CoreMS 2.2.1

Mass Spectrometry Framework for Small Molecules Analysis

Table of Contents

• Introduction
  • CoreMS
  • Current Version
  • Contact Information
  • Contribution Information
  • Data Input
  • Data Output
  • Data Structure
  • Features
  • Overview Slides
  • Framework Documentation
• Installation
  • Installation
  • Thermo Raw File on Mac and Linux
• Execution
  • Jupyter Notebook and Docker containers
  • Simple Example
  • Python Examples
  • Jupyter Notebook Examples
• Sibling Projects
  • EnviroMS
  • MetaMS

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CoreMS

CoreMS is a comprehensive mass spectrometry framework for software development and data analysis of small molecules analysis.

Data handling and software development for modern mass spectrometry (MS) is an interdisciplinary endeavor requiring skills in computational science and a deep understanding of MS. To enable scientific software development to keep pace with fast improvements in MS technology, we have developed a Python software framework named CoreMS. The goal of the framework is to provide a fundamental, high-level basis for working with all mass spectrometry data types, allowing custom workflows for data signal processing, annotation, and curation. The data structures were designed with an intuitive, mass spectrometric hierarchical structure, thus allowing organized and easy access to the data and calculations. Moreover, CoreMS supports direct access for almost all vendors’ data formats, allowing for the centralization and automation of all data processing workflows from the raw signal to data annotation and curation.

CoreMS aims to provide

• logical mass spectrometric data structure
• self-containing data and metadata storage
• modern molecular formulae assignment algorithms
• dynamic molecular search space database search and generator

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Current Version

2.2.1

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Main Developers/Contact

• Yuri. E. Corilo
• William Kew

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Contributing

As an open source project, CoreMS welcomes contributions of all forms. Before contributing, please see our Dev Guide

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Data formats

Data input formats

• Bruker Solarix (CompassXtract)
• Bruker Solarix transients, ser and fid (FT magnitude mode only)
• ThermoFisher (.raw)
• Spectroswiss signal booster data-acquisition station (.hdf5)
• MagLab ICR data-acquisition station (FT and magnitude mode) (.dat)
• ANDI NetCDF for GC-MS (.cdf)
• Generic mass list in profile and centroid mde (include all delimiters types and Excel formats)
• CoreMS exported processed mass list files(excel, .csv, .txt, pandas dataframe as .pkl)
• CoreMS self-containing Hierarchical Data Format (.hdf5)
• Pandas Dataframe
• Support for cloud Storage using s3path.S3path(see examples of usage here: S3 Support)

Data output formats

• Pandas data frame (can be saved using pickle, h5, etc)
• Text Files (.csv, tab separated .txt, etc)
• Microsoft Excel (xlsx)
• Automatic JSON for metadata storage and reuse
• Self-containing Hierarchical Data Format (.hdf5) including raw data and ime-series data-point for processed data-sets with all associated metadata stored as json attributes

Data structure types

• LC-MS
• GC-MS
• Transient
• Mass Spectra
• Mass Spectrum
• Mass Spectral Peak
• Molecular Formula

In progress data structures

• IMS-MS
• LC-IMS-MS
• Collections
• Molecular Structure

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Available features

FT-MS Signal Processing, Calibration, and Molecular Formula Search and Assignment

• Apodization, Zerofilling, and Magnitude mode FT
• Manual and automatic noise threshold calculation
• Peak picking using apex quadratic fitting
• Experimental resolving power calculation
• Frequency and m/z domain calibration functions:
• LedFord equation
• Linear equation
• Quadratic equation
• Automatic search most abundant Ox homologue series
• Automatic local (SQLite) or external (PostgreSQL) database check, generation, and search
• Automatic molecular formulae assignments algorithm for ESI(-) MS for natural organic matter analysis
• Automatic fine isotopic structure calculation and search for all isotopes
• Flexible Kendrick normalization base
• Kendrick filter using density-based clustering
• Kendrick classification
• Heteroatoms classification and visualization

GC-MS Signal Processing, Calibration, and Compound Identification

• Baseline detection, subtraction, smoothing
• m/z based Chromatogram Peak Deconvolution,
• Manual and automatic noise threshold calculation
• First and second derivatives peak picking methods
• Peak Area Calculation
• Retention Index Calibration
• Automatic local (SQLite) or external (MongoDB or PostgreSQL) database check, generation, and search
• Automatic molecular match algorithm with all spectral similarity methods

High Resolution Mass Spectrum Simulations

• Peak shape (Lorentz, Gaussian, Voigt, and pseudo-Voigt)
• Peak fitting for peak shape definition
• Peak position in function of data points, signal to noise and resolving power (Lorentz and Gaussian)
• Prediction of mass error distribution
• Calculated ICR Resolving Power based on magnetic field (B), and transient time(T)

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Installation

pip install corems

By default the molecular formula database will be generated using SQLite

To use Postgresql the easiest way is to build a docker container:

docker-compose up -d

• Change the url_database on MSParameters.molecular_search.url_database to: "postgresql+psycopg2://coremsappdb:coremsapppnnl@localhost:5432/coremsapp"
• Set the url_database env variable COREMS_DATABASE_URL to: "postgresql+psycopg2://coremsappdb:coremsapppnnl@localhost:5432/coremsapp"

Thermo Raw File Access:

To be able to open thermo file a installation of pythonnet is needed:

• Windows:

  pip install pythonnet
  

• Mac and Linux:

  brew install mono
  pip install pythonnet   
  

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Docker stack

Another option to use CoreMS is to run the docker stack that will start the CoreMS containers

Molecular Database and Jupyter Notebook Docker Containers

A docker container containing:

• A custom python distribution will all dependencies installed
• A Jupyter notebook server with workflow examples
• A PostgreSQL database for the molecular formulae assignment

If you don't have docker installed, the easiest way is to install docker for desktop

1. Start the containers using docker-compose (easiest way):

   On docker-compose-jupyter.yml there is a volume mapping for the tests_data directory with the data provided for testing, to change to your data location:

   • locate the volumes on docker-compose-jupyter.yml:

   volumes:
     - ./tests/tests_data:/home/CoreMS/data
   

   • change "./tests/tests_data" to your data directory location

   volumes:
     - path_to_your_data_directory:/home/corems/data
   

   • save the file and then call:

   docker-compose -f docker-compose-jupyter.yml up
   

2. Another option is to manually build the containers:

   • Build the corems image:

     docker build -t corems:local .
     

   • Start the database container:

     docker-compose up -d   
     

   • Start the Jupyter Notebook:

     docker run --rm -v ./data:/home/CoreMS/data corems:local
     

   • Open your browser, copy and past the URL address provided in the terminal: http://localhost:8888/?token=<token>.

   • Open the CoreMS-Tutorial.ipynb

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Simple Script Example

More examples can be found under the directory examples/scripts, examples/notebooks

• Basic functionality example

from corems.transient.input.brukerSolarix import ReadBrukerSolarix
from corems.molecular_id.search.molecularFormulaSearch import SearchMolecularFormulas
from corems.mass_spectrum.output.export import HighResMassSpecExport
from matplotlib import pyplot

file_path= 'tests/tests_data/ftms/ESI_NEG_SRFA.d'

# Instatiate the Bruker Solarix reader with the filepath
bruker_reader = ReadBrukerSolarix(file_path)

# Use the reader to instatiate a transient object
bruker_transient_obj = bruker_reader.get_transient()

# Calculate the transient duration time
T =  bruker_transient_obj.transient_time

# Use the transient object to instatitate a mass spectrum object
mass_spectrum_obj = bruker_transient_obj.get_mass_spectrum(plot_result=False, auto_process=True)

# The following SearchMolecularFormulas function does the following
# - searches monoisotopic molecular formulas for all mass spectral peaks
# - calculates fine isotopic structure based on monoisotopic molecular formulas found and current dynamic range
# - searches molecular formulas of correspondent calculated isotopologues
# - settings are stored at SearchConfig.json and can be changed directly on the file or inside the framework class

SearchMolecularFormulas(mass_spectrum_obj, first_hit=False).run_worker_mass_spectrum()

# Iterate over mass spectral peaks objs within the mass_spectrum_obj
for mspeak in mass_spectrum_obj.sort_by_abundance():

    # If there is at least one molecular formula associated, mspeak returns True
    if  mspeak:

        # Get the molecular formula with the highest mass accuracy
        molecular_formula = mspeak.molecular_formula_lowest_error

        # Plot mz and peak height
        pyplot.plot(mspeak.mz_exp, mspeak.abundance, 'o', c='g')

        # Iterate over all molecular formulas associated with the ms peaks obj
        for molecular_formula in mspeak:

            # Check if the molecular formula is a isotopologue
            if molecular_formula.is_isotopologue:

                # Access the molecular formula text representation and print
                print (molecular_formula.string)

                # Get 13C atoms count
                print (molecular_formula['13C'])
    else:
        # Get mz and peak height
        print(mspeak.mz_exp,mspeak.abundance)

# Save data
## to a csv file
mass_spectrum_obj.to_csv("filename")
mass_spectrum_obj.to_hdf("filename")
# to pandas Datarame pickle
mass_spectrum_obj.to_pandas("filename")

# Extract data as a pandas Dataframe
df = mass_spectrum_obj.to_dataframe()

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UML Diagrams

UML (unified modeling language) diagrams for Direct Infusion FT-MS and GC-MS classes can be found here.

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Citing CoreMS

If you use CoreMS in your work, please use the following citation:

Version 2.2.1 Release on GitHub, archived on Zenodo:

Yuri E. Corilo, William R. Kew, Lee Ann McCue (2021, March 27). EMSL-Computing/CoreMS: CoreMS 2.0.1 (Version v2.0.1), as developed on Github. Zenodo. http://doi.org/10.5281/zenodo.4641552

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