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Mass Spectrometry: Complex Analysis

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...

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LC-MS alignment in theory and practice: a comprehensive algorithmic review.

Rob Smith, Dan Ventura, John T Prince

Briefings in Bioinformatics

|November 26, 2013

Summary

This review details 50 correspondence algorithms for liquid chromatography-mass spectrometry (LC-MS) data analysis. It aims to simplify algorithm selection for proteomics, lipidomics, and metabolomics studies by analyzing limitations and proposing new approaches.

Keywords:

LC-MS Alignment LC-MS Correspondence LC-MS Registration

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Area of Science:

Analytical Chemistry
Biochemistry
Computational Biology

Background:

Liquid chromatography-mass spectrometry (LC-MS) is crucial for comparative analysis in omics fields.
Accurate data registration between runs is essential for statistical comparison.
Alignment algorithms are commonly used for matching analytes across samples.

Purpose of the Study:

To provide a comprehensive overview of 50 correspondence algorithms for LC-MS data.
To assist researchers in selecting appropriate algorithms for their specific applications.
To identify limitations of current correspondence methods and suggest future directions.

Main Methods:

Systematic review and description of 50 correspondence algorithms.
Analysis of algorithm motivations, limitations, and performance characteristics.
Discussion of challenges including runtime, parameter tuning, and reference sample dependency.

Main Results:

Categorization and detailed explanation of 50 distinct correspondence algorithms.
Identification of common limitations such as computational cost and model constraints.
Highlighting the need for advanced approaches beyond traditional alignment.

Conclusions:

A broad selection of correspondence algorithms exists, posing a challenge for researchers.
Current methods have limitations that hinder efficient and accurate data registration.
A paradigm shift leveraging inherent LC-MS behavior is proposed to overcome existing challenges.