🔍 NegMDF is a metabologenomics workflow that integrates mass defect filtering (MDF) with bioinformatic structural prediction and targeted MS/MS analysis, enabling the rapid discovery and identification of type I polyketides (T1PKs) from microbial sources, particularly under negative ionization mode. The method is described in detail in the referenced paper.
conda create -n negmdf python=3.10
conda activate negmdf
conda install pandas numpy scipy shapely- Ion list of the target strain culture in a csv file
- NegMDF window in a csv file
- If you want to convert an MZmine output quant table to an ion list for NegMDF, use this command:
python3 mzmine2ionlist.py -i data/MZmine_Features_iimn_gnps_quant.csv -o data/ion_list.csv- For single mode
python3 negmdf_screen.py single -i data/ion_list.csv -w data/NegMDF_window.csv -o data/ion_list_screened.csv
- For multiple mode
Note: Place all your ion lists in a folder as input. The output must also be specified as a folder path in multiple mode.
python3 negmdf_screen.py multiple -i data/ion_lists -w data/NegMDF_window.csv -o data/outputThe so-called MDF window is essentially a convex hull constructed from points, which are derived through calculations based on predicted varient structures, in the mass defect plot (MDP).
A convex hull is the smallest convex shape (polygon or polyhedron) that encloses a given set of points in a plane or higher-dimensional space, as illustrated in the simple example below.
The code in this repository is implemented with reference to the following paper.
@article{Liu2024,
title = {A metabologenomics strategy for rapid discovery of polyketides derived from modular polyketide synthases},
ISSN = {2041-6539},
url = {http://dx.doi.org/10.1039/D4SC04174G},
DOI = {10.1039/d4sc04174g},
journal = {Chemical Science},
publisher = {Royal Society of Chemistry (RSC)},
author = {Liu, Run-Zhou and Zhang, Zhihan and Li, Min and Zhang, Lihan},
year = {2024}
}