Table of Contents

• Overview
• Support
• Installation
• Usage
• License
• Citation

Overview

FLiPPR (FragPipe Limited-Proteolysis Processor) is modular, fast, and easy-to-use data processing tool for LiP-MS & LFQ-based experiments analyzed in FragPipe.

FLiPPR is:

• Super Fast: FLiPPR utilizes Polars in the back-end to ensure all CPU cores contribute to your data processing.
• Convinent to Use: FragPipe produces standarized outputs, FLiPPR takes full advantage of this feat and integrates seemlessly with any FragPipe LFQ-MBR anaylsis.
• Flexible and Expandable: Experiments introduce unique and novel variables, FLiPPR ensures capatibility with all experimental setups and gives you full control of the data processing pipeline.

Support

FLiPPR has been tested with Python 3.10 – 3.12 To learn more about how FLiPPR can help you analyze your LiP-MS or LFQ data, head over to the FLiPPR docs

Installation

    # Initial install
    python -m pip install flippr

    # Update to latest release
    python -m pip install -U flippr

Usage

1. Start a Study

import flippr as fp
from flippr import Study

# Pass the FragPipe output directory path from your LiP-MS experiment
study = Study(lip = "path/to/lip")

# Include protein normalization factors from a Trypsin-only experiment
study = Study(lip = "path/to/lip", trp = "path/to/trp")

# Including the protein FASTA file will add metadata and remove contaminants
study = Study(lip = "path/to/lip", trp = "path/to/trp", fasta = "UP000000625_83333.fasta")

2. View the experimental sample annotations

print(study.samples)
# >>> { 'LiP': {'Native', 'Refolded_001_min', 'Refolded_005_min', 'Refolded_120_min'}, 'TrP': {'Refolded', 'Native'}}

3. Add an experimental process

# Process without normalization
study.add_process(1, "Native", "Refolded_001_min", 3)

# Process with normalizations (only when `trp` is included in the study)
study.add_process(5, "Native", "Refolded_005_min", 3, "Native", "Refolded", 3)

# Multiple processes within one study
study.add_process(1, "Native", "Refolded_001_min", 3, "Native", "Refolded", 3)
study.add_process(5, "Native", "Refolded_005_min", 3, "Native", "Refolded", 3)
study.add_process(120, "Native", "Refolded_120_min", 3, "Native", "Refolded", 3)

4. Run your study

# Running a study returns results and populates the `Study().results` dictionary
results = study.run()
# >>> {1: <flippr.Results>, 5: <flippr.Results>, 120: <flippr.Results>}

# Change analysis parameters within `fp.rcParams` before running for more control
fp.rcParams["protein.fc_sig_thresh"] = 2.0
results = study.run()

5. View or save your results as Polars DataFrames

# 1 min time point results
results_1_min = results[1]

# View all ions included in the 1 min experimental process
results_1_min.ion

# View higher-order results
results_1_min.modified_peptide
results_1_min.peptide
results_1_min.cut_site

# View protein-level summary of all data
results_1_min.protein_summary

Protein ID	Protein	Entry Name	Gene	...	No. of Valid Cut Sites	No. of Significant Cut Sites	No. of Significant Cut Sites
P00350	sp|P00350|6PGD_ECOLI	6PGD_ECOLI	gnd	...	111	40	50
P00363	sp|P00363|FRDA_ECOLI	FRDA_ECOLI	frdA	...	15	2	2
Q57261	sp|Q57261|TRUD_ECOLI	TRUD_ECOLI	truD	...	24	7	8
Q59385	sp|Q59385|COPA_ECOLI	COPA_ECOLI	copA	...	13	2	1
Q7DFV3	sp|Q7DFV3|YMGG_ECOLI	YMGG_ECOLI	ymgG	...	4	2	4
Q93K97	sp|Q93K97|ADPP_ECOLI	ADPP_ECOLI	nudF	...	9	4	5

License

This project is licensed under the CC BY-NC-ND 4.0 License. Feel free to use the code according to the terms specified in the license. This project is licensed under the CC BY-NC-ND 4.0 License. Feel free to use the code according to the terms specified in the license.

Thank you for your interest in FLiPPR! If you encounter any issues or have suggestions, please open an issue. We appreciate your feedback!

Citation

If you found this work helpful in your research, please cite us!

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe
Edgar Manriquez-Sandoval, Joy Brewer, Gabriela Lule, Samanta Lopez, and Stephen D. Fried
Journal of Proteome Research
DOI: 10.1021/acs.jproteome.3c00887