xfcui / isofusion

Peptide feature detection based on multitask fusion learning

Authors: Qihong Jiao^, Yuxiao Wang^, Shiwei Sun*, Xuefeng Cui*
    - ^: These authors contributed equally to this work.
    - *: To whom correspondence should be addressed.
Contact: [email protected]
Publish: https://xxx.org/xxx

Introduction

We propose a model based on deep learning, called IsoFusion. This method does not rely on expert knowledge and does not need to adjust complex parameters, which makes it easier to use than traditional methods. In addition, our model is an end-to-end model that can predict charge, number of isotopes and retention time directly from the mass spectrum. The main contributions of our work are listed as followings.: (a) A novel end-to-end model for peptide feature detection from mass spectrum were proposed. (b) FuseBlock that integrates features from different dimensions. (c) Using the multi-task learning to predict charge, number of isotopes and retention time simultaneously, the auxiliary task can help improve the learning performance of the main task.

Usage

You will need to convert your raw mass spectrometry files to MS1 format. The conversion tool can use MSConvert, which you need to download and install yourself.

Suppose you have MS1 files now, and then, you can directly use the Docker Image we provide, or you can build your own running environment:

Run with docker

Pull the docker image: docker pull jorhelp/isofusion, for users in Mainland China: docker pull registry.cn-hangzhou.aliyuncs.com/sdu-bioinfo/isofusion

Run:

docker run --name isofusion --runtime=nvidia  -v PATH_TO_MS1:/mnt isofusion python3 -Bu IsoFusion/run_IsoFusion.py --file /mnt/MS1_FILE --output /mnt/ --process_num 8 --gpu 0 --batch_size 512

Run without docker

Clone this repository by:

git clone https://github.com/xfcui/IsoFusion.git

Make sure the python version you use is >= 3.7, and install the packages by:

pip install -r requirements.txt

Optional arguments:

-h, --help                show this help message and exit
--file FILE               the target file (absolute path)
--output OUTPUT           the dir where results will be saved
--process_num PROCESS_NUM multiprocess
--gpu GPU                 specify the gpu num you use
--batch_size BATCH_SIZE   batch size will be used

Run:

./run_IsoFusion.py --file ~/dataset/ms1/***.ms1 --process_num 8 --gpu 0 --batch_size 256