Metadata-Version: 2.1
Name: ms2query
Version: 0.3.0
Summary: Tool to query MS/MS spectra against GNPS library data.
Home-page: https://github.com/iomega/ms2query
Author: Netherlands eScience Center
Author-email: 
License: Apache Software License 2.0
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        <img src="https://github.com/iomega/ms2query/blob/main/images/ms2query_logo.svg" width="280">
        
        ### MS2Query - machine learning assisted library querying of MS/MS spectra.
        MS2Query is a tool for fast library searching for both analogs and true matches.
        
        
        ## Documentation for users
        ### Prepare environmnent
        We recommend to create an Anaconda environment with
        
        ```
        conda create --name ms2query python=3.8
        conda activate ms2query
        ```
        ### Pip install MS2Query
        MS2Query can simply be installed by running:
        ```
        pip install ms2query
        ```
        
        ### Run MS2Query
        Below you can find an example script for running MS2Query.
        Before running the script, replace the variables `ms2query_library_files_directory` and `ms2_spectra_directory` with the correct directories.
        
        This script will first download files for a default MS2Query library.
        This default library is trained on the [GNPS library](https://gnps.ucsd.edu/) from 2021-04-09.
        
        After downloading, a **library search** and an **analog search** is performed on the query spectra in your directory (`ms2_spectra_directory`).
        The results generated by MS2Query, are stored as csv files in a results directory within the same directory as your query spectra.
        
        Note: When running, Tensorflow often raises a few warnings on most computers. These warnings are raised when no GPU is installed and can be ignored. 
        
        ```python
        from ms2query.run_ms2query import download_default_models, default_library_file_base_names, run_complete_folder
        from ms2query.ms2library import create_library_object_from_one_dir
        
        # Set the location where all your downloaded model files are stored
        ms2query_library_files_directory = "./ms2query_library_files"
        # Define the folder in which your query spectra are stored.
        # Accepted formats are: "mzML", "json", "mgf", "msp", "mzxml", "usi" or a pickled matchms object. 
        ms2_spectra_directory = "specify_directory"
        
        # Downloads pretrained models and files for MS2Query (>10GB download)
        download_default_models(ms2query_library_files_directory, default_library_file_base_names())
        
        # Create a MS2Library object
        ms2library = create_library_object_from_one_dir(ms2query_library_files_directory, default_library_file_base_names())
        
        # Run library search and analog search on your files.
        run_complete_folder(ms2library, ms2_spectra_directory)
        
        ```
        ## Documentation for developers
        ### Prepare environmnent
        We recommend to create an Anaconda environment with
        
        ```
        conda create --name ms2query python=3.7
        conda activate ms2query
        ```
        ### Clone repository
        Clone the present repository, e.g. by running
        ```
        git clone https://github.com/iomega/ms2query.git
        ```
        And then install the required dependencies, e.g. by running the following from within the cloned directory
        ```
        pip install -e .
        ```
        To run all unit tests, to check if everything was installed successfully run: 
        ```
        pytest
        ```
        
        ## Contributing
        
        If you want to contribute to the development of ms2query,
        have a look at the [contribution guidelines](CONTRIBUTING.md).
        
        ## License
        
        Copyright (c) 2021, Netherlands eScience Center
        
        Licensed under the Apache License, Version 2.0 (the "License");
        you may not use this file except in compliance with the License.
        You may obtain a copy of the License at
        
        http://www.apache.org/licenses/LICENSE-2.0
        
        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        See the License for the specific language governing permissions and
        limitations under the License.
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
