PepMapViz: A Versatile Toolkit for Peptide Mapping, Visualization, and Comparative Exploration ================
PepMapViz is a versatile R visualization package that empowers researchers with comprehensive visualization tools for seamlessly mapping peptides to protein sequences, identifying distinct domains and regions of interest, accentuating mutations, and highlighting post-translational modifications, all while enabling comparisons across diverse experimental conditions. Potential applications of PepMapViz include the visualization of cross-software mass spectrometry results at the peptide level for specific protein and domain details in a linearized format and post-translational modification coverage across different experimental conditions; unraveling insights into disease mechanisms. It also enables visualization of MHC-presented peptide clusters in different antibody regions predicting immunogenicity in antibody drug development.
You can install the development version of PepMapViz from GitHub
using the devtools package.
# Install devtools if you haven't already
install.packages("devtools")
# Install PepMapViz from the package
devtools::build()
devtools::install()This is a basic example which shows you how to solve a common problem:
library(PepMapViz)
# Read all files from a folder
folder_path <- system.file("extdata/example_PEAKS_result", package = "PepMapViz")
resulting_df <- combine_files_from_folder(folder_path)
meta_data_path <- system.file("extdata/example_PEAKS_metadata", package = "PepMapViz")
meta_data_df <- combine_files_from_folder(meta_data_path)
resulting_df <- merge(
x = resulting_df,
y = meta_data_df,
by = "Source File",
all.x = TRUE # Left join behavior
)
# Strip the sequence
striped_data_peaks <- strip_sequence(resulting_df, "Peptide", "Sequence", "PEAKS")
# Extract modifications information
PTM_table <- data.frame(PTM_mass = c("15.99", ".98", "57.02"),
PTM_type = c("Ox", "Deamid", "Cam"))
converted_data_peaks <- obtain_mod(
striped_data_peaks,
"Peptide",
"PEAKS",
seq_column = NULL,
PTM_table,
PTM_annotation = TRUE,
PTM_mass_column = "PTM_mass"
)
# Match peptide sequence with provided sequence and calculate positions
whole_seq <- data.frame(
Epitope = c("Boco", "Boco"),
Chain = c("HC", "LC"),
Region_Sequence = c("QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYYMHWVRQAPGQGLEWMGEISPFGGRTNYNEKFKSRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARERPLYASDLWGQGTTVTVSSASTKGPSVFPLAPCSRSTSESTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSNFGTQTYTCNVDHKPSNTKVDKTVERKCCVECPPCPAPPVAGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVQFNWYVDGVEVHNAKTKPREEQFNSTFRVVSVLTVVHQDWLNGKEYKCKVSNKGLPSSIEKTISKTKGQPREPQVYTLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPMLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK",
"DIQMTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQRYSLWRTFGQGTKLEIKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC"
)
)
matching_result <- match_and_calculate_positions(
converted_data_peaks,
'Sequence',
whole_seq,
match_columns = NULL,
sequence_length = c(10, 30),
column_keep = c(
"PTM_mass",
"PTM_position",
"reps",
"Area",
"Donor",
"PTM_type"
)
)
# Quantify matched peptide sequences by PSM
matching_columns = c("Chain", "Epitope")
distinct_columns = c("Donor")
data_with_psm <- peptide_quantification(
whole_seq,
matching_result,
matching_columns,
distinct_columns,
quantify_method = "PSM",
with_PTM = TRUE,
reps = TRUE
)
region <- data.frame(
Epitope = c("Boco", "Boco", "Boco", "Boco", "Boco", "Boco"),
Chain = c("HC", "HC", "HC", "HC", "LC", "LC"),
Region = c("VH", "CH1", "CH2", "CH3", "VL", "CL"),
Region_start = c(1,119,229,338,1,108),
Region_end = c(118,228,337,444,107,214)
)
result_with_psm <- data.frame()
for (i in 1:nrow(region)) {
chain <- region$Chain[i]
region_start <- region$Region_start[i]
region_end <- region$Region_end[i]
region_name <- region$Region[i]
temp <- data_with_psm[data_with_psm$Chain == chain &
data_with_psm$Position >= region_start &
data_with_psm$Position <= region_end, ]
temp$Region <- region_name
result_with_psm <- rbind(result_with_psm, temp)
}
head(result_with_psm)## Character Position Chain Epitope PSM Donor PTM PTM_type Region
## 1 Q 1 HC Boco 0 D1 FALSE <NA> VH
## 2 V 2 HC Boco 0 D1 FALSE <NA> VH
## 3 Q 3 HC Boco 0 D1 FALSE <NA> VH
## 4 L 4 HC Boco 0 D1 FALSE <NA> VH
## 5 V 5 HC Boco 0 D1 FALSE <NA> VH
## 6 Q 6 HC Boco 0 D1 FALSE <NA> VH# Plotting peptide in whole provided sequence
domain <- data.frame(
domain_type = c("VH", "CH1", "CH2", "CH3", "VL", "CL", "CDR H1", "CDR H2", "CDR H3", "CDR L1", "CDR L2", "CDR L3"),
Chain = c("HC", "HC", "HC", "HC", "LC", "LC", "HC", "HC", "HC", "LC", "LC", "LC"),
Epitope = c("Boco", "Boco", "Boco", "Boco", "Boco", "Boco", "Boco", "Boco", "Boco", "Boco", "Boco", "Boco"),
domain_start = c(1, 119, 229, 338, 1, 108, 26, 50, 97, 24, 50, 89),
domain_end = c(118, 228, 337, 444, 107, 214, 35, 66, 107, 34, 56, 97),
domain_color = c("black", "black", "black", "black", "black", "black", "#F8766D", "#B79F00", "#00BA38", "#00BFC4", "#619CFF", "#F564E3"),
domain_fill_color = c("white", "white", "white", "white", "white", "white", "yellow", "yellow", "yellow", "yellow", "yellow", "yellow"),
domain_label_y = c(1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4)
)
x_axis_vars <- c("Region")
y_axis_vars <- c("Donor")
column_order <- list(
Donor = "D1,D2,D3,D4,D5,D6,D7,D8",
Region = "VH,CH1,CH2,CH3,VL,CL"
)
PTM_color <- c(
"Ox" = "red",
"Deamid" = "cyan",
"Cam" = "blue",
"Acetyl" = "magenta"
)
label_filter = list(Donor = "D1"){r psm-plot, fig.width=30, fig.height=6, echo=TRUE, message=FALSE, warning=FALSE} library(PepMapViz) p_psm <- create_peptide_plot( data_with_psm, y_axis_vars, x_axis_vars, y_expand = c(0.2, 0.2), x_expand = c(0.5, 0.5), theme_options = list(legend.box = "horizontal", legend.position = "bottom"), labs_options = list(title = "PSM Plot", x = "Position", fill = "PSM"), color_fill_column = 'PSM', fill_gradient_options = list(), # Set the limits for the color scale label_size = 1.3, add_domain = TRUE, domain = domain, domain_start_column = "domain_start", domain_end_column = "domain_end", domain_type_column = "domain_type", domain_border_color_column = "domain_color", domain_fill_color_column = "domain_fill_color", add_domain_label = TRUE, domain_label_size = 2, domain_label_y_column = "domain_label_y", domain_label_color = "black", PTM = TRUE, PTM_type_column = "PTM_type", PTM_color = PTM_color, add_label = TRUE, label_column = "Character", label_filter = label_filter, label_y = 1, column_order = column_order ) print(p_psm)
You can interactively explore your data and visualization options using the built-in Shiny application provided by PepMapViz. Simply run the following command in your R console to launch the app:
PepMapViz::run_pepmap_app()This will open a user-friendly graphical interface for peptide mapping, visualization, and comparative exploration.
For a detailed guide on how to use PepMapViz, please refer to our vignette and docuemntation under inst/doc.
This project is licensed under the MIT License
Copyright (c) 2024, Genentech, Inc.
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