PepNet
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The state of the art Deep CNN neural network for de novo sequencing of tandem mass spectra
PepNet
Code for "Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks"
Link to Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks
The state of the art Deep CNN neural network for de novo sequencing of tandem mass spectra, currently works on unmodified HCD spectra of charges 1+ to 4+.
Free for academic uses. Licensed under LGPL.
Visit https://denovo.predfull.com/ to try online prediction
Update History
- 2023.04.27: 2nd Revised version.
- 2022.11.28: Revised version.
- 2021.12.28: First version.
Method
Based on the structure of the residual convolutional networks. Current precision (bin size): 0.1 Th.
How to use
After clone this project, you should download the pre-trained model (model.h5
) from zenodo.org and place it into PepNet's folder.
Important Notes
- Will only output unmodification sequences.
- This model assumes a FIXED carbamidomethyl on C
- The length of output peptides are limited to =< 30
Required Packages
Recommend to install dependency via Anaconda
- Python >= 3.7
- Tensorflow >= 2.5.0
- Pandas >= 0.20
- pyteomics
- numba
Packages Required for traning:
- Tensorflow-addons
Output format
Sample output looks like:
TITLE | DENOVO | Score | PPM Difference | Positional Score |
---|---|---|---|---|
spectra 1 | LALYCHQLNLCSK | 1.0000 | -3.8919184 | [1.0, 0.9999956, 1.0, 1.0, 1.0, 1.0, 0.99999976, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] |
spectra 2 | HEELMLGDPCLK | 1.0000 | 4.207922 | [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.99999976, 1.0] |
spectra 3 | AGLVGPEFHEK | 1.0000 | 0.54602236 | [1.0, 1.0, 1.0, 1.0, 1.0, 0.99999917, 1.0, 1.0, 1.0, 1.0, 1.0] |
Usage
Simply run:
python denovo.py --input example.mgf --model model.h5 --output example_prediction.tsv
The output file is in MGF format
- --input: the input mgf file
- --output: the output file path
- --model: the pretrained model
Typical running speed: sequencing 10,000 spectra in ~59 seconds on a NVIDIA A6000 GPU.
Prediction Examples
We provide sample data on for you to evaluate the sequencing performance. The
example.mgf
file contains ground truth spectra (randomly sampled from NIST Human Synthetic Peptide Spectral Library), while the example.tsv
file contains pre-run predictions.
Also, you can run python evaluation.py --mgf example.mgf --novorst example_prediction.tsv
to generate figures presenting the de novo performance.
Train this model
See train.py
for sample training codes
Related works
Also, Visit https://www.predfull.com/ to check our previous project on full spectrum prediction