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PepMapViz: A Versatile Toolkit for Peptide Mapping, Visualization, and Comparative Exploration ================

PepMapViz

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.

Installation

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()

Features

  1. Mapping peptides to protein sequences
  2. Identifying distinct domains and regions of interest
  3. Accentuating mutations
  4. Highlighting post-translational modifications
  5. Enabling comparisons across diverse experimental conditions

Usage

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")
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)

Getting Started

Launching the Shiny App

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.

License

This project is licensed under the MIT License

Copyright (c) 2024, Genentech, Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Acknowledgments

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Version

Install

install.packages('PepMapViz')

Monthly Downloads

214

Version

1.1.0

License

MIT + file LICENSE

Maintainer

Zhenru Zhou

Last Published

June 25th, 2025

Functions in PepMapViz (1.1.0)

obtain_mod_Spectronaut

Obtain modification information from Peptide data generated by Spectronaut
obtain_mod_Skyline

Obtain modification information from Peptide data generated by Skyline
strip_sequence_Maxquant

Strip sequence from Maxquant outputs
strip_sequence_Skyline

Strip sequence from Skyline outputs
strip_sequence_DIANN

Strip sequence from DIANN outputs
strip_sequence

Strip peptide sequences based on the specified data type
strip_sequence_PEAKS

Strip sequence from PEAKS outputs
strip_sequence_Comet

Strip sequence from Comet outputs
strip_sequence_Spectronaut

Strip sequence from Spectronaut outputs
strip_sequence_MSFragger

Strip sequence from MSFragger outputs
obtain_mod_DIANN

Obtain modification information from Peptide data generated by DIA-NN
obtain_mod_MSFragger

Obtain modification information from Peptide data generated by MSFragger
calculate_PSM

Calculate Spectra Count (PSM) for one row of the input sequence dataframe
obtain_mod_Comet

Obtain modification information from Peptide data generated by Comet
calculate_Area

Calculate Area/Intensity for one row of the input sequence dataframe
combine_files_from_folder

Combine CSV and TXT Files from a Folder
create_peptide_plot

Create a peptide Plot
match_and_calculate_positions

Match peptide sequence with provided sequence and calculate positions
convert_to_regex_pattern

Convert Peptide Sequence to Regex Pattern
obtain_mod

Obtain post translational modification(PTM) information from Peptide data based on the specified data type
calculate_all_PSM

Calculate Spectra Count (PSM) for the whole input sequence dataframe
calculate_all_Area

Calculate Area/Intensity for the whole input sequence dataframe
obtain_mod_mzIdenML

Obtain modification information from Peptide data generated by mzIdenML
obtain_mod_mzTab

Obtain modification information from Peptide data generated by mzTab
run_pepmap_app

Launch PepMapViz Shiny Application
peptide_quantification

Peptide Quantification
obtain_mod_PEAKS

Obtain modification information from Peptide data generated by PEAKS
obtain_mod_Maxquant

Obtain modification information from Peptide data generated by Maxquant