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ViMMS 2.0

ViMMS 2.0: A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics - Published in JOSS (2022)

https://github.com/glasgowcompbio/vimms

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in JOSS metadata
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    3 of 7 committers (42.9%) from academic institutions
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

dda fragmentation metabolomics python simulation

Keywords from Contributors

mesh

Scientific Fields

Artificial Intelligence and Machine Learning Computer Science - 83% confidence
Economics Social Sciences - 63% confidence
Last synced: 4 months ago · JSON representation

Repository

A programmable and modular LC/MS simulator in Python

Basic Info
Statistics
  • Stars: 23
  • Watchers: 4
  • Forks: 7
  • Open Issues: 14
  • Releases: 10
Topics
dda fragmentation metabolomics python simulation
Created over 5 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Zenodo

README.md

ViMMS Logo

About

Liquid-Chromatography (LC) coupled with tandem mass spectrometry (MS/MS) is a prevalent technique for identifying small molecules in untargeted metabolomics. There are various strategies for acquiring MS/MS fragmentation spectra, but devising new methods is often challenging due to the absence of a structured environment where researchers can prototype, compare, and optimize strategies before testing on real equipment.

To solve this, we introduce the Virtual Metabolomics Mass Spectrometer (VIMMS), a flexible and modular framework designed to simulate fragmentation strategies in tandem mass spectrometry-based metabolomics.

Quick Start & Documentation

Eager to start using ViMMS? Take advantage of these resources: - Installation guide. - Visit our project documentation page: Documentation - Our Demo folder contains notebooks that demonstrate how to use the framework in a simulated environment. - For specific examples that accompany our publications, see the Example folder. - You can also find this quick guide on how to get started using ViMMS. - For instructions on publishing a release to PyPI, see the Release guide.

Development Setup

This repository uses pre-commit to automatically format code with Black and AutoPEP8 and to run flake8 checks. Install the development dependencies and set up the hooks with:

bash poetry install --with dev pre-commit install

You can run all hooks on the entire project anytime using:

bash pre-commit run --all-files

Key Features

ViMMS provides scan-level control simulation of the MS2 acquisition process in a virtual environment. You can generate new LC-MS/MS data based on empirical data or virtually replay a previous LC-MS/MS analysis using existing data, which allows for testing different fragmentation strategies. With ViMMS, you can evaluate diverse fragmentation strategies using real data, and extract the scan results as mzML files.

Moreover, ViMMS serves as a platform for the development, optimization, and testing of new fragmentation strategies. These strategies can be implemented by extending a Controller class in ViMMS, and can be tested on both the simulator and actual mass spectrometry instruments that support compatible APIs.

To see a more thorough explanation of the use cases of ViMMS, please refer to the Use Cases section.

Contributions

As an open-source project licensed under MIT, we welcomes all forms of contributions, including bug fixes, new features, and more. You can find our community contribution guidelines here.

Citing ViMMS

To cite ViMMS or read about the list of publications that are built on top of ViMMS, please refer to the Publications page. ViMMS is also actively presented in various computational biology venues.

Owner

  • Name: glasgowcompbio
  • Login: glasgowcompbio
  • Kind: organization

JOSS Publication

ViMMS 2.0: A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics
Published
March 30, 2022
DOI
Volume 7, Issue 71, Page 3990
Authors
Joe Wandy ORCID
Glasgow Polyomics, University of Glasgow, United Kingdom
Vinny Davies ORCID
School of Mathematics and Statistics, University of Glasgow, United Kingdom
Ross McBride ORCID
School of Computing Science, University of Glasgow, United Kingdom
Stefan Weidt
Glasgow Polyomics, University of Glasgow, United Kingdom
Simon Rogers ORCID
School of Computing Science, University of Glasgow, United Kingdom
Rónán Daly ORCID
Glasgow Polyomics, University of Glasgow, United Kingdom
Editor
Charlotte Soneson ORCID
Tags
mass spectrometry fragmentation simulation metabolomics
View PDF Review Thread Software Archive

GitHub Events

Total
  • Create event: 23
  • Release event: 3
  • Issues event: 1
  • Watch event: 3
  • Delete event: 19
  • Issue comment event: 2
  • Push event: 47
  • Pull request event: 39
  • Fork event: 1
Last Year
  • Create event: 23
  • Release event: 3
  • Issues event: 1
  • Watch event: 3
  • Delete event: 19
  • Issue comment event: 2
  • Push event: 47
  • Pull request event: 39
  • Fork event: 1

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 1,068
  • Total Committers: 7
  • Avg Commits per committer: 152.571
  • Development Distribution Score (DDS): 0.561
Past Year
  • Commits: 9
  • Committers: 3
  • Avg Commits per committer: 3.0
  • Development Distribution Score (DDS): 0.222
Top Committers
Name Email Commits
Joe Wandy j****y@g****m 469
VinnyDavies v****s@g****k 210
Simon Rogers s****s@g****m 190
unknown m****2@g****m 173
Rónán Daly r****y@g****k 14
Kei kuan To 2****t@s****k 9
dependabot[bot] 4****] 3
Committer Domains (Top 20 + Academic)
glasgow.ac.uk: 2 student.gla.ac.uk: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 73
  • Total pull requests: 98
  • Average time to close issues: 7 months
  • Average time to close pull requests: 21 days
  • Total issue authors: 11
  • Total pull request authors: 4
  • Average comments per issue: 1.97
  • Average comments per pull request: 0.37
  • Merged pull requests: 51
  • Bot issues: 0
  • Bot pull requests: 36
Past Year
  • Issues: 3
  • Pull requests: 52
  • Average time to close issues: 1 minute
  • Average time to close pull requests: about 3 hours
  • Issue authors: 2
  • Pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.04
  • Merged pull requests: 34
  • Bot issues: 0
  • Bot pull requests: 5
View more stats
Top Authors
Issue Authors
  • joewandy (40)
  • sdrogers (9)
  • vinnydavies (8)
  • mcbrider5002 (7)
  • RonanDaly (2)
  • samsonjm (1)
  • NitTza (1)
  • pisistrato (1)
  • MKoesters (1)
  • oolonek (1)
  • jspaezp (1)
Pull Request Authors
  • joewandy (53)
  • dependabot[bot] (36)
  • sdrogers (7)
  • vinnydavies (2)
Top Labels
Issue Labels
bug (22) enhancement (22) refactoring (8) question (6) discussion (3) priority (2) codex (2) invalid (1) documentation (1) good first issue (1)
Pull Request Labels
codex (44) dependencies (36) python (5) enhancement (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 33 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 13
  • Total maintainers: 1
pypi.org: vimms

A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics.

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 33 Last month
Rankings
Dependent packages count: 10.1%
Forks count: 13.3%
Stargazers count: 15.6%
Average: 17.2%
Dependent repos count: 21.5%
Downloads: 25.3%
Maintainers (1)
joewandy
Last synced: 4 months ago

Dependencies

Pipfile pypi
  • build * develop
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  • twine * develop
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  • jupyterlab *
  • loguru *
  • mass-spec-utils *
  • matplotlib *
  • networkx *
  • numpy *
  • pandas *
  • pipenv-setup *
  • psims *
  • pymzml ==2.4.7
  • pysmiles *
  • pytest *
  • pytest-cov *
  • requests *
  • scikit-learn *
  • scipy *
  • seaborn *
  • statsmodels *
  • tabulate *
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requirements.txt pypi
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  • kiwisolver ==1.3.2
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  • mkdocs ==1.2.2
  • mkdocs-autorefs ==0.2.1
  • mkdocstrings ==0.15.2
  • molmass ==2021.6.18
  • more-itertools ==8.4.0
  • nbclassic ==0.3.2
  • nbclient ==0.5.4
  • nbconvert ==6.1.0
  • nbformat ==5.1.3
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  • numpy ==1.21.2
  • orderedmultidict ==1.0.1
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  • parso ==0.8.2
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  • pep517 ==0.11.0
  • pexpect ==4.8.0
  • pickleshare ==0.7.5
  • pip-shims ==0.5.3
  • pipenv-setup ==3.1.1
  • pipfile ==0.0.2
  • pkginfo ==1.7.1
  • plette ==0.2.3
  • pluggy ==1.0.0
  • prometheus-client ==0.11.0
  • prompt-toolkit ==3.0.20
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  • psutil ==5.8.0
  • ptyprocess ==0.7.0
  • py ==1.10.0
  • pyDOE ==0.3.8
  • pycodestyle ==2.7.0
  • pycparser ==2.20
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  • pymdown-extensions ==8.2
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  • pyparsing ==2.4.7
  • pyrsistent ==0.18.0
  • pysmiles ==1.0.1
  • pytest ==6.2.5
  • pytest-cov ==2.12.1
  • python-dateutil ==2.8.2
  • pytkdocs ==0.11.1
  • pytz ==2021.1
  • pyyaml-env-tag ==0.1
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  • readme-renderer ==29.0
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  • requests-toolbelt ==0.9.1
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  • rfc3986 ==1.5.0
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  • scipy ==1.4.1
  • seaborn ==0.11.2
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  • sniffio ==1.2.0
  • sortedcontainers ==2.4.0
  • statsmodels ==0.12.2
  • tabulate ==0.8.9
  • terminado ==0.12.1
  • testpath ==0.5.0
  • threadpoolctl ==2.2.0
  • toml ==0.10.2
  • tomli ==1.2.1
  • tomlkit ==0.7.2
  • tornado ==6.1
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setup.py pypi
  • events *
  • gpy *
  • intervaltree *
  • ipyparallel *
  • joblib *
  • jsonpickle *
  • loguru *
  • mass-spec-utils *
  • matplotlib *
  • networkx *
  • numpy *
  • pandas *
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  • pymzml ==2.4.7
  • pysmiles *
  • requests *
  • scikit-learn *
  • scipy *
  • seaborn *
  • statsmodels *
  • tabulate *
  • tqdm *
.github/workflows/python-package.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
environment.yml conda
  • autopep8
  • flake8
  • intervaltree
  • ipyparallel
  • ipywidgets
  • joblib
  • jsonpickle
  • jupyterlab
  • loguru
  • matplotlib
  • networkx
  • numba
  • numpy
  • pandas
  • pip
  • plotly
  • pytest
  • pytest-cov
  • requests
  • scikit-learn
  • scipy
  • seaborn
  • statsmodels
  • tabulate
  • tqdm