Alignment of LC‐MS images, with applications to biomarker discovery and protein identification

Vandenbogaert, Mathias; Li-Thiao-Té, Sébastien; Kaltenbach, Hans‐Michael; Zhang, Runxuan; Aittokallio, Tero; Schwikowski, Benno

doi:10.1002/pmic.200700791

Cited by 96 publications

(68 citation statements)

References 105 publications

(250 reference statements)

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“…To adjust RT shifts in LC-MALDI-TOF maps, a discrete alignment method is expected to be more efficient than continuous methods, which have been developed and extensively investigated in LC-ESI-MS data [15]. Although a reasonable approximation can be made by a continuous alignment method to solve the discrete alignment optimization problem, an assumption must be made that the elution order of peptides is conserved throughout the experiments [5,15]. Second, the reproducibility level in LC-MALDI-TOF runs is relatively poor, which results from the semiquantitative nature of peak intensity due to sample preparation and matrix cocrystallization [16].…”

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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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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“…The elution time of m/z-rt pairs (i.e. features) is the key information used to associate the same compound detected in different runs (Vandenbogaert et al, 2008). In practice, elution times vary from sample to sample due to many factors (Quarry et al, 1984).…”

Section: Introductionmentioning

confidence: 99%

Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis

2015

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Motivation: Current informatic techniques for processing raw chromatography/mass spectrometry data break down under several common, non-ideal conditions. Importantly, hydrophilic liquid interaction chromatography (a key separation technology for metabolomics) produces data which are especially challenging to process. We identify three critical points of failure in current informatic workflows: compound specific drift, integration region variance, and naive missing value imputation. We implement the Warpgroup algorithm to address these challenges. Results: Warpgroup adds peak subregion detection, consensus integration bound detection, and intelligent missing value imputation steps to the conventional informatic workflow. When compared with the conventional workflow, Warpgroup made major improvements to the processed data. The coefficient of variation for peaks detected in replicate injections of a complex Escherichia Coli extract were halved (a reduction of 19%). Integration regions across samples were much more robust. Additionally, many signals lost by the conventional workflow were 'rescued' by the Warpgroup refinement, thereby resulting in greater analyte coverage in the processed data. Availability and implementation: Warpgroup is an open source R package available on GitHub at github.com/nathaniel-mahieu/warpgroup. The package includes example data and XCMS compatibility wrappers for ease of use.

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“…These algorithms have been extensively reviewed [9][10][11][12][13][14]. Most of these alignment algorithms belong to one of two general types [12].…”

Section: Introductionmentioning

confidence: 99%

Retention Time Alignment of LC/MS Data by a Divide-and-Conquer Algorithm

Zhang

2012

J. Am. Soc. Mass Spectrom.

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Liquid chromatography-mass spectrometry (LC/MS) has become the method of choice for characterizing complex mixtures. These analyses often involve quantitative comparison of components in multiple samples. To achieve automated sample comparison, the components of interest must be detected and identified, and their retention times aligned and peak areas calculated. This article describes a simple pairwise iterative retention time alignment algorithm, based on the divide-and-conquer approach, for alignment of ion features detected in LC/MS experiments. In this iterative algorithm, ion features in the sample run are first aligned with features in the reference run by applying a single constant shift of retention time. The sample chromatogram is then divided into two shorter chromatograms, which are aligned to the reference chromatogram the same way. Each shorter chromatogram is further divided into even shorter chromatograms. This process continues until each chromatogram is sufficiently narrow so that ion features within it have a similar retention time shift. In six pairwise LC/MS alignment examples containing a total of 6507 confirmed true corresponding feature pairs with retention time shifts up to five peak widths, the algorithm successfully aligned these features with an error rate of 0.2%. The alignment algorithm is demonstrated to be fast, robust, fully automatic, and superior to other algorithms. After alignment and gap-filling of detected ion features, their abundances can be tabulated for direct comparison between samples.

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Alignment of LC‐MS images, with applications to biomarker discovery and protein identification

Cited by 96 publications

References 105 publications

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

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

Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis

Retention Time Alignment of LC/MS Data by a Divide-and-Conquer Algorithm

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