From d0ff635d40bb9f5b9d1bc9017d64251c09b9e2c9 Mon Sep 17 00:00:00 2001 From: Chris Bielow Date: Tue, 27 Aug 2024 14:44:35 +0200 Subject: [PATCH] enhance glossary and links --- docs/source/index.rst | 10 +++++----- docs/source/user_guide/adduct_detection.rst | 2 +- docs/source/user_guide/glossary.rst | 18 +++++++++++++++--- .../untargeted_metabolomics_preprocessing.rst | 4 ++-- 4 files changed, 23 insertions(+), 11 deletions(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index 369393ab0..154758f3c 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,13 +1,13 @@ Summary ======= -pyOpenMS is an open-source Python library for :term:`mass spectrometry`, specifically for the analysis of proteomics +pyOpenMS is an open-source Python library for :term:`mass spectrometry`, specifically for the analysis of proteomics and metabolomics data in Python. pyOpenMS implements a set of Python bindings to the OpenMS library for computational -:term:`mass spectrometry` and is available for Windows, Linux and macOS. +:term:`mass spectrometry` and is available for Windows, Linux and macOS. pyOpenMS provides functionality that is commonly used in computational mass spectrometry. The pyOpenMS package contains Python bindings for a large part of the `OpenMS `_ -library for :term:`mass spectrometry` based proteomics. It thus provides easy access to +library for :term:`mass spectrometry` based proteomics. It thus provides easy access to a feature-rich, open-source algorithm library for mass-spectrometry based proteomics analysis. pyOpenMS facilitates the execution of common tasks in proteomics (and other fields of mass spectrometry) such as @@ -20,8 +20,8 @@ pyOpenMS facilitates the execution of common tasks in proteomics (and other fiel - Chromatogram analysis (chromatographic peak picking, smoothing, elution profiles and peak scoring for :term:`SRM`/MRM/PRM/:term:`SWATH`/DIA data) - Interaction with common tools in proteomics and metabolomics: - - Search engines such as Comet, Mascot, MSGF+, MSFragger, SpectraST, XTandem - - Post-processing tools such as Percolator, MSstats, Fido + - Search engines such as Comet, Mascot, MSGF+, MSFragger, SpectraST, Sage + - Post-processing tools such as Percolator, MSstats, Epiphany - Metabolomics tools such as SIRIUS, CSI:FingerId diff --git a/docs/source/user_guide/adduct_detection.rst b/docs/source/user_guide/adduct_detection.rst index 6c47c8266..fb0e6c61d 100644 --- a/docs/source/user_guide/adduct_detection.rst +++ b/docs/source/user_guide/adduct_detection.rst @@ -16,7 +16,7 @@ In pyOpenMS, :py:class:`~.MetaboliteFeatureDeconvolution` takes a :term:`feature | **Suggested follow up step:** | The resulting feature map can be exported to a pandas DataFrame with adduct information from the *dc_charge_adducts* feature meta values. -| Multiple feature maps can be `combined using the feature linking algorithms `_. Each consensus feature will get a new meta value *best ion* based on the most common annotated adduct within the consensus feature group. +| Multiple :term:`feature maps` can be `combined using the feature linking algorithms `_. Each consensus feature will get a new meta value *best ion* based on the most common annotated adduct within the consensus feature group. .. code-block:: python diff --git a/docs/source/user_guide/glossary.rst b/docs/source/user_guide/glossary.rst index dd38bd13c..5526d81a6 100644 --- a/docs/source/user_guide/glossary.rst +++ b/docs/source/user_guide/glossary.rst @@ -38,14 +38,22 @@ A glossary of common terms used throughout OpenMS documentation. TOF time-of-flight Time-of-flight (TOF) is the time taken by an object, particle or wave (be it acoustic, electromagnetic, e.t.c) to travel a distance through a medium. + TOF analyzers can obtain good, but not ultra-high resolution, such as :term:`orbitrap`s. quadrupole - A mass filter allowing one mass channel at a time to reach the detector as the mass range is scanned. + A mass filter allowing one mass channel at a time to reach the detector as the mass range is scanned. A low resolution MS analyzer. orbitrap In MS, an ion trap mass analyzer consisting of an outer barrel-like electrode and a coaxial inner spindle-like electrode that traps ions in an orbital motion around the spindle. - A high resolution MS analyzer. + An ultra-high resolution MS analyzer, capable of resolving fine-isotope structure. + + Mass Spectrometry + MS + An analytical technique to measure the mass over charge (m/z) ratio of ions along with their abundance. This gives rise to a mass spectrum (with m/z on the x-axis and abundance on the y-axis). + + mass spectrum + A visual or numerical representation of a measurement from an MS instrument. A spectrum contains (usually many) pairs of mass-over-charge(m/z)+intensity values. MS1 Mass spectra of a sample where only precursor ions (i.e. no fragment ions) can be observed. @@ -150,9 +158,13 @@ A glossary of common terms used throughout OpenMS documentation. OpenMS API A C++ interface that allows developers to use OpenMS core library classes and methods. + feature + features + A feature, in the OpenMS terminology, subsumes all m/z signals originating from a single compound at a certain charge state. This includes the isotope pattern and usually spans multiple spectra in retention time (the elution profile). + feature maps feature map - A feature map is a collection of features (all signal originating from a single compound at a certain charge state) identified from a single experiment. + A feature map is a collection of :term:`feature`s identified from a single experiment. One feature map usually contains many features. OpenMS represents a feature map using the class `FeatureMap `_. consensus features diff --git a/docs/source/user_guide/untargeted_metabolomics_preprocessing.rst b/docs/source/user_guide/untargeted_metabolomics_preprocessing.rst index 6ae1ece40..7064019fc 100644 --- a/docs/source/user_guide/untargeted_metabolomics_preprocessing.rst +++ b/docs/source/user_guide/untargeted_metabolomics_preprocessing.rst @@ -1,9 +1,9 @@ Untargeted Metabolomics Pre-Processing ====================================== -The universal workflow for untargeted :terM:`metabolomics` always consists of :terM:`feature` detection in the individual MS sample +The universal workflow for untargeted metabolomics always consists of :term:`feature` detection in the individual MS sample files and their linkage to :term:`consensus features` with common m/z and retention time values. -In addition, there are optional steps such as adduct detection and annotation of :terM:`features` with associated :term:`MS2` spectra. +In addition, there are optional steps such as adduct detection and annotation of :term:`features` with associated :term:`MS2` spectra. .. image:: img/metabolomics-preprocessing.png