Package 'glyread'

Read Byonic-Byologic result

Description

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 = "label-free",
  glycan_type = "N",
  sample_name_converter = NULL,
  orgdb = "org.Hs.eg.db",
  multisite = "expand"
)

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. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". 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".
multisite	How to handle multisite glycopeptides. "expand" (default) expands each multisite glycopeptide into site-specific rows. "drop" removes multisite glycopeptides.

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

Some glycopeptides can have more than one glycosylation site. By default, they are expanded into multiple rows with the same quantification value but different protein_site and glycan_composition. Set multisite = "drop" to remove multisite glycopeptides instead.

Variable information

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

• peptide: character, peptide sequence

• peptide_site: integer, site of glycosylation on peptide

• protein: character, protein accession

• protein_site: integer, site of glycosylation on protein

• gene: character, gene name (symbol)

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

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

pGlyco3 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.

See Also

glyexp::experiment(), glyrepr::glycan_composition()

---

Read Byonic-pGlycoQuant result

Description

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

Usage

read_byonic_pglycoquant(
  fp,
  sample_info = NULL,
  quant_method = "label-free",
  glycan_type = "N",
  sample_name_converter = NULL,
  orgdb = "org.Hs.eg.db",
  parse_structure = TRUE,
  multisite = "expand"
)

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. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". 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".
parse_structure	Logical. Whether to parse glycan structures. If TRUE, glycan structures are parsed and included in the var_info as glycan_structure column. If FALSE (default), structure parsing is skipped and structure-related columns are removed.
multisite	How to handle multisite glycopeptides. "expand" (default) expands each multisite glycopeptide into site-specific rows. "drop" removes multisite glycopeptides.

Value

An glyexp::experiment() object.

Which file to use?

You should use the "Quant.spectra.list" file in the pGlycoQuant result folder. Files from Byonic result folder are not needed. For instructions on how to use Byonic and pGlycoQuant, please refer to the manual: pGlycoQuant.

Multisite glycopeptides

Some glycopeptides can have more than one glycosylation site. By default, they are expanded into multiple rows with the same quantification value but different protein_site and glycan_composition. Set multisite = "drop" to remove multisite glycopeptides instead.

Variable information

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

• peptide: character, peptide sequence

• peptide_site: integer, site of glycosylation on peptide

• protein: character, protein accession

• protein_site: integer, site of glycosylation on protein

• gene: character, gene name (symbol)

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

• glycan_structure: glyrepr::glycan_structure(), glycan structures (if parse_structure = TRUE).

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

pGlyco3 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.

See Also

glyexp::experiment(), glyrepr::glycan_composition()

---

Read GlycanFinder result

Description

GlycanFinder is a software for intact glycopeptide identification. This function reads in the result file and returns a glyexp::experiment() object. Currently only label-free quantification is supported.

Usage

read_glycan_finder(
  fp,
  sample_info = NULL,
  quant_method = "label-free",
  glycan_type = "N",
  sample_name_converter = NULL,
  orgdb = "org.Hs.eg.db",
  parse_structure = TRUE
)

Arguments

fp	File path of the GlycanFinder 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. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". 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".
parse_structure	Logical. Whether to parse glycan structures. If TRUE (default), glycan structures are parsed and included in the var_info as glycan_structure column. If FALSE, structure parsing is skipped and structure-related columns are removed.

Value

An glyexp::experiment() object.

Which file to use?

You should use the lfq/lfq.protein-glycopeptides.csv file from the GlycanFinder result folder. This file contains the quantification information for glycopeptides.

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 sample names in the Area columns of the GlycanFinder result file (e.g., "C_3", "H_3"), although the order can be different.

Variable information

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

• peptide: character, peptide sequence

• peptide_site: integer, site of glycosylation on peptide

• protein: character, protein accession

• protein_site: integer, site of glycosylation on protein

• gene: character, gene name (symbol)

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

• glycan_structure: glyrepr::glycan_structure(), glycan structures (if parse_structure = TRUE).

See Also

glyexp::experiment(), glyrepr::glycan_composition(), glyrepr::glycan_structure()

---

Read glyco-decipher output

Description

Glyco-Decipher is a software for glycopeptide identification and quantification. This function reads in the result file and returns a glyexp::experiment() object. Currently only label-free quantification is supported.

Usage

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

Arguments

fp	File path of the pGlyco3 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. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". 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?

You should use the "site.csv" file in the result folder. This file contains "Site" and "Glycan" columns, followed by quantification result for each sample.

Protein inference and uncertain sites

Glyco-Decipher reports uncertain sites and proteins. This function automatically performs protein inference using the parsimony method to find the leader proteins. This ensures each glycopeptide is uniquely mapped to a single protein, gene, and glycosite. Besides, uncertain sites on proteins are assigned NA.

Variable information

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

• protein: character, protein accession (after protein inference)

• protein_site: integer, site of glycosylation on protein (after protein inference)

• gene: character, gene name (symbol) (after protein inference)

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

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

---

Read GlyHunter result

Description

This function reads in a GlyHunter result file and returns a glyexp::experiment() object.

Usage

read_glyhunter(
  fp,
  sample_info = NULL,
  preset = c("Fu_NC_2026", "Fu_NP_2026"),
  glycan_type = "N",
  sample_name_converter = NULL
)

Arguments

fp	File path of the GlyHunter result file.
sample_info	File path of the sample information file (csv), or a sample information data.frame/tibble.
preset	"Fu_NC_2026" (default) or "Fu_NP_2026". Use "Fu_NC_2026" to use the configuration following "DOI: 10.1038/s41467-026-68579-x". Use "Fu_NP_2026" to use the configuration in an unpublished Nature Protocols paper.
glycan_type	Glycan type. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". 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.

Value

An glyexp::experiment() object.

Which file to use?

Use the "summary_area.csv" file in the GlyHunter result folder. No edit is necessary.

Variable information

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

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

If "Fu_NP_2026" preset is used, two additional columns will be included:

• nL: Number of α2,3-linked sialic acids.

• nE: Number of α2,6-linked sialic acids.

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

See Also

glyexp::experiment(), glyrepr::glycan_composition()

---

Read MSFragger-Glyco result

Description

MSFragger-Glyco is a software for glycopeptide identification and quantification. This function reads in the result file and returns a glyexp::experiment() object.

Usage

read_msfragger(
  dp,
  sample_info = NULL,
  quant_method = "label-free",
  glycan_type = "N",
  sample_name_converter = NULL
)

Arguments

dp	The directory path of the MSFragger-Glyco result folder.
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. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". 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.

Details

This function uses the "psm.tsv" file in each sample folder. Sample names are extracted from the file paths. They are the parent of each "psm.tsv" file. For example, "msfragger_result/H1/psm.tsv" will be named "H1".

Value

An glyexp::experiment() object.

Variable information

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

• peptide: character, peptide sequence

• peptide_site: integer, site of glycosylation on peptide

• protein: character, protein accession

• protein_site: integer, site of glycosylation on protein

• gene: character, gene name (symbol)

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

See Also

glyexp::experiment(), glyrepr::glycan_composition()

---

Read pGlyco3 result

Description

pGlyco3 is a software for intact glycopeptide identification and quantification. This function reads in the result file and returns a glyexp::experiment() object. Currently only label-free quantification is supported.

Use this function if you only use pGlyco3. If you also use pGlycoQuant for quantification, use read_pglyco3_pglycoquant() instead.

Usage

read_pglyco3(
  fp,
  sample_info = NULL,
  quant_method = "label-free",
  glycan_type = "N",
  sample_name_converter = NULL,
  parse_structure = FALSE
)

Arguments

fp	File path of the pGlyco3 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. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". 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.
parse_structure	Logical. Whether to parse glycan structures. If TRUE, glycan structures are parsed and included in the var_info as glycan_structure column. If FALSE (default), structure parsing is skipped and structure-related columns are removed.

Value

An glyexp::experiment() object.

Which file to use?

You should use the file with "-Pro-Quant" suffix that contains quantification information. The file should have columns including RawName, MonoArea, Peptide, Proteins, Genes, GlycanComposition, PlausibleStruct, GlySite, and ProSites.

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

Protein inference

pGlyco3 reports protein groups. That is, shared glycopeptides are reported as a group of proteins separated by ";". This function automatically performs protein inference using the parsimony method to find the leader proteins. This ensures each glycopeptide is uniquely mapped to a single protein, gene, and glycosite.

Aggregation

pGlyco3 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.

Variable information

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

• peptide: character, peptide sequence

• peptide_site: integer, site of glycosylation on peptide

• protein: character, protein accession (after protein inference)

• protein_site: integer, site of glycosylation on protein (after protein inference)

• gene: character, gene name (symbol) (after protein inference)

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

• glycan_structure: glyrepr::glycan_structure(), glycan structures (if parse_structure = TRUE).

Glycan structures

pGlyco3 reports a "plausible structure" for each glycan. You can set parse_structure = TRUE to parse these structures into a "glycan_structure" column as a glyrepr::glycan_structure() vector. However, please take caution with these structures, because pGlyco3 does not have strict quality control on glycan structure annotations.

See Also

glyexp::experiment(), glyrepr::glycan_composition(), glyrepr::glycan_structure()

---

Read pGlyco3-pGlycoQuant result

Description

If you used pGlyco3 for intact glycopeptide identification, and used pGlycoQuant for quantification, this is the function for you. It reads in a pGlycoQuant result file and returns a glyexp::experiment() object. Currently only label-free quantification is supported.

Usage

read_pglyco3_pglycoquant(
  fp,
  sample_info = NULL,
  quant_method = "label-free",
  glycan_type = "N",
  sample_name_converter = NULL,
  parse_structure = FALSE
)

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. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". 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.
parse_structure	Logical. Whether to parse glycan structures. If TRUE, glycan structures are parsed and included in the var_info as glycan_structure column. If FALSE (default), structure parsing is skipped and structure-related columns are removed.

Value

An glyexp::experiment() object.

Which file to use?

You should use the "Quant.spectra.list" file in the pGlycoQuant result folder. Files from pGlyco3 result folder are not needed. For instructions on how to use pGlyco3 and pGlycoQuant, please refer to the manual: pGlycoQuant.

Variable information

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

• peptide: character, peptide sequence

• peptide_site: integer, site of glycosylation on peptide

• protein: character, protein accession (after protein inference)

• protein_site: integer, site of glycosylation on protein (after protein inference)

• gene: character, gene name (symbol) (after protein inference)

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

• glycan_structure: glyrepr::glycan_structure(), glycan structures (if parse_structure = TRUE).

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

Protein inference

pGlyco3 reports protein groups. That is, shared glycopeptides are reported as a group of proteins separated by ";". This function automatically performs protein inference using the parsimony method to find the leader proteins. This ensures each glycopeptide is uniquely mapped to a single protein, gene, and glycosite.

Aggregation

pGlyco3 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.

Glycan structures

pGlyco3 reports a "plausible structure" for each glycan. You can set parse_structure = TRUE to parse these structures into a "glycan_structure" column as a glyrepr::glycan_structure() vector. However, please take caution with these structures, because pGlyco3 does not have strict quality control on glycan structure annotations.

See Also

glyexp::experiment(), glyrepr::glycan_composition(), glyrepr::glycan_structure()

---

Read the result from StrucGP

Description

StrucGP is a software for intact glycopeptide identification. As StrucGP doesn't support quantification, this function returns an glyexp::experiment() object with a binary (0/1) expression matrix indicating whether each glycopeptide was identified in each sample.

Usage

read_strucgp(fp, sample_info = NULL, glycan_type = "N", parse_structure = TRUE)

Arguments

fp	File path of the StrucGP result file.
sample_info	File path of the sample information file (csv), or a sample information data.frame/tibble. If NULL (default), a simple sample information tibble will be created.
glycan_type	Glycan type. One of "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc". Default is "N".
parse_structure	Logical. Whether to parse glycan structures. If TRUE (default), glycan structures are parsed and included in the var_info as glycan_structure column. If FALSE, structure parsing is skipped and the structure column is removed.

Value

An glyexp::experiment() object with a binary (0/1) expression matrix, where 1 indicates the glycopeptide was identified in that sample and 0 indicates it was not.

Variable information

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

• peptide: character, peptide sequence

• protein: character, protein accession

• gene: character, gene name (symbol)

• protein_site: integer, site of glycosylation on protein

• glycan_composition: glyrepr::glycan_composition(), glycan compositions.

• glycan_structure: glyrepr::glycan_structure(), glycan structures (if parse_structure = TRUE).

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 file_name column in the StrucGP 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

See Also

glyexp::experiment(), glyrepr::glycan_composition(), glyrepr::glycan_structure()

---