Literature DB >> 21593992

Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects.

Yuliya V Karpievitch1, Ashoka D Polpitiya, Gordon A Anderson, Richard D Smith, Alan R Dabney.   

Abstract

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.

Entities:  

Year:  2010        PMID: 21593992      PMCID: PMC3095207          DOI: 10.1214/10-AOAS341

Source DB:  PubMed          Journal:  Ann Appl Stat        ISSN: 1932-6157            Impact factor:   2.083


  110 in total

1.  Imaging mass spectrometry: a new technology for the analysis of protein expression in mammalian tissues.

Authors:  M Stoeckli; P Chaurand; D E Hallahan; R M Caprioli
Journal:  Nat Med       Date:  2001-04       Impact factor: 53.440

2.  Accurate quantitation of protein expression and site-specific phosphorylation.

Authors:  Y Oda; K Huang; F R Cross; D Cowburn; B T Chait
Journal:  Proc Natl Acad Sci U S A       Date:  1999-06-08       Impact factor: 11.205

3.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

Authors:  Andrew Keller; Alexey I Nesvizhskii; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2002-10-15       Impact factor: 6.986

4.  PepNovo: de novo peptide sequencing via probabilistic network modeling.

Authors:  Ari Frank; Pavel Pevzner
Journal:  Anal Chem       Date:  2005-02-15       Impact factor: 6.986

5.  Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research.

Authors:  Sebastian Wiese; Kai A Reidegeld; Helmut E Meyer; Bettina Warscheid
Journal:  Proteomics       Date:  2007-02       Impact factor: 3.984

6.  Analysis of complex protein mixtures with improved sequence coverage using (CE-MS/MS)n.

Authors:  Selynda Garza; Mehdi Moini
Journal:  Anal Chem       Date:  2006-10-15       Impact factor: 6.986

7.  Direct analysis of protein complexes using mass spectrometry.

Authors:  A J Link; J Eng; D M Schieltz; E Carmack; G J Mize; D R Morris; B M Garvik; J R Yates
Journal:  Nat Biotechnol       Date:  1999-07       Impact factor: 54.908

8.  Community proteomics of a natural microbial biofilm.

Authors:  Rachna J Ram; Nathan C Verberkmoes; Michael P Thelen; Gene W Tyson; Brett J Baker; Robert C Blake; Manesh Shah; Robert L Hettich; Jillian F Banfield
Journal:  Science       Date:  2005-05-05       Impact factor: 47.728

9.  A reanalysis of a published Affymetrix GeneChip control dataset.

Authors:  Alan R Dabney; John D Storey
Journal:  Genome Biol       Date:  2006-03-22       Impact factor: 13.583

10.  Comparison of CID versus ETD based MS/MS fragmentation for the analysis of protein ubiquitination.

Authors:  Frank Sobott; Stephen J Watt; Julia Smith; Mariola J Edelmann; Holger B Kramer; Benedikt M Kessler
Journal:  J Am Soc Mass Spectrom       Date:  2009-05-18       Impact factor: 3.109

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  34 in total

1.  Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

Authors:  Carmen D Tekwe; Raymond J Carroll; Alan R Dabney
Journal:  Bioinformatics       Date:  2012-05-24       Impact factor: 6.937

2.  Profile-Based LC-MS data alignment--a Bayesian approach.

Authors:  Tsung-Heng Tsai; Mahlet G Tadesse; Yue Wang; Habtom W Ressom
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2013 Mar-Apr       Impact factor: 3.710

3.  Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards.

Authors:  Tsung-Heng Tsai; Mahlet G Tadesse; Cristina Di Poto; Lewis K Pannell; Yehia Mechref; Yue Wang; Habtom W Ressom
Journal:  Bioinformatics       Date:  2013-09-06       Impact factor: 6.937

4.  Preprocessing and Analysis of LC-MS-Based Proteomic Data.

Authors:  Tsung-Heng Tsai; Minkun Wang; Habtom W Ressom
Journal:  Methods Mol Biol       Date:  2016

Review 5.  Advances in Detection of Kidney Transplant Injury.

Authors:  Sanjeeva Herath; Jonathan Erlich; Amy Y M Au; Zoltán H Endre
Journal:  Mol Diagn Ther       Date:  2019-06       Impact factor: 4.074

6.  ESTIMATION AND INFERENCE IN METABOLOMICS WITH NON-RANDOM MISSING DATA AND LATENT FACTORS.

Authors:  Chris McKennan; Carole Ober; Dan Nicolae
Journal:  Ann Appl Stat       Date:  2020-06-29       Impact factor: 2.083

Review 7.  Understanding nanoparticle endocytosis to improve targeting strategies in nanomedicine.

Authors:  Mauro Sousa de Almeida; Eva Susnik; Barbara Drasler; Patricia Taladriz-Blanco; Alke Petri-Fink; Barbara Rothen-Rutishauser
Journal:  Chem Soc Rev       Date:  2021-03-05       Impact factor: 54.564

Review 8.  Advances in diagnostics for transplant rejection.

Authors:  Michael Nasr; Tara Sigdel; Minnie Sarwal
Journal:  Expert Rev Mol Diagn       Date:  2016-10       Impact factor: 5.225

9.  Estimation of low-level components lost through chromatographic separations with finite detection limits.

Authors:  Nicole M Devitt; Joe M Davis; Mark R Schure
Journal:  J Chromatogr A       Date:  2020-05-31       Impact factor: 4.759

10.  Multi-Omic Single-Shot Technology for Integrated Proteome and Lipidome Analysis.

Authors:  Yuchen He; Edrees H Rashan; Vanessa Linke; Evgenia Shishkova; Alexander S Hebert; Adam Jochem; Michael S Westphall; David J Pagliarini; Katherine A Overmyer; Joshua J Coon
Journal:  Anal Chem       Date:  2021-02-22       Impact factor: 6.986

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