A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS

Bellew, Matthew; Coram, Marc; Fitzgibbon, Matthew; Igra, Mark; Randolph, Tim; Wang, Pei; May, Damon; Eng, Jimmy K.; Fang, Ruihua; Lin, Chenwei; Chen, Jinzhi; Goodlett, David R.; Whiteaker, Jeffrey R.; Paulovich, Amanda G.; McIntosh, Martin W.

doi:10.1093/bioinformatics/btl276

Cited by 254 publications

(284 citation statements)

References 19 publications

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“…In practice, the characteristic shape of a metabolite feature can be highly varied. Features are defined using a wide array of existing algorithms, such as XCMS [25], msInspect [26], mzMine [27], commercial options, and many more [28,29]. As an open-source application, XCMS is the most widely used of these algorithms, and the recently described CentWave peak finding strategy is an important development [30].…”

Section: Feature Finding and Prioritizationmentioning

confidence: 99%

Dealing with the unknown: Metabolomics and Metabolite Atlases

Bowen

Northen

2010

J. Am. Soc. Mass Spectrom.

182

158

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Metabolomics is the comprehensive profiling of the small molecule composition of a biological sample. Since metabolites are often the indirect products of gene expression, this approach is being used to provide new insights into a variety of biological systems (clinical, bioenergy, etc.). A grand challenge for metabolomics is the complexity of the data, which often include many experimental artifacts. This is compounded by the tremendous chemical diversity of metabolites. Identification of each uncharacterized metabolite is in many ways its own puzzle (compared with proteomics, which is based on predictable fragmentation patterns of polypeptides). Therefore, effective data reduction/prioritization strategies are critical for this rapidly developing field. Here we review liquid chromatography electrospray ionization mass spectrometry (LC/MS)-based metabolomics, methods for feature finding/prioritization, approaches for identifying unknown metabolites, and construction of method specific 'Metabolite Atlases'.(J Am Soc Mass Spectrom 2010, 21, 1471-1476) © 2010 Published by Elsevier Inc. on behalf of American Society for Mass Spectrometry N ature utilizes a tremendous diversity of metabolites, and it is estimated that there are Ͼ200,000 plant metabolites alone [1]. Metabolomics is a rapidly growing field for studying the small molecule composition of a biological system. Liquid chromatography coupled to electrospray ionization mass spectrometry (LC/MS) is becoming a method of choice for profiling metabolites in complex biological samples. This is due to its ability to effectively ionize a breath of metabolites with minimal fragmentation (versus gas chromatography-mass spectroscopy (GC/MS), robustness, and ability to scale-up to support tandem mass spectrometry based structural studies (versus capillary electrophoresis-mass spectrometry (CE-MS)) [2]. However, known metabolites are typically a small portion of the data obtained in a LC/MS metabolomics experiment (ϳ10%) [3] where the bulk are MS-artifacts and uncharacterized metabolites.Identification of unknown metabolites is cost and effort intensive, often requiring preparative scale isolation for nuclear magnetic resonance (NMR) studies or extensive chemical synthesis to enable structural comparisons using tandem mass spectrometry (MS/MS) [4]. Therefore, it is not surprising that the majority of reports focus on changes in metabolites that have authentic standards or at a minimum are found in metabolite databases. Yet, there are only a few thousand commercially available analytical standards due to lack of demand and the inherent instability of many metabolites. These standards and the endogenous metabolites contained in databases represent only a portion of endogenous metabolites. Therefore, effective methods for prioritizing, studying, and ultimately identifying uncharacterized metabolites is a critical development for LC/MS based metabolomics [5][6][7][8][9][10]. Here we discuss current LC/MS metabolomic approaches (detailed elsewhere [2,4,11,12]) and their inte...

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Section: Feature Finding and Prioritizationmentioning

confidence: 99%

Dealing with the unknown: Metabolomics and Metabolite Atlases

Bowen

Northen

2010

J. Am. Soc. Mass Spectrom.

182

158

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“…The AMT database and normalized peptide feature files (A), now on a common mass and time scale, are then matched using two-dimensional divisive clustering approaches previously described. 2 The result is a set of peptide LC-MS feature files with amino acid sequences assigned to each matching peptide location (B). We then construct a decoy AMT database (as suggested by Smith et al 5 ) and perform the same matching procedures against the decoy database to approximate assignment error.…”

Section: Methodsmentioning

confidence: 99%

“…The msInspect/AMT pipeline contains optional components allowing for, but not requiring, tight integration between the Computational Proteomics Analysis System 10 and msInspect. 2 Specifically, msInspect may be used to browse a CPAS site and select a set of LC-MS/MS data files for use in building the AMT database. This client application uses the caBIG client API provided by CPAS, which conforms to caBIG requirements.…”

Section: Methodsmentioning

confidence: 99%

“…1 LC-MS data provides comparatively greater surveys of the constituent peptides in a complex mixture than LC-MS/MS approaches and has gained popularity recently due in part to advancements in high-resolution instrumentation (LTQ-FT, Orbitrap, and hybrid time-of-flight [TOF] platforms). Multiple samples may be compared using LC-MS data with recently developed advanced bioinformatics approaches (msInspect, 2 MapQuant, 3 etc.) to locate the mass and retention times of peptides in an experiment and associate the peptide locations across multiple experiments.…”

Section: Introductionmentioning

confidence: 99%

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A Platform for Accurate Mass and Time Analyses of Mass Spectrometry Data

et al. 2007

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We describe an integrated suite of algorithms and software for general accurate mass and time (AMT) tagging data analysis of mass spectrometry data. The AMT approach combines identifications from liquid chromatography (LC) tandem mass spectrometry (MS/MS) data with peptide accurate mass and retention time locations from high-resolution LC-MS data. Our workflow includes the traditional AMT approach, in which MS/MS identifications are located in external databases, as well as methods based on more recent hybrid instruments such as the LTQ-FT or Orbitrap, where MS/MS identifications are embedded with the MS data. We demonstrate our AMT workflow's utility for general data synthesis by combining data from two dissimilar biospecimens. Specifically, we demonstrate its use relevant to serum biomarker discovery by identifying which peptides sequenced by MS/MS analysis of tumor tissue may also be present in the plasma of tumor-bearing and control mice. The analysis workflow, referred to as msInspect/AMT, extends and combines existing open-source platforms for LC-MS/MS (CPAS) and LC-MS (msInspect) data analysis and is available in an unrestricted open-source distribution.

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“…Existing methods do not adequately address the computational challenges of XIC detection and multiscan alignment and are limited to a small number of scans. Several of these methods rely on image processing to collect quantitative data from LC-MS/MS scans (9)(10)(11)(12). Because image processing typically requires computation on all individual pixels, these methods require a large amount of computation per LC-MS/MS scan.…”

Section: Background Lc-ms/ms Datamentioning

confidence: 99%

Protein quantification across hundreds of experimental conditions

Khan

Bloom

Garcia³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use spacepartitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graphtheoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.kd-tree ͉ orthogonal range query ͉ quantitative proteomics ͉ space partitioning data structures ͉ tandem mass spectrometry

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A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS

Cited by 254 publications

References 19 publications

Dealing with the unknown: Metabolomics and Metabolite Atlases

Dealing with the unknown: Metabolomics and Metabolite Atlases

A Platform for Accurate Mass and Time Analyses of Mass Spectrometry Data

Protein quantification across hundreds of experimental conditions

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