Difference detection in LC-MS data for protein biomarker discovery

Neal, Radford M.; Roweis, Sam T.; Wong, Patrick; Emili, Andrew

doi:10.1093/bioinformatics/btl326

Cited by 86 publications

(91 citation statements)

References 11 publications

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“…For example, a recently introduced method called continuous profile model (CPM) has been applied for alignment and normalization of continuous time-series data and for detection of differences in multiple LC-MS data (6,17). Similarly, we developed a probabilistic mixture regression model (PMRM) for global alignment of LC-MS data (18,19).…”

Section: Alignmentmentioning

confidence: 99%

“…Thus, alignment with respect to both m/z and retention time is a prerequisite for quantitative comparison of proteins/peptides by LC-MS. Alignment algorithms have traditionally been used on data points and/or feature vectors of fixed dimension (5). Applications of these algorithms for LC-MS data alignment have been reported in the literature (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16). The most common approaches for aligning LC-MS data are based on the identification of landmarks or structural points (referring to the unique charge species in data) and the use of internal standards, respectively.…”

Section: Introductionmentioning

confidence: 99%

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LC-MS Data Analysis for Differential Protein Expression Detection

Varghese

Ressom

2010

Methods in Molecular Biology

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In proteomic studies, liquid chromatography coupled with mass spectrometry (LC-MS) is a common platform to compare the abundance of various peptides that characterize particular proteins in biological samples. Each LC-MS run generates data consisting of thousands of peak intensities for peptides represented by retention time (RT) and mass-to-charge ratio (m/z) values. In label-free differential protein expression studies, multiple LC-MS runs are compared to identify differentially abundant peptides between distinct biological groups. This approach presents a computational challenge because of the following reasons (i) substantial variation in RT across multiple runs due to the LC instrument conditions and the variable complexity of peptide mixtures, (ii) variation in m/z values due to occasional drift in the calibration of the mass spectrometry instrument, and (iii) variation in peak intensities caused by various factors including noise and variability in sample handling and processing. In this chapter, we present computational methods for quantification and comparison of peptides by label-free LC-MS analysis. We discuss data preprocessing methods for alignment and normalization of LC-MS data. Also, we present multivariate statistical methods and pattern recognition methods for detection of differential protein expression from preprocessed LC-MS data.

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Section: Alignmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LC-MS Data Analysis for Differential Protein Expression Detection

Varghese

Ressom

2010

Methods in Molecular Biology

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“…runs is well discussed in the literature [3,[12][13][14]. However, more research is needed to build alignment methods specifically targeted to LC-MALDI-TOF runs.…”

Section: After Alignmentmentioning

confidence: 99%

A new method for alignment of LC-MALDI-TOF data

Tang

Zhang

Amrita

et al. 2010

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Background: In proteomics studies, liquid chromatography coupled to mass spectrometry (LC-MS) has proven to be a powerful technology to investigate differential expression of proteins/peptides that are characterized by their peak intensities, mass-to-charge ratio (m/z), and retention time (RT). The variable complexity of peptide mixtures and occasional drifts lead to substantial variations in m/z and RT dimensions. Thus, label-free differential protein expression studies by LC-MS technology require alignment with respect to both RT and m/z to ensure that same proteins/peptides are compared from multiple runs.

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“…The first class uses full scan MS data to 'align' the retention times of one run to another. This alignment is performed by algorithms such as dynamic time warping or correlation optimized warping which find an optimal mapping of retention times between runs that maximizes their similarity [34][35][36]. The second class matches detected features (such as peptide isotope distributions or MS/MS spectra) between runs, and applies algorithms such as regression to fit a time correction function to the matched markers [29,37,38].…”

Section: Differential Ms Quantitationmentioning

confidence: 99%

Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics

Kline¹,

Finney²,

Wu³

2009

Briefings in Functional Genomics and Proteomics

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The ultimate goal of most shotgun proteomic pipelines is the discovery of novel biomarkers to direct the development of quantitative diagnostics for the detection and treatment of disease. Differential comparisons of biological samples identify candidate peptides that can serve as proxys of candidate proteins. While these discovery approaches are robust and fairly comprehensive, they have relatively low throughput. When merged with targeted mass spectrometry, this pipeline can fuel hypothesis-driven studies and the development of novel diagnostics and therapeutics.

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Difference detection in LC-MS data for protein biomarker discovery

Abstract: Our data are publicly available as a benchmark for further studies of this nature at http://www.cs.toronto.edu/~jenn/LCMS

Cited by 86 publications

References 11 publications

LC-MS Data Analysis for Differential Protein Expression Detection

LC-MS Data Analysis for Differential Protein Expression Detection

A new method for alignment of LC-MALDI-TOF data

Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics

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