Extracting iBAQ values from DIA data from DIA-NN 1.9.1 search results

The following packages are required to run this script:

library(arrow)
library(here)
library(tidyverse)
library(DIAgui)

Load and filter the report.parquet file from DIANN search

diann_report <- arrow::read_parquet("report.parquet") %>%
    dplyr::filter(PG.MaxLFQ.Quality >= 0.75 & Lib.PG.Q.Value <= 0.01 & Lib.Q.Value <= 0.01 & PG.Q.Value <= 0.01) %>%
    dplyr::mutate(File.Name = Run)

transforming the diann_report in a format compatible with DIAgui

We get the gene names and peptides from the diann_report.

⚠️ the function diann_matrix used here is not the same from diann package. It comes from the DIAgui package.

unique_genes <- DIAgui::diann_matrix(diann_report,
    id.header = "Precursor.Id",
    quantity.header = "Precursor.Normalised",
    proteotypic.only = TRUE,
    pg.q = .01
)

Get the protein sequences and iBAQ values

The fasta file needs to be in the same folder as the script, so we can use the getallseq function properly.

sequence_list <- DIAgui::getallseq(
    spec = "Homo sapiens",
    pr_id = unique_genes$Protein.Group,
    bank_name = "UP000005640_9606.fasta",
    fasta_file = TRUE
    )

# get the iBAQ values for each protein
iBAQ <- DIAgui::get_iBAQ(
  unique_genes,
  proteinDB = sequence_list,
  id_name = "Protein.Group",
  ecol = 7:21,       # here we need to specify the indexes of the columns containing the intensity values
  peptideLength = c(5, 35),
  nbMiscleavages = 0,
  proteaseRegExp = getProtease("trypsin"),
  log2_transformed = TRUE,
  keep_original = FALSE
)

# save the file in the working directory
write_tsv(iBAQ, "DIANN_iBAQ_values.tsv")

Extracting the relative iBAQ for each sample

df_riBAQ <- iBAQ %>%
    dplyr::select(Precursor.Id, Protein.Group, Genes, starts_with("iBAQ")) %>%
      pivot_longer(
        -c(Precursor.Id, Protein.Group, Genes),
          names_to = "sample", values_to = "iBAQ"
  ) %>%
  group_by(sample) %>%
  na.omit() %>%
  mutate(sum_iBAQ = sum(iBAQ)) %>%
  ungroup() %>%
  mutate(riBAQ = (iBAQ / sum_iBAQ) * 100)

Plot the relative iBAQ distribution per sample and save the figure

riBAQ_plot <- df_riBAQ %>%
  ggplot() +
  geom_boxplot(aes(x = sample, y = riBAQ)) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(y = "Relative iBAQ", x = NULL)

ggsave("riBAQ_plot.png",
    riBAQ_plot, width = 10, 
    height = 10, dpi = 300)

Additional features that can be interesting

Annotate the protease inhibitors and peptidases found in the dataset.

Use the IDmapping in the UniProtKB to recover the protein families information for each ID. Dowload the query as .tsv file with the usefull information.

annotated_iBAQ <- iBAQ %>%
  left_join(read_tsv("idmapping_2024_09_19.tsv"), 
                        by = join_by(Protein.Group == From)) %>%
  dplyr::mutate(protease_or_inhibitor = case_when(
    str_detect(`Protein families`, "Protease inhibitor") ~ "Protease inhibitor",
    str_detect(`Protein families`, "Peptidase") ~ "Protease",
    TRUE ~ "General"
  ))

View(annotated_iBAQ)

plot the iBAQ values idstribution higlihting the protease inhibitors and peptidases

boxplot_iBAQ <- annotated_iBAQ %>%
  dplyr::select(Protein.Group, Genes, protease_or_inhibitor, starts_with("iBAQ")) %>%
  pivot_longer(-c(Protein.Group, Genes, protease_or_inhibitor), names_to = "sample", values_to = "iBAQ") %>%
  ggplot(aes(x = protease_or_inhibitor, y = iBAQ, fill = protease_or_inhibitor)) +
  geom_boxplot(alpha = 0.5) +
  scale_fill_manual(values = c("grey60", "firebrick3", "dodgerblue2")) +
  facet_wrap(~sample, scales = "free_y", ncol = 3) +
  theme_bw() +
  theme(legend.position = "none") +
  labs(y = "log2(iBAQ)", x = NULL, fill = NULL)

ggsave("boxplot_iBAQ.png",
    boxplot_iBAQ, width = 10, 
    height = 10, dpi = 300)

annotated_iBAQ %>% 
  dplyr::count(protease_or_inhibitor)