Literature DB >> 16944896

Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping.

John T Prince1, Edward M Marcotte.   

Abstract

Mass spectrometry proteomics typically relies upon analyzing outcomes of single analyses; however, comparing raw data across multiple experiments should enhance both peptide/protein identification and quantitation. In the absence of convincing tandem MS identifications, comparing peptide quantities between experiments (or fractions) requires the chromatographic alignment of MS signals. An extension of dynamic time warping (DTW), termed ordered bijective interpolated warping (OBI-Warp), is presented and used to align a variety of electrospray ionization liquid chromatography mass spectrometry (ESI-LC-MS) proteomics data sets. An algorithm to produce a bijective (one-to-one) function from DTW output is coupled with piecewise cubic hermite interpolation to produce a smooth warping function. Data sets were chosen to represent a broad selection of ESI-LC-MS alignment cases. High confidence, overlapping tandem mass spectra are used as standards to optimize and compare alignment parameters. We determine that Pearson's correlation coefficient as a measure of spectra similarity outperforms covariance, dot product, and Euclidean distance in its ability to produce correct alignments with optimal and suboptimal alignment parameters. We demonstrate the importance of penalizing gaps for best alignments. Using optimized parameters, we show that OBI-Warp produces alignments consistent with time standards across these data sets. The source and executables are released under MIT style license at http://obi-warp.sourceforge.net/.

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Year:  2006        PMID: 16944896     DOI: 10.1021/ac0605344

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  67 in total

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

Authors:  Nathaniel G Mahieu; Jonathan L Spalding; Gary J Patti
Journal:  Bioinformatics       Date:  2015-09-30       Impact factor: 6.937

Review 3.  Accurate mass measurements in proteomics.

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4.  Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction.

Authors:  Magnus Palmblad; Davinia J Mills; Laurence V Bindschedler; Rainer Cramer
Journal:  J Am Soc Mass Spectrom       Date:  2007-07-26       Impact factor: 3.109

Review 5.  Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics.

Authors:  Kelli G Kline; Greg L Finney; Christine C Wu
Journal:  Brief Funct Genomic Proteomic       Date:  2009-03

6.  Multi-class alignment of LC-MS data using probabilistic-based mixture regression models.

Authors:  Getachew K Befekadu; Mahlet G Tadesse; Yetrib Hathout; Habtom W Ressom
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008

7.  Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments.

Authors:  Bedilu Alamirie Ejigu; Dirk Valkenborg; Geert Baggerman; Manu Vanaerschot; Erwin Witters; Jean-Claude Dujardin; Tomasz Burzykowski; Maya Berg
Journal:  OMICS       Date:  2013-06-29

Review 8.  Image analysis tools and emerging algorithms for expression proteomics.

Authors:  Andrew W Dowsey; Jane A English; Frederique Lisacek; Jeffrey S Morris; Guang-Zhong Yang; Michael J Dunn
Journal:  Proteomics       Date:  2010-12       Impact factor: 3.984

9.  Large-Scale and Targeted Quantitative Cross-Linking MS Using Isotope-Labeled Protein Interaction Reporter (PIR) Cross-Linkers.

Authors:  Xuefei Zhong; Arti T Navare; Juan D Chavez; Jimmy K Eng; Devin K Schweppe; James E Bruce
Journal:  J Proteome Res       Date:  2016-11-30       Impact factor: 4.466

10.  Methods and Challenges for Computational Data Analysis for DNA Adductomics.

Authors:  Scott J Walmsley; Jingshu Guo; Jinhua Wang; Peter W Villalta; Robert J Turesky
Journal:  Chem Res Toxicol       Date:  2019-11-06       Impact factor: 3.739

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