Read Byonic-Byologic result

If you used Byonic for intact glycopeptide identification, and used Byologic for quantification, this is the function for you. It reads in a result file and returns a glyexp::experiment() object. Currently only label-free quantification is supported.

Usage

read_byonic_byologic(
  fp,
  sample_info = NULL,
  quant_method = c("label-free", "TMT"),
  glycan_type = c("N", "O"),
  sample_name_converter = NULL,
  orgdb = "org.Hs.eg.db"
)

Arguments

fp: File path of the pGlycoQuant result file.
sample_info: File path of the sample information file (csv), or a sample information data.frame/tibble.
quant_method: Quantification method. Either "label-free" or "TMT".
glycan_type: Glycan type. Either "N" or "O". Default is "N".
sample_name_converter: A function to convert sample names from file paths. The function should take a character vector of old sample names and return new sample names. Note that sample names in sample_info should match the new names. If NULL, original names are kept.
orgdb: name of the OrgDb package to use for UniProt to gene symbol conversion. Default is "org.Hs.eg.db".

Value

An glyexp::experiment() object.

Which file to use?

Open the .blgc file in the result folder with PMI-Byos. In the "Peptide List" panel (usually on the bottom right), click "Export content of the table to a CSV file" button. The exported .csv file is the file you should use.

Multisite glycopeptides

Currently, only single-site glycopeptides are supported. Multisite glycopeptides will be removed.

Sample information

The sample information file should be a csv file with the first column named sample, and the rest of the columns being sample information. The sample column must match the RawName column in the pGlyco3 result file, although the order can be different.

You can put any useful information in the sample information file. Recommended columns are:

group: grouping or conditions, e.g. "control" or "tumor", required for most downstream analyses
batch: batch information, required for batch effect correction

Aggregation

pGlycoQuant performs quantification on the PSM level. This level of information is too detailed for most downstream analyses. This function aggregate PSMs into glycopeptides through summation. For each glycopeptide (unique combination of "peptide", "peptide_site", "protein", "protein_site", "gene", "glycan_composition", "glycan_structure"), we sum up the quantifications of all PSMs that belong to this glycopeptide.

Output

This function returns a glyexp::experiment() object.

The following columns could be found in the variable information tibble:

peptide: character, peptide sequence
glycan_composition: glyrepr::glycan_composition(), glycan compositions.
peptide_site: integer, site of glycosylation on peptide
protein: character, protein accessions
protein_site: integer, site of glycosylation on protein
gene: character, gene symbols