With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field. The book teaches the reader how to perform proteomic analysis by mass spectrometry and how to interpret the large amount of data collected.
If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Skip to Main Content. First published: 4 January About this book The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments.
Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises.
List of mass spectrometry software - Wikipedia
Provides both clear and rigorous descriptions of methods and approaches. Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches and statistical and computational methods.
Export Citation s. Export Citation. Plain Text. Citation file or direct import. Bramer, K. Stratton, A. Stratton, K. Administrative, technical, or material support i. Bramer, M. NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses. Skip to main content. Focus on Computer Resources. Bobbie-Jo M.
Webb-Robertson , Lisa M. Bramer , Jeffrey L. Jensen , Markus A. Kobold , Kelly G. Stratton , Amanda M. White and Karin D. DOI: Abstract P-MartCancer is an interactive web-based software environment that enables statistical analyses of peptide or protein data, quantitated from mass spectrometry—based global proteomics experiments, without requiring in-depth knowledge of statistical programming. Figure 1. Quality control processing A challenge with proteomics data is preprocessing in a manner that does not ignore the different sources of variability that contribute to the complexity of these datasets.
Differential statistics Statistical analysis of peptide, gene, or protein-level data is currently focused on quantitative ANOVA-based methods and qualitative G-test methods Protein quantification There are numerous approaches to quantify proteins from the measured peptide-level data Exploratory data analysis P-MartCancer offers two exploratory data analysis capabilities. Disclosure of Potential Conflicts of Interest No potential conflicts of interest were disclosed. Authors' Contributions Conception and design: B. Stratton Development of methodology: B.
Stratton Analysis and interpretation of data e. Rodland Administrative, technical, or material support i. References 1. Phosphotyrosine signaling analysis in human tumors is confounded by systemic ischemia-driven artifacts and intra-specimen heterogeneity.
Cancer Res ; 75 : — Proteogenomics connects somatic mutations to signalling in breast cancer. Nature ; : 55 — Proteomic analysis of colon and rectal carcinoma using standard and customized databases.
Sci Data ; 2 : Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell ; : — J Proteome Res ; 16 : — Bayesian proteoform modeling improves protein quantification of global proteomic measurements.
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Mol Cell Proteomics ; 13 : — Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. J Proteome Res ; 14 : — Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol ; 11 : R Improved quality control processing of peptide-centric LC-MS proteomics data. Bioinformatics ; 27 : — Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data.
Computational and Statistical Analysis of Protein Mass Spectrometry Data
J Proteome Res ; 9 : — A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments. Proteomics ; 13 : — DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics ; 24 : — 8. Previous Next. Back to top. November Volume 77, Issue Search for this keyword. Sign up for alerts.
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