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 aspartate 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 aspartate 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 Fumarate 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_Aspartate.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)
}
A1_bc <- read_collapsed_file("./Final_BC/A1_S25_collapse_d1.tsv", "A1")
A2_bc <- read_collapsed_file("./Final_BC/A2_S26_collapse_d1.tsv", "A2")
A3_bc <- read_collapsed_file("./Final_BC/A3_S27_collapse_d1.tsv", "A3")
A4_bc <- read_collapsed_file("./Final_BC/A4_S28_collapse_d1.tsv", "A4")
A5_bc <- read_collapsed_file("./Final_BC/A5_S29_collapse_d1.tsv", "A5")
A6_bc <- read_collapsed_file("./Final_BC/A6_S30_collapse_d1.tsv", "A6")
A7_bc <- read_collapsed_file("./Final_BC/A7_S31_collapse_d1.tsv", "A7")
A8_bc <- read_collapsed_file("./Final_BC/A8_S32_collapse_d1.tsv", "A8")
A9_bc <- read_collapsed_file("./Final_BC/A9_S33_collapse_d1.tsv", "A9")
A10_bc <- read_collapsed_file("./Final_BC/A10_S34_collapse_d1.tsv", "A10")
A11_bc <- read_collapsed_file("./Final_BC/A11_S35_collapse_d1.tsv", "A11")
A12_bc <- read_collapsed_file("./Final_BC/A12_S36_collapse_d1.tsv", "A12")Put all BCs into one dataframe for each condition
A_allBC <- A1_bc %>%
select(BC) %>%
rbind(., A2_bc %>%
select(BC)) %>%
rbind(., A3_bc %>%
select(BC)) %>%
rbind(., A4_bc %>%
select(BC)) %>%
rbind(., A5_bc %>%
select(BC)) %>%
rbind(., A6_bc %>%
select(BC)) %>%
rbind(., A7_bc %>%
select(BC)) %>%
rbind(., A8_bc %>%
select(BC)) %>%
rbind(., A9_bc %>%
select(BC)) %>%
rbind(., A10_bc %>%
select(BC)) %>%
rbind(., A11_bc %>%
select(BC)) %>%
rbind(., A12_bc %>%
select(BC)) %>%
distinct()Add counts for barcodes
A_allBC <- left_join(A_allBC, A1_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A2_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A3_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A4_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A5_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A6_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A7_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A8_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A9_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A10_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A11_bc %>% select(-collapsedBCs), by="BC")
A_allBC <- left_join(A_allBC, A12_bc %>% select(-collapsedBCs), by="BC")Filter all barcodes by length.
A_allBC <- A_allBC %>%
filter(str_length(BC) >= 24)If not in a set, force to 0
A_allBC$A1reads[is.na(A_allBC$A1reads)] <- 0
A_allBC$A2reads[is.na(A_allBC$A2reads)] <- 0
A_allBC$A3reads[is.na(A_allBC$A3reads)] <- 0
A_allBC$A4reads[is.na(A_allBC$A4reads)] <- 0
A_allBC$A5reads[is.na(A_allBC$A5reads)] <- 0
A_allBC$A6reads[is.na(A_allBC$A6reads)] <- 0
A_allBC$A7reads[is.na(A_allBC$A7reads)] <- 0
A_allBC$A8reads[is.na(A_allBC$A8reads)] <- 0
A_allBC$A9reads[is.na(A_allBC$A9reads)] <- 0
A_allBC$A10reads[is.na(A_allBC$A10reads)] <- 0
A_allBC$A11reads[is.na(A_allBC$A11reads)] <- 0
A_allBC$A12reads[is.na(A_allBC$A12reads)] <- 0
rm(A1_bc,A2_bc,A3_bc,A4_bc,A5_bc,A6_bc,A7_bc,A8_bc,A9_bc,A10_bc,A11_bc,A12_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.
A_bin1_NormVal = 8.3333333/3.66423061
A_bin2_NormVal = 8.3333333/8.85099472
A_bin3_NormVal = 8.3333333/13.07348762
A_bin4_NormVal = 8.3333333/9.41940723
A_bin5_NormVal = 8.3333333/11.87576127
A_bin6_NormVal = 8.3333333/11.81485993
A_bin7_NormVal = 8.3333333/9.59196102
A_bin8_NormVal = 8.3333333/7.63296793
A_bin9_NormVal = 8.3333333/8.51603735
A_bin10_NormVal = 8.3333333/8.46528624
A_bin11_NormVal = 8.3333333/5.21721478
A_bin12_NormVal = 8.3333333/1.87779131
A_allBC <- A_allBC %>%
mutate(A1reads_corrected = A1reads / A_bin1_NormVal,
A2reads_corrected = A2reads / A_bin2_NormVal,
A3reads_corrected = A3reads / A_bin3_NormVal,
A4reads_corrected = A4reads / A_bin4_NormVal,
A5reads_corrected = A5reads / A_bin5_NormVal,
A6reads_corrected = A6reads / A_bin6_NormVal,
A7reads_corrected = A7reads / A_bin7_NormVal,
A8reads_corrected = A8reads / A_bin8_NormVal,
A9reads_corrected = A9reads / A_bin9_NormVal,
A10reads_corrected = A10reads / A_bin10_NormVal,
A11reads_corrected = A11reads / A_bin11_NormVal,
A12reads_corrected = A12reads / A_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.
A1_total <- sum(A_allBC$A1reads_corrected)
A2_total <- sum(A_allBC$A2reads_corrected)
A3_total <- sum(A_allBC$A3reads_corrected)
A4_total <- sum(A_allBC$A4reads_corrected)
A5_total <- sum(A_allBC$A5reads_corrected)
A6_total <- sum(A_allBC$A6reads_corrected)
A7_total <- sum(A_allBC$A7reads_corrected)
A8_total <- sum(A_allBC$A8reads_corrected)
A9_total <- sum(A_allBC$A9reads_corrected)
A10_total <- sum(A_allBC$A10reads_corrected)
A11_total <- sum(A_allBC$A11reads_corrected)
A12_total <- sum(A_allBC$A12reads_corrected)
A_allBC_R <- A_allBC %>%
mutate(A1_norm=A1reads_corrected/A1_total,
A2_norm=A2reads_corrected/A2_total,
A3_norm=A3reads_corrected/A3_total,
A4_norm=A4reads_corrected/A4_total,
A5_norm=A5reads_corrected/A5_total,
A6_norm=A6reads_corrected/A6_total,
A7_norm=A7reads_corrected/A7_total,
A8_norm=A8reads_corrected/A8_total,
A9_norm=A9reads_corrected/A9_total,
A10_norm=A10reads_corrected/A10_total,
A11_norm=A11reads_corrected/A11_total,
A12_norm=A12reads_corrected/A12_total) %>%
select(BC, A1_norm, A2_norm, A3_norm, A4_norm, A5_norm, A6_norm, A7_norm, A8_norm, A9_norm, A10_norm, A11_norm, A12_norm) %>%
dplyr::rename(barcode=BC)
# Check, sum off all values in each column should equal 1
sum(A_allBC_R$A1_norm)## [1] 1Calculate the total reads for each barcode across all bins
A_allBC_total_counts <- A_allBC %>%
mutate(BC_SorTotReads = A1reads_corrected + A2reads_corrected + A3reads_corrected + A4reads_corrected + A5reads_corrected + A6reads_corrected + A7reads_corrected + A8reads_corrected + A9reads_corrected + A10reads_corrected + A11reads_corrected + A12reads_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_A <- right_join(consensus_gene2, A_allBC_R, join_by(barcode)) %>%
mutate(NTlen=nchar(as.character(NTseq)))
sum(is.na(tgood_A$NTseq)) #471038 barcodes in A_allBC_R don't have a nucleotide sequence associated with them## [1] 471038# Normalization check, sum of all values in each column should still be 1
sum(tgood_A$A4_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_A <- consensus_gene_sum %>%
filter(count==1) %>%
select(-count)
# Filter to only keep barcodes which appear
A_allBC_R_NTfilter <- tgood_A %>%
semi_join(bcgood_A,by="barcode") %>%
left_join(A_allBC_total_counts,by="barcode")A_allBC_R_NTfilter_totals <- A_allBC_R_NTfilter %>%
group_by(barcode) %>%
summarise(NTseq=NTseq,
A1_t=sum(A1_norm),
A2_t=sum(A2_norm),
A3_t=sum(A3_norm),
A4_t=sum(A4_norm),
A5_t=sum(A5_norm),
A6_t=sum(A6_norm),
A7_t=sum(A7_norm),
A8_t=sum(A8_norm),
A9_t=sum(A9_norm),
A10_t=sum(A10_norm),
A11_t=sum(A11_norm),
A12_t=sum(A12_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=",")
A_allBC_R_AAfilter <- A_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.
A_allBC_R_AAfilter <- A_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
A_allBC_R_AAfilter$presort1reads[is.na(A_allBC_R_AAfilter$presort1reads)] <- 0.1
A_allBC_R_AAfilter$presort1reads[A_allBC_R_AAfilter$presort1reads == 0] <- 0.1
A_allBC_R_AAfilter$presort2reads[is.na(A_allBC_R_AAfilter$presort2reads)] <- 0.1
A_allBC_R_AAfilter$presort2reads[A_allBC_R_AAfilter$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.
A_allBC_F <- A_allBC_R_AAfilter %>%
mutate(A1_fc=A1_t/presort2_norm,
A2_fc=A2_t/presort2_norm,
A3_fc=A3_t/presort2_norm,
A4_fc=A4_t/presort2_norm,
A5_fc=A5_t/presort2_norm,
A6_fc=A6_t/presort2_norm,
A7_fc=A7_t/presort2_norm,
A8_fc=A8_t/presort2_norm,
A9_fc=A9_t/presort2_norm,
A10_fc=A10_t/presort2_norm,
A11_fc=A11_t/presort2_norm,
A12_fc=A12_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.
A_allBC_A <- A_allBC_F %>%
mutate(rowsum=A1_fc+A2_fc+A3_fc+A4_fc+A5_fc+A6_fc+A7_fc+A8_fc+A9_fc+A10_fc+A11_fc+A12_fc) %>%
mutate(A1=A1_fc/rowsum,
A2=A2_fc/rowsum,
A3=A3_fc/rowsum,
A4=A4_fc/rowsum,
A5=A5_fc/rowsum,
A6=A6_fc/rowsum,
A7=A7_fc/rowsum,
A8=A8_fc/rowsum,
A9=A9_fc/rowsum,
A10=A10_fc/rowsum,
A11=A11_fc/rowsum,
A12=A12_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
A_allBC_CS <- A_allBC_A %>%
rowwise() %>%
mutate(
cumulative_p = list(cumsum(c(A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11,A12))),
lower_index = max(which(cumsum(c(A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11,A12)) < 0.5)),
upper_index = min(which(cumsum(c(A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11,A12)) >= 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 1443 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `lower_index = max(...)`.
## ℹ In row 9.
## Caused by warning in `max()`:
## ! no non-missing arguments to max; returning -Inf
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1442 remaining warnings.A_allBC_CS_toCSV <- A_allBC_CS %>%
select(barcode, NTseq, seq, lower_index, upper_index, median, BC_SorTotReads, presort1reads, presort2reads)
write.csv(A_allBC_CS_toCSV,
"./output_files/DcuS_Aspartate_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.