DeepMASS2

DeepMASS2 is a cross-platform GUI software tool, which enables deep-learning based metabolite annotation via semantic similarity analysis of mass spectral language. This approach enables the prediction of structurally related metabolites for the unknown compounds. By considering the chemical space, these structurally related metabolites provide valuable information about the potential location of the unknown metabolites and assist in ranking candidates obtained from molecular structure databases.

video_DeepMASS-GUI.mov

News

• [10/2024] Using DeepMASS2, we made a web UI interface, check it out! Website
• [04/2025] We've enhanced DeepMASS with a distributed search feature. Curious how it works? See the guide: Distributed_ReadMe.md

Data Input Specifications

To ensure DeepMASS2 accurately identifies metabolites and correctly names output files, your input data must include specific metadata tags. While various formats are supported, the following specifications use the .mgf format as a reference.

For a practical example of a compatible .mgf file structure, please refer to the CASMI Example Dataset.

Mandatory Metadata Tags

1. Precursor m/z - Required
   This tag specifies the precursor ion mass-to-charge ratio ($m/z$). This is a fundamental requirement for the search engine to filter candidates within the structural databases.

   • Example: PRECURSOR_MZ=517.22098

2. Ion Mode - Required
   This specifies the polarity of the data, ensuring DeepMASS2 utilizes the correct deep-learning model (Positive vs. Negative) and reference libraries.

   • Positive mode: IONMODE=positive
   • Negative mode: IONMODE=negative

Optional & Recommended Tags

3. Automatic Naming - Recommended
   DeepMASS2 uses this tag to define the output filename for the exported semantic similarity analysis. If provided, the results will be saved as <COMPOUND_NAME>.csv.

   • Example: COMPOUND_NAME=challenge_0 will export results to challenge_0.csv.

4. Molecular Formula - Optional
   Adding the molecular formula helps the semantic similarity engine constrain potential chemical space, significantly improving the ranking accuracy of structurally related metabolites.

   • Example: FORMULA=C25H38O9

Installation

Please follow the following installation steps:

1. Install Anaconda or Miniconda

2. Download the lastest released version of source codes, unzip, enter:

    # download and unzip, then,
    cd DeepMASS2_GUI
   

3. For the installation of dependencies

   Use the following step for installation:

    conda env create -f environment.yml
    conda activate deepmass
   

   Or follow the steps below:

    (1) Create a new conda environment and activate:
            conda create -n deepmass python=3.8.13
            conda activate deepmass
   
    (2) Install dependency (note, for *MacOS* some dependency may install with conda manually):
   
            pip install -r requirements.txt
   

4. Download the dependent data.

   1. put the following files into data folder:

           DeepMassStructureDB-v1.1.csv
           references_index_negative_spec2vec.bin
           references_index_positive_spec2vec.bin
           references_spectrums_negative.pickle
           references_spectrums_positive.pickle
      

   2. put the following files into model folder:

           Ms2Vec_allGNPSnegative.hdf5
           Ms2Vec_allGNPSnegative.hdf5.syn1neg.npy
           Ms2Vec_allGNPSnegative.hdf5.wv.vectors.npy
           Ms2Vec_allGNPSpositive.hdf5
           Ms2Vec_allGNPSpositive.hdf5.syn1neg.npy
           Ms2Vec_allGNPSpositive.hdf5.wv.vectors.npy
      

5. Run DeepMASS

    python DeepMASS2.py
   

Release

• Version 0.99.0
• Version 0.99.1

Documentation

For the details on how to use DeepMASS, please check Ducomentation.

Citation

In preparation

Contact

Ji Hongchao
E-mail: ji.hongchao@foxmail.com

https://orcid.org/0000-0002-7364-0741

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