peptidomicspy

Tools for peptidomics analysis of digesta from protein digestion

peptidomicspy provides functions to process, filter, analyze, and visualize peptidomics data, especially from MaxQuant protein digestion studies. This README introduces the package through four parts:

✨ Functions

🚀 Example analysis workflow

📊 Example plots

🛠️ Required input files

📦 Installation

python -m pip install "git+https://github.com/xchuam/peptidomicsPy.git"

✨ Functions

1) Data processing — processPeptides()

• Import a MaxQuant peptide export, intensity-column metadata, and protein mapping.
• Automatically remove contaminants and reverse sequences.
• Compute replicate- and group-level mean intensities and peptide type numbers.
• Map parent protein name and protein group for each peptide.
• Calculate peptide GRAVY scores.

2) Filtering & statistics

• Subset by sequence, regex pattern, or grouping variables — filterPeptides().
• Compare specific groups using statistical analysis to identify significantly different peptides — ttestPeptides().

3) Visualization

• Stacked bar plots of peptide intensities by parent proteins — plot_int().
• Stacked bar plots of numbers of unique peptide types by parent proteins — plot_type_num().
• Peptide-length distribution as stacked bars or weighted density curves — plot_length_distribution().
• Cleavage-site sequence logos for N, C, or both termini — plot_cleavage_site().
• GRAVY vs. intensity scatter or density plots — plot_gravy_vs_intensity().
• Peptide alignment along a selected protein, with optional auto-sizing and sequence lookup — plot_pep_align().
• Volcano plot(s) for ttestPeptides() results — plot_volcano().

🚀 Example analysis workflow

1. Import and process data → processPeptides()
2. Explore distributions:
   • Intensities → plot_int()
   • Peptide types → plot_type_num()
   • Length distribution → plot_length_distribution()
   • Cleavage sites → plot_cleavage_site()
   • Hydrophobicity trends → plot_gravy_vs_intensity()
3. Filter subsets of peptides → filterPeptides()
4. Perform statistical comparisons → ttestPeptides()
5. Visualize results → plot_volcano()

📊 Example plots

This is only a small sample of the exported functions included in the package. For more information, please check the help files and tests.

1) Import and process data

from peptidomicspy import (
    plot_cleavage_site,
    plot_int,
    plot_length_distribution,
    plot_pep_align,
    processPeptides,
)

result = processPeptides(
    peptides_file="examples/data/Yogurtexample_QR188-205.csv",
    intensity_columns_file="examples/data/Intensity_columns.csv",
    protein_mapping_file="examples/data/protein_mapping.csv",
)

2) Mean peptide intensities by parent proteins

plot_int(
    result,
    x_var="Yogurt",
    type="mean",
    filter_params={"Digest.stage": "G120"},
    color_by="Protein.name",
)

3) Peptide-length distribution (by intensity)

plot_length_distribution(
    result,
    facet_rows="Yogurt",
    filter_params={"Digest.stage": "G120"},
)

plot_length_distribution(
    result,
    metric="type_num",
    filter_params={"Yogurt": "Y1"},
    plot_mode="density",
)

4) Peptide alignment along a protein

pep_align = plot_pep_align(
    result,
    protein_name="P02662",
    filter_params={"Digest.stage": "G120"},
    x_interval=5,
    x_range=(80, 165),
    y_range=(0, 12),
)
pep_align

5) Cleavage-site intensity (N and/or C termini)

plot_cleavage_site(
    result,
    terminal="both",
    measure="intensity",
    replicate_mode="mean",
    filter_params={"Digest.stage": "G120"},
    scientific_10_y=True,
    drop_constant_groups=True,
)

🛠️ Required input files

Three files are required:

1) MaxQuant peptide output (peptides.txt)

• Must contain the structural columns used to derive cleavage information and peptide identities.
• All intensity columns must be present and numeric.

Example:

Sequence	Leading razor protein	Length	Start position	End position	Amino acid before	First amino acid	Last amino acid	Amino acid after	Intensity Sample1_1	Intensity Sample1_2	Intensity Sample2_1
AAGGPGAPADPGRPT	P81265	15	609	623	D	A	T	G	40332000	51443000	39094000
AAIDEASKKLNAQ	P15497	13	253	265	L	A	Q	—	18167000	26893000	17524000

2) Intensity-column metadata (intensity_columns_file)

• Each row maps a single intensity column to the replicate and all associated grouping variables (e.g. digest stage, treatment).
• The non-Intensity.column fields must uniquely identify the row; they define the nested grouping structure used to calculate replicate and group summaries.
• It is recommended to use dots (.) instead of spaces in column names for easier programming.

Example:

Intensity.column	Digest.stage	Sample	Replicate
Intensity Sample1_1	Gastric	S1	1
Intensity Sample1_2	Gastric	S1	2
Intensity Sample2_1	Gastric	S2	1

3) Protein mapping (protein_mapping_file)

• Provides human-readable protein names and groups for each Leading.razor.protein identifier.
• Missing matches are filled with the fallback levels Others and Whey.
• A curated default mapping ships with the package as an in-memory dataset you can load via load_example_protein_mapping().

Example:

Leading.razor.protein	Protein.name	Protein.group
UNIPROT:A0A452FK23_CAPHI	Beta-casein	Casein
UNIPROT:CASB_CAPHI	Beta-casein	Casein
UNIPROT:A0A452G9D9_CAPHI	Kappa-casein	Casein

To use the in-memory default mapping file, call:

from peptidomicspy import load_example_protein_mapping, processPeptides

protein_mapping = load_example_protein_mapping()

result = processPeptides(
    peptides_file="path/to/your/peptides.txt",
    intensity_columns_file="path/to/your/Intensity_columns.csv",
    protein_mapping_file=protein_mapping,
)

📖 Citation

If you use peptidomicsPy in your work, please cite this repository:

Ma, X., & Ren, Q. (2026). peptidomicsPy (Version 1.1.1-alpha) [Computer software]

📜 License

This project is licensed under the GPL-3.0 License — see the LICENSE file for details.