ACE: A fast quantitative approach of amino acid composition estimation

Calculating amino acid composition of a food sample usually requires expensive instruments and long processing time in laboratories. We propose a fast quantitative approach to approximate the amino acid composition of food samples given their proteomics data. We utilize Top3 quantitation method and a sophisticated data preprocessing pipeline to achieve a relatively accurate estimation. Fig 1. Amino acid composition estimation of broiled groun beef patty food sample. Blue is the ground truth provided by USDA. Red is a baseline approach without using Top3 quantitation. Green is our proposed approach with Top3 quantitation and sophiscated data preprocessing.

Directories

• Example: Example notebooks.
• CLI: Command-line interface script.

Getting Started

Installation

git clone https://github.com/IBPA/ACE.git
pip install ./ACE

Prerequisites

python>=3.6
numpy>=1.19.3
pandas>=1.1.5
notebook>=6.1.5

How to Use

Command-line Interface

Usage

cd path/to/ACE/ace
python main.py -h
usage: main.py [-h] [--output [OUTPUT]] [--save-pqi]
               [--log-level {10,20,30,40,50}] [input]

positional arguments:
  input                 The path to your proteomics data file.

optional arguments:
  -h, --help            show this help message and exit
  --output [OUTPUT], -o [OUTPUT]
                        The path to store your output.
  --save-pqi, -s
  --log-level {10,20,30,40,50}, -l {10,20,30,40,50}
                        The specified log level:
                        - 50: CRITICAL
                        - 40: ERROR
                        - 30: WARNING
                        - 20: INFO
                        - 10: DEBUG

Example

python main.py ../example/proteomics_example.csv -o example --save-pqi

API

Example

• Estimate amino acid composition
• Customize your hash table

Authors

• Fangzhou Li - https://github.com/fangzhouli

Contact

For any questions, please contact us at tagkopouloslab@ucdavis.edu.

License

This project is licensed under the Apache 2.0 License. Please see the LICENSE file for details.

Credits

Thanks Nikita Bacalzo for providing data. Thanks Jason Youn for code review. Thanks Prof. Tagkopoulos and Prof. Lebrilla for advising and support.

References

• [1] Gerster, Sarah, et al., 2014. Statistical Approach to Protein Quantification.