R Notebook: Provides reproducible analysis for Association of mutant sequence, barcode, and inferred fluorescence phenotype in the following manuscript:
Citation: Lippert LB, Hinton SR, Holston A, Romanowicz KJ, Plesa C. Characterizing Sequence-Function Relationships in Chimeric DcuS/EnvZ Histidine Kinases. In Prep. 2026.
GitHub Repository: https://github.com/PlesaLab/DcuSEnvZ
This pipeline processes barcode counts sequenced from twelve fluroescence-activated cell sorting (FACS) samples of the synTCS-MutLib strain in the fumarate condition. Sequence data was generated using the Illumina NextSeq platform using paired-end sequencing read amplicons. Raw sequencing data was pre-processed on a high-performance computer using the Makefile script available in the project GitHub repository. Here, pre-processed barcode-count files for all twelve fumarate samples are merged, adjusted abundances are computed using the protocol from Biswas et al., (2021), and a median activation score- here, “median bin”- is calculated for each observed barcode. This analysis is replicated for the No Ligand and Aspartate samples.
The following R packages must be installed prior to loading into the R session. See the Reproducibility tab for a complete list of packages and their versions used in this workflow.
# Make a vector of required packages
required.packages <- c("devtools", "knitr", "patchwork", "tidyverse", "ggplot2", "dplyr", "tidyr", "magrittr", "stringr", "seqinr")
# Load required packages
lapply(required.packages, library, character.only = TRUE)This section is based on the R file: “Counts_to_Median_Bin_Fumarate.R”. It describes how to load all of the pre-existing barcode data necessary for downstream analysis. The end result is a .CSV file containing the total set of observed barcodes, their associated nucleotide and amino acid sequences, activation (“median bin”) scores, the lower and upper indices of their activation bins.
# Function to load barcode/BC reads
read_collapsed_file <- function(filename, sam_name) {
df <- read.table(file=filename, sep="\t", header=FALSE)
colnames(df) <- c("BC", paste0(sam_name, "reads"), "collapsedBCs")
return(df)
}
F1_bc <- read_collapsed_file("./Final_BC/F1_S13_collapse_d1.tsv", "F1")
F2_bc <- read_collapsed_file("./Final_BC/F2_S14_collapse_d1.tsv", "F2")
F3_bc <- read_collapsed_file("./Final_BC/F3_S15_collapse_d1.tsv", "F3")
F4_bc <- read_collapsed_file("./Final_BC/F4_S16_collapse_d1.tsv", "F4")
F5_bc <- read_collapsed_file("./Final_BC/F5_S17_collapse_d1.tsv", "F5")
F6_bc <- read_collapsed_file("./Final_BC/F6_S18_collapse_d1.tsv", "F6")
F7_bc <- read_collapsed_file("./Final_BC/F7_S19_collapse_d1.tsv", "F7")
F8_bc <- read_collapsed_file("./Final_BC/F8_S20_collapse_d1.tsv", "F8")
F9_bc <- read_collapsed_file("./Final_BC/F9_S21_collapse_d1.tsv", "F9")
F10_bc <- read_collapsed_file("./Final_BC/F10_S22_collapse_d1.tsv", "F10")
F11_bc <- read_collapsed_file("./Final_BC/F11_S23_collapse_d1.tsv", "F11")
F12_bc <- read_collapsed_file("./Final_BC/F12_S24_collapse_d1.tsv", "F12")Put all BCs into one dataframe for each condition
F_allBC <- F1_bc %>%
select(BC) %>%
rbind(., F2_bc %>%
select(BC)) %>%
rbind(., F3_bc %>%
select(BC)) %>%
rbind(., F4_bc %>%
select(BC)) %>%
rbind(., F5_bc %>%
select(BC)) %>%
rbind(., F6_bc %>%
select(BC)) %>%
rbind(., F7_bc %>%
select(BC)) %>%
rbind(., F8_bc %>%
select(BC)) %>%
rbind(., F9_bc %>%
select(BC)) %>%
rbind(., F10_bc %>%
select(BC)) %>%
rbind(., F11_bc %>%
select(BC)) %>%
rbind(., F12_bc %>%
select(BC)) %>%
distinct()Add counts for barcodes
F_allBC <- left_join(F_allBC, F1_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F2_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F3_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F4_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F5_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F6_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F7_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F8_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F9_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F10_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F11_bc %>% select(-collapsedBCs), by="BC")
F_allBC <- left_join(F_allBC, F12_bc %>% select(-collapsedBCs), by="BC")Filter all barcodes by length.
F_allBC <- F_allBC %>%
filter(str_length(BC) >= 24)If not in a set, force to 0
F_allBC$F1reads[is.na(F_allBC$F1reads)] <- 0
F_allBC$F2reads[is.na(F_allBC$F2reads)] <- 0
F_allBC$F3reads[is.na(F_allBC$F3reads)] <- 0
F_allBC$F4reads[is.na(F_allBC$F4reads)] <- 0
F_allBC$F5reads[is.na(F_allBC$F5reads)] <- 0
F_allBC$F6reads[is.na(F_allBC$F6reads)] <- 0
F_allBC$F7reads[is.na(F_allBC$F7reads)] <- 0
F_allBC$F8reads[is.na(F_allBC$F8reads)] <- 0
F_allBC$F9reads[is.na(F_allBC$F9reads)] <- 0
F_allBC$F10reads[is.na(F_allBC$F10reads)] <- 0
F_allBC$F11reads[is.na(F_allBC$F11reads)] <- 0
F_allBC$F12reads[is.na(F_allBC$F12reads)] <- 0
rm(F1_bc,F2_bc,F3_bc,F4_bc,F5_bc,F6_bc,F7_bc,F8_bc,F9_bc,F10_bc,F11_bc,F12_bc)Without doing this, the bins are weighted equally (pct. total pop = 8.3333%), when in actuality, the population was not perfectly distributed across bins. Population values were calculated by dividing the number of events recorded per bin by total population in a Python script, synTCS-MutLib_FACS_Bin_Population_Fractions.ipynb.
F_bin1_NormVal = 8.3333333/4.49165402
F_bin2_NormVal = 8.3333333/7.81992919
F_bin3_NormVal = 8.3333333/8.47749115
F_bin4_NormVal = 8.3333333/6.96004047
F_bin5_NormVal = 8.3333333/8.46737481
F_bin6_NormVal = 8.3333333/9.36772888
F_bin7_NormVal = 8.3333333/9.5599393
F_bin8_NormVal = 8.3333333/9.86342944
F_bin9_NormVal = 8.3333333/13.29286798
F_bin10_NormVal = 8.3333333/12.28123419
F_bin11_NormVal = 8.3333333/7.50632271
F_bin12_NormVal = 8.3333333/1.91198786
F_allBC <- F_allBC %>%
mutate(F1reads_corrected = F1reads / F_bin1_NormVal,
F2reads_corrected = F2reads / F_bin2_NormVal,
F3reads_corrected = F3reads / F_bin3_NormVal,
F4reads_corrected = F4reads / F_bin4_NormVal,
F5reads_corrected = F5reads / F_bin5_NormVal,
F6reads_corrected = F6reads / F_bin6_NormVal,
F7reads_corrected = F7reads / F_bin7_NormVal,
F8reads_corrected = F8reads / F_bin8_NormVal,
F9reads_corrected = F9reads / F_bin9_NormVal,
F10reads_corrected = F10reads / F_bin10_NormVal,
F11reads_corrected = F11reads / F_bin11_NormVal,
F12reads_corrected = F12reads / F_bin12_NormVal
) Biswas et al., 2021: Compute a relative abundance table, R, by dividing the columns of C by their sums. The columns of R sum to 1.
F1_total <- sum(F_allBC$F1reads_corrected)
F2_total <- sum(F_allBC$F2reads_corrected)
F3_total <- sum(F_allBC$F3reads_corrected)
F4_total <- sum(F_allBC$F4reads_corrected)
F5_total <- sum(F_allBC$F5reads_corrected)
F6_total <- sum(F_allBC$F6reads_corrected)
F7_total <- sum(F_allBC$F7reads_corrected)
F8_total <- sum(F_allBC$F8reads_corrected)
F9_total <- sum(F_allBC$F9reads_corrected)
F10_total <- sum(F_allBC$F10reads_corrected)
F11_total <- sum(F_allBC$F11reads_corrected)
F12_total <- sum(F_allBC$F12reads_corrected)
F_allBC_R <- F_allBC %>%
mutate(F1_norm=F1reads_corrected/F1_total,
F2_norm=F2reads_corrected/F2_total,
F3_norm=F3reads_corrected/F3_total,
F4_norm=F4reads_corrected/F4_total,
F5_norm=F5reads_corrected/F5_total,
F6_norm=F6reads_corrected/F6_total,
F7_norm=F7reads_corrected/F7_total,
F8_norm=F8reads_corrected/F8_total,
F9_norm=F9reads_corrected/F9_total,
F10_norm=F10reads_corrected/F10_total,
F11_norm=F11reads_corrected/F11_total,
F12_norm=F12reads_corrected/F12_total) %>%
select(BC, F1_norm, F2_norm, F3_norm, F4_norm, F5_norm, F6_norm, F7_norm, F8_norm, F9_norm, F10_norm, F11_norm, F12_norm) %>%
dplyr::rename(barcode=BC)
# Check, sum off all values in each column should equal 1
sum(F_allBC_R$F1_norm)## [1] 1Calculate the total reads for each barcode across all bins
F_allBC_total_counts <- F_allBC %>%
mutate(BC_SorTotReads = F1reads_corrected + F2reads_corrected + F3reads_corrected + F4reads_corrected + F5reads_corrected + F6reads_corrected + F7reads_corrected + F8reads_corrected + F9reads_corrected + F10reads_corrected + F11reads_corrected + F12reads_corrected) %>%
select(BC, BC_SorTotReads) %>%
dplyr::rename(barcode=BC)Read in barcode-nucleotide sequence mapping file.
consensus_gene <- read.csv(file="./input_files/consensus_gene.csv",head=TRUE,sep=",")
consensus_gene %>% select(description) %>% distinct() %>% nrow() # 951065 unique barcodes## [1] 951065consensus_gene2 <- consensus_gene %>%
select(description,sequence) %>%
dplyr::rename(barcode=description,NTseq=sequence)
# convert to strings
str(consensus_gene2)## 'data.frame': 951065 obs. of 2 variables:
## $ barcode: chr "AAAAAACTGCCAAGGTAAAAAACT" "AAAAAAGTGACATGTCCCTTATTA" "AAAAACCCGTATGCGGAACTACAG" "AAAAACGCACAACCCAATAGTGTA" ...
## $ NTseq : chr "AGACATTCATTCCCTACCGCATGTTACGCAAACGTCCGATGAAATTGAGTACCACAGTGATCTTAATGGTCAGTGCGGTACTGTTCTCGGTGCTATTGGTGGTGCATCTGA"| __truncated__ "AGACATTCATTGCCCTACCGCATGTTACGCAAACGTCCGATGAAATTGAGTACCACAGTGATCTTAATGGTCAGTGCGGTACTGTTCTCGGTGCTATTGGTGGTGCATCTG"| __truncated__ "AGACATTCATTGCCCTACCGCATGTTACGCAAACGTCCGATGAAATTGAGTACCACAGTGATCTTAATGGTCAGTGCGGTACTGTTCTCGGTGCTATTGGTGGTGCATCTG"| __truncated__ "AGACATTCATTGCCCTACCGCATGTTACGCAAACGTCCGATGAAATTGAGTACCACAGTGATCTTAATGGTCAGTGCGGTACTGTTCTCGGTGCTATTGGTGGTGCATCTG"| __truncated__ ...Merge from all bins and create a master list of unique variants and BCs
tgood_F <- right_join(consensus_gene2, F_allBC_R, join_by(barcode)) %>%
mutate(NTlen=nchar(as.character(NTseq)))
sum(is.na(tgood_F$NTseq)) #718487 barcodes in F_allBC_R don't have a nucleotide sequence associated with them## [1] 718487# Normalization check, sum of all values in each column should still be 1
sum(tgood_F$F11_norm) ## [1] 1Mapping check: how many barcodes have more than 1 variant?
consensus_gene_sum <- consensus_gene2 %>%
group_by(barcode) %>%
summarise(count=n())
consensus_gene_sum %>%
filter(count>1) %>%
nrow(.) ## [1] 0Make a list of BCs with only 1 variant and filter the dataset to keep only barcodes which have been mapped.
bcgood_F <- consensus_gene_sum %>%
filter(count==1) %>%
select(-count)
# Filter to only keep barcodes which appear
F_allBC_R_NTfilter <- tgood_F %>%
semi_join(bcgood_F,by="barcode") %>%
left_join(F_allBC_total_counts,by="barcode")F_allBC_R_NTfilter_totals <- F_allBC_R_NTfilter %>%
group_by(barcode) %>%
summarise(NTseq=NTseq,
F1_t=sum(F1_norm),
F2_t=sum(F2_norm),
F3_t=sum(F3_norm),
F4_t=sum(F4_norm),
F5_t=sum(F5_norm),
F6_t=sum(F6_norm),
F7_t=sum(F7_norm),
F8_t=sum(F8_norm),
F9_t=sum(F9_norm),
F10_t=sum(F10_norm),
F11_t=sum(F11_norm),
F12_t=sum(F12_norm),
BC_SorTotReads=BC_SorTotReads)Load file of synTCS-MutLib variants - amino acid sequences - and filter to only keep translated sequences
consensus_prot <- read.csv(file="./input_files/consensus_prot_with_PreSortBC.csv",head=TRUE,sep=",")
F_allBC_R_AAfilter <- F_allBC_R_NTfilter_totals %>%
left_join(consensus_prot %>% dplyr::rename(barcode=BC),by="barcode")
# Some sequences have mutations which place a stop codon at the beginning and some barcodes were not mapped to amino acid sequences; filter these out.
F_allBC_R_AAfilter <- F_allBC_R_AAfilter %>%
filter(!is.na(seq))Replace read counts of NA and 0 with an arbitrary value of 0.1 for presort1 and presort2 libraries
F_allBC_R_AAfilter$presort1reads[is.na(F_allBC_R_AAfilter$presort1reads)] <- 0.1
F_allBC_R_AAfilter$presort1reads[F_allBC_R_AAfilter$presort1reads == 0] <- 0.1
F_allBC_R_AAfilter$presort2reads[is.na(F_allBC_R_AAfilter$presort2reads)] <- 0.1
F_allBC_R_AAfilter$presort2reads[F_allBC_R_AAfilter$presort2reads == 0] <- 0.1Biswas et al., 2021: Divide each column of R element-wise by the input relative abundance vector (relative abundance of variants in the library before flow cytometry) to obtain a fold-change table, F.
F_allBC_F <- F_allBC_R_AAfilter %>%
mutate(F1_fc=F1_t/presort2_norm,
F2_fc=F2_t/presort2_norm,
F3_fc=F3_t/presort2_norm,
F4_fc=F4_t/presort2_norm,
F5_fc=F5_t/presort2_norm,
F6_fc=F6_t/presort2_norm,
F7_fc=F7_t/presort2_norm,
F8_fc=F8_t/presort2_norm,
F9_fc=F9_t/presort2_norm,
F10_fc=F10_t/presort2_norm,
F11_fc=F11_t/presort2_norm,
F12_fc=F12_t/presort2_norm)Biswas et al., 2021: Divide each row of F by its sum to obtain a table of adjusted abundances, A. Each row of A sums to 1.
F_allBC_A <- F_allBC_F %>%
mutate(rowsum=F1_fc+F2_fc+F3_fc+F4_fc+F5_fc+F6_fc+F7_fc+F8_fc+F9_fc+F10_fc+F11_fc+F12_fc) %>%
mutate(F1=F1_fc/rowsum,
F2=F2_fc/rowsum,
F3=F3_fc/rowsum,
F4=F4_fc/rowsum,
F5=F5_fc/rowsum,
F6=F6_fc/rowsum,
F7=F7_fc/rowsum,
F8=F8_fc/rowsum,
F9=F9_fc/rowsum,
F10=F10_fc/rowsum,
F11=F11_fc/rowsum,
F12=F12_fc/rowsum)# create functions to compute upper and lower index
maxcs = function(x, output){
return(max(which(c(x[1],x[2],x[3],x[4],x[5],x[6],x[7],x[8],x[9],x[10],x[11],x[12]) < 0.5)))
}
mincs = function(x, output){
return(min(which(c(x[1],x[2],x[3],x[4],x[5],x[6],x[7],x[8],x[9],x[10],x[11],x[12]) >= 0.5)))
}
# Compute cumulative sum across adjusted barcode abundances for all bins to estimate median bin
F_allBC_CS <- F_allBC_A %>%
rowwise() %>%
mutate(
cumulative_p = list(cumsum(c(F1,F2,F3,F4,F5,F6,F7,F8,F9,F10,F11,F12))),
lower_index = max(which(cumsum(c(F1,F2,F3,F4,F5,F6,F7,F8,F9,F10,F11,F12)) < 0.5)),
upper_index = min(which(cumsum(c(F1,F2,F3,F4,F5,F6,F7,F8,F9,F10,F11,F12)) >= 0.5)),
median = ifelse(
is.infinite(lower_index),
1,
lower_index + (0.5 - unlist(cumulative_p)[lower_index]) /
(unlist(cumulative_p)[upper_index] - unlist(cumulative_p)[lower_index])
)
)## Warning: There were 1831 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `lower_index = max(...)`.
## ℹ In row 8.
## Caused by warning in `max()`:
## ! no non-missing arguments to max; returning -Inf
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1830 remaining warnings.F_allBC_CS_toCSV <- F_allBC_CS %>%
select(barcode, NTseq, seq, lower_index, upper_index, median, BC_SorTotReads, presort1reads, presort2reads)
write.csv(F_allBC_CS_toCSV,
"./output_files/DcuS_Fumarate_bin_distribution-byBC.csv", row.names = FALSE)The session information is provided for full reproducibility.
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.1 (2024-06-14)
## os macOS 15.7.3
## system x86_64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Los_Angeles
## date 2026-05-19
## pandoc 3.6.3 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/x86_64/ (via rmarkdown)
## quarto 1.7.32 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/quarto
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##
## ──────────────────────────────────────────────────────────────────────────────Biswas, S.; Khimulya, G.; Alley, E. C.; Esvelt, K. M.; Church, G. M. Low-N Protein Engineering with Data-Efficient Deep Learning. Nat. Methods 2021, 18 (4), 389–396. https://doi.org/10.1038/s41592-021-01100-y.