Experimental standards for high-throughput proteomics.

Proteome analysis, utilizing high-throughput proteomics approaches, involves studying proteins that a whole organism (or specific tissue or cellular compartment) expresses under certain conditions. Intrinsic difficulties of these studies, as well as the enormous volumes of data they typically produce, make the proteome analysis and interpretation very difficult. As with any high-throughput approach, proteomics experiments should be carefully designed, analyzed, and verified. In addition to computational standards,experimental standards--simple and complex mixtures of known proteins--for high-throughput proteomics have to be developed and utilized. This article discusses such experimental standards and their implementations.

[1]  Alexey I Nesvizhskii,et al.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. , 2002, Analytical chemistry.

[2]  Steven P Gygi,et al.  Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations , 2005, Nature Methods.

[3]  R. Nadon,et al.  Inferential literacy for experimental high-throughput biology. , 2006, Trends in genetics : TIG.

[4]  E. Kolker,et al.  Spectral quality assessment for high-throughput tandem mass spectrometry proteomics. , 2004, Omics : a journal of integrative biology.

[5]  Dekel Tsur,et al.  Identification of post-translational modifications by blind search of mass spectra , 2005, Nature Biotechnology.

[6]  E. Kolker,et al.  LIP index for peptide classification using MS/MS and SEQUEST search via logistic regression. , 2004, Omics : a journal of integrative biology.

[7]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[8]  Henrik Antti,et al.  Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. , 2003, Toxicology and applied pharmacology.

[9]  Chris F. Taylor,et al.  The work of the Human Proteome Organisation's Proteomics Standards Initiative (HUPO PSI). , 2006, Omics : a journal of integrative biology.

[10]  Alexey I Nesvizhskii,et al.  Initial Proteome Analysis of Model Microorganism Haemophilus influenzae Strain Rd KW20 , 2003, Journal of bacteriology.

[11]  Michael J MacCoss,et al.  Quantitative comparison of proteomic data quality between a 2D and 3D quadrupole ion trap. , 2006, Analytical chemistry.

[12]  A. Nesvizhskii,et al.  Experimental protein mixture for validating tandem mass spectral analysis. , 2002, Omics : a journal of integrative biology.

[13]  David Han,et al.  Systematic Comparison of a Two-dimensional Ion Trap and a Three-dimensional Ion Trap Mass Spectrometer in Proteomics*S , 2005, Molecular & Cellular Proteomics.

[14]  Ruben Abagyan,et al.  Algorithms for high-density oligonucleotide array. , 2003, Current opinion in drug discovery & development.

[15]  Ruben Abagyan,et al.  Match-Only Integral Distribution (MOID) Algorithm for high-density oligonucleotide array analysis , 2002, BMC Bioinformatics.

[16]  Z. Szallasi,et al.  Reliability and reproducibility issues in DNA microarray measurements. , 2006, Trends in genetics : TIG.

[17]  P. Marriott,et al.  Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC x GC-TOFMS) for drug screening and confirmation. , 2004, Forensic science international.

[18]  Gordon A Anderson,et al.  Global profiling of Shewanella oneidensis MR-1: expression of hypothetical genes and improved functional annotations. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Michael Y. Galperin,et al.  Identification and functional analysis of ‘hypothetical’ genes expressed in Haemophilus influenzae , 2004 .

[20]  Carsten Warneke,et al.  Validation of atmospheric VOC measurements by proton-transfer-reaction mass spectrometry using a gas-chromatographic preseparation method. , 2003, Environmental science & technology.

[21]  Ilan Beer,et al.  Evaluation of prefractionation methods as a preparatory step for multidimensional based chromatography of serum proteins , 2005, Proteomics.

[22]  E. Kolker,et al.  Standard mixtures for proteome studies. , 2004, Omics : a journal of integrative biology.

[23]  R. Aebersold,et al.  Mass spectrometry-based proteomics , 2003, Nature.

[24]  W. Barrett,et al.  Differences among techniques for high‐abundant protein depletion , 2005, Proteomics.

[25]  Pavel A. Pevzner,et al.  Peptide sequence tags for fast database search in mass-spectrometry. , 2005 .

[26]  R. Shields,et al.  MIAME, we have a problem. , 2006, Trends in genetics : TIG.

[27]  William Stafford Noble,et al.  Peptide charge state determination for low-resolution tandem mass spectra , 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).

[28]  Roger E. Moore,et al.  Qscore: An algorithm for evaluating SEQUEST database search results , 2002, Journal of the American Society for Mass Spectrometry.

[29]  Joshua E. Elias,et al.  Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. , 2003, Journal of proteome research.

[30]  Gilbert S Omenn,et al.  An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis , 2005, Proteomics.

[31]  Richard D. Smith,et al.  Two-dimensional gas-phase separations coupled to mass spectrometry for analysis of complex mixtures. , 2005, Analytical chemistry.

[32]  Eugene A. Kapp,et al.  Overview of the HUPO Plasma Proteome Project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly‐available database , 2005, Proteomics.

[33]  Eugene Kolker,et al.  Randomized sequence databases for tandem mass spectrometry peptide and protein identification. , 2005, Omics : a journal of integrative biology.

[34]  R. Aebersold,et al.  A statistical model for identifying proteins by tandem mass spectrometry. , 2003, Analytical chemistry.

[35]  Eugene Kolker,et al.  Statistical analysis of global gene expression data: some practical considerations. , 2004, Current opinion in biotechnology.

[36]  Eugene Kolker,et al.  Charge state estimation for tandem mass spectrometry proteomics. , 2005, Omics : a journal of integrative biology.