>% select(lib, gene_id = gene, left_oligo_pos, right_oligo_pos) %>% filter( lib == 1),
aes(xmin = left_oligo_pos, xmax = right_oligo_pos, ymin = 0, ymax = 50), fill = 'light gray')+
geom_segment(data = df_read_nearest_down %>% filter(lib==1) %>% filter(min_val <500),
aes(x = start, xend = end, y = width, yend = width ),
#position = position_jitter(height = 75),
alpha = 0.01)+
geom_segment(data = df_read_nearest_up%>% filter(lib==1) %>% filter(min_val <500),
aes(x = start, xend = end, y = width, yend = width ),
#position = position_jitter(height = 75),
alpha = 0.01)+
scale_x_continuous(labels = scales::label_comma())+
scale_y_continuous(breaks = c(0,40,80), limits = c(0,100))+
labs(x = "Genomic position (bp)", y = 'Mapped read lengths (bp)')+
guides(fill = 'none')+
facet_wrap(~gene_id, scales = 'free', ncol = 8,strip.position = 'top')
plot_all <- ggplot()+
geom_polygon(data = df_poly %>% filter(gene_id %in% c('alpA','argR','arsR')), aes(x = x_pos, y = y_pos, group = gene_id),fill = 'light gray')+
geom_segment(data = df_read_nearest_up_down_org%>% filter(gene_id %in% c('alpA','argR','arsR')) %>% filter(min_val <500),
aes(x = start, xend = end, y = read_num, yend = read_num, color = perfect), alpha = 0.25)+
labs(x = "Genomic position (Kb)", y = 'Mapped read lengths (bp)')+
scale_x_continuous(labels = kb_label)+
scale_y_log10()+
guides(fill = 'none')+
facet_wrap(~gene_id, scales = 'free_x', ncol = 8,strip.position = 'top')Fig. S11 - IDT oPool
plot_lib_0 <- ggplot()+
geom_polygon(data = df_poly %>% filter(lib == 0), aes(x = x_pos, y = y_pos, group = gene_id),fill = 'light gray')+
rasterise(geom_segment(data = df_read_nearest_up_down_org%>% filter(lib == 0) %>% filter(min_val <500),
aes(x = start, xend = end, y = read_num, yend = read_num, color = perfect), alpha = 0.25, linewidth = 0.5), dpi = 2000, dev = "cairo")+
labs(x = "Genomic position (Kb)", y = 'Read_count')+
scale_x_continuous(labels = kb_label,breaks = scales::extended_breaks(n=3))+
scale_y_log10(limits =c(1, NA))+
guides(fill = 'none')+
facet_wrap(~gene_id, scales = 'free', ncol = 8,strip.position = 'top') + guides(color = 'none')
plot_lib_0
Fig. S13 - Lib #1 - short TFs
plot_lib_1 <- ggplot()+
geom_polygon(data = df_poly %>% filter(lib == 1), aes(x = x_pos, y = y_pos, group = gene_id),fill = 'light gray')+
rasterise(geom_segment(data = df_read_nearest_up_down_org%>% filter(lib == 1) %>% filter(min_val <500),
aes(x = start, xend = end, y = read_num, yend = read_num, color = perfect), alpha = 0.25, linewidth = 0.5), dpi = 2000, dev = "cairo")+
labs(x = "Genomic position (Kb)", y = 'Read count')+
scale_x_continuous(labels = kb_label,breaks = scales::extended_breaks(n=3))+
scale_y_log10(limits =c(1, NA))+
guides(fill = 'none')+
facet_wrap(~gene_id, scales = 'free', ncol = 8,strip.position = 'top') + guides(color = 'none')
plot_lib_1
Fig. S14 - Lib #2 - long TFs
Note this plot is so large, that the Rmd notebook seems to have trouble displaying it, however the pdf output works fine.
plot_lib_2 <- ggplot()+
geom_polygon(data = df_poly %>% filter(lib == 2), aes(x = x_pos, y = y_pos, group = gene_id),fill = 'light gray')+
rasterise(
geom_segment(data = df_read_nearest_up_down_org%>% filter(lib == 2) %>% filter(min_val <500),
aes(x = start, xend = end, y = read_num, yend = read_num, color = perfect), alpha = 0.25, linewidth = 0.5),
dpi = 2000, dev = "cairo")+
labs(x = "Genomic position (Kb)", y = 'Read_count')+
scale_x_continuous(labels = kb_label,breaks = scales::extended_breaks(n=3))+
scale_y_log10(limits =c(1, NA))+
guides(fill = 'none')+
facet_wrap(~gene_id, scales = 'free', ncol = 8,strip.position = 'top') + guides(color = 'none')
#plot_lib_2
#save_plot("../../figures/r_pdf_figs/supp_figs/plot_lib_2.pdf",plot_lib_2, base_height = 20, base_width = 8)Fig. S15 - Lib #3 - sRNAs
plot_lib_3 <- ggplot()+
geom_polygon(data = df_poly %>% filter(lib == 3), aes(x = x_pos, y = y_pos, group = gene_id),fill = 'light gray')+
rasterise(geom_segment(data = df_read_nearest_up_down_org%>% filter(lib == 3) %>% filter(min_val <500),
aes(x = start, xend = end, y = read_num, yend = read_num, color = perfect), alpha = 0.25, linewidth = 0.5), dpi = 2000, dev = "cairo")+
labs(x = "Genomic position (Kb)", y = 'Read_count')+
scale_x_continuous(labels = kb_label,breaks = scales::extended_breaks(n=3))+
scale_y_log10(limits =c(1, NA))+
guides(fill = 'none')+
facet_wrap(~gene_id, scales = 'free', ncol = 8,strip.position = 'top') + guides(color = 'none')
plot_lib_3
#save_plot("../../figures/r_pdf_figs/supp_figs/plot_lib_3.pdf",plot_lib_3, base_height = 10, base_width = 8)## R version 4.2.0 (2022-04-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggrastr_1.0.1 kableExtra_1.3.4 cowplot_1.1.1 viridis_0.6.2
## [5] viridisLite_0.4.1 knitr_1.41 forcats_0.5.2 stringr_1.5.0
## [9] dplyr_1.1.0 purrr_0.3.5 readr_2.1.3 tidyr_1.2.1
## [13] tibble_3.1.8 ggplot2_3.4.0 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.4 sass_0.4.4 bit64_4.0.5
## [4] vroom_1.6.0 jsonlite_1.8.3 modelr_0.1.10
## [7] bslib_0.4.1 assertthat_0.2.1 highr_0.9
## [10] vipor_0.4.5 googlesheets4_1.0.1 cellranger_1.1.0
## [13] yaml_2.3.6 pillar_1.8.1 backports_1.4.1
## [16] glue_1.6.2 digest_0.6.30 rvest_1.0.3
## [19] colorspace_2.0-3 htmltools_0.5.4 pkgconfig_2.0.3
## [22] broom_1.0.1 haven_2.5.1 scales_1.2.1
## [25] webshot_0.5.4 svglite_2.1.0 tzdb_0.3.0
## [28] timechange_0.1.1 googledrive_2.0.0 farver_2.1.1
## [31] generics_0.1.3 ellipsis_0.3.2 cachem_1.0.6
## [34] withr_2.5.0 cli_3.4.1 magrittr_2.0.3
## [37] crayon_1.5.2 readxl_1.4.1 evaluate_0.18
## [40] fs_1.5.2 fansi_1.0.3 xml2_1.3.3
## [43] beeswarm_0.4.0 Cairo_1.6-0 tools_4.2.0
## [46] hms_1.1.2 gargle_1.2.1 lifecycle_1.0.3
## [49] munsell_0.5.0 reprex_2.0.2 compiler_4.2.0
## [52] jquerylib_0.1.4 systemfonts_1.0.4 rlang_1.1.1
## [55] grid_4.2.0 rstudioapi_0.14 labeling_0.4.2
## [58] rmarkdown_2.18 gtable_0.3.1 DBI_1.1.3
## [61] R6_2.5.1 gridExtra_2.3 lubridate_1.9.0
## [64] fastmap_1.1.0 bit_4.0.5 utf8_1.2.2
## [67] stringi_1.7.8 ggbeeswarm_0.7.1 parallel_4.2.0
## [70] vctrs_0.5.2 dbplyr_2.2.1 tidyselect_1.2.0
## [73] xfun_0.35