Table of Contents

• Introduction

  • CoreMS
  • Current Version
  • Contact 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

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.

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

Current Version

2.5.2.beta

Main Developers/Contact

• Yuri. E. Corilo
• William Kew

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 Could 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
• IMS-MS (TODO)
• LC-IMS-MS (TODO)
• Collections (TODO)
• Transient
• Mass Spectra
• Mass Spectrum
• Mass Spectral Peak
• Molecular Formula
• Molecular Structure (TODO)

Available features

FT-MS Signal Processing

• Apodization, Zerofilling, and Magnitude mode FT
• Manual and automatic noise threshold calculation
• Peak picking using apex quadratic fitting
• Experimental resolving power calculation

GC-MS Signal Processing

• 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

GC-MS Calibration

• Retention Index Calibration

GC-MS Compound Identification

• Automatic local (SQLite) or external (MongoDB or PostgreSQL) database check, generation, and search
• Automatic molecular match algorithm with all spectral similarity methods

FT-MS Calibration

• Frequency and m/z domain calibration functions:
• LedFord equation [ref]
• Linear equation
• Quadratic equation
• Automatic search most abundant Ox homologue series
• Step fit ('walking calibration") based on the LedFord equation [ref]

FT-MS Molecular formulae search and assignment

• 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

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)

CoreMS 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   

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

Molecular Database and Jupyter Notebook 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

Simple Script Example

More examples can be found under the directory docs/example, docs/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'

#Bruker Solarix class reader
bruker_reader = ReadBrukerSolarix(file_path)

#access the transient object
bruker_transient_obj = bruker_reader.get_transient()

#calculates the transient duration time
T =  bruker_transient_obj.transient_time

#access the mass spectrum object
mass_spectrum_obj = bruker_transient_obj.get_mass_spectrum(plot_result=False, auto_process=True)

# - search monoisotopic molecular formulas for all mass spectral peaks
# - calculate fine isotopic structure based on monoisotopic molecular formulas found and current dynamic range
# - search 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
for mspeak in mass_spectrum_obj.sort_by_abundance():

    # returns true if there is at least one molecular formula associated
    # with the mass spectral peak
    # same as mspeak.is_assigned -- > bool
    if  mspeak:

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

        # plot mz and peak height, use mass_spectrum_obj.mz_exp to access all mz
        # and mass_spectrum_obj.mz_exp_profile to access mz with all available datapoints
        pyplot.plot(mspeak.mz_exp, mspeak.abundance, 'o', c='g')

        #or use 
        mspeak.plot(color="black", derivative=True, deriv_color='red')
        # iterate over all molecular formulae 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
                print (molecular_formula.string)

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


#exporting data
mass_spectrum_obj.to_csv("filename")

mass_spectrum_obj.to_hdf("filename")
# save pandas Datarame to pickle
mass_spectrum_obj.to_pandas("filename")
# get pandas Dataframe
df = mass_spectrum_obj.to_dataframe()

UML Diagrams

• Direct Infusion FT-MS:

  

• GC-MS:

  

Citing CoreMS

If you use CoreMS in your work, please use the following citation: Version 1.0.0 Release on GitHub, archived on Zenodo:

Yuri E. Corilo, William R. Kew, Lee Ann McCue (2021, March 27). EMSL-Computing/CoreMS: CoreMS 1.0.0 (Version v1.0.0), as developed on Github. Zenodo. http://doi.org/10.5281/zenodo.4641553

Disclaimer

This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights.

Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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