Bioinformatics Methods in Clinical Research -

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Résumé :
Integrated bioinformatics solutions have become increasingly valuable in past years, as technological advances have allowed researchers to consider the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes, and as the costs of such techniques have begun to lessen. In Bioinformatics Methods in Clinical Research, experts examine the latest developments impacting clinical omics, and describe in great detail the algorithms that are currently used in publicly available software tools. Chapters discuss statistics, algorithms, automated methods of data retrieval, and experimental consideration in genomics, transcriptomics, proteomics, and metabolomics. Composed in the highly successful Methods in Molecular Biology? series format, each chapter contains a brief introduction, provides practical examples illustrating methods, results, and conclusions from data mining strategies wherever possible, and includes a Notes section which shares tips on troubleshooting and avoiding known pitfalls. Informative and ground-breaking, Bioinformatics Methods in Clinical Research establishes a much-needed bridge between theory and practice, making it an indispensable resource for bioinformatics researchers.

Sommaire:
Section I: Algorithms for interpreting MS and MS/MS data Introduction: Assigning peptides and proteins to MS spectra Itziar Frades, Ewa Gubb and Rune Matthiesen Identification of Post-translational Modifications via Blind Search of Mass-Spectra Stephen Tanner Peptide sequence tags for fast database search in mass-spectrometry Ari Frank De novo sequencing with PepHMM Ting Chen The Global Proteome Machine Organization Ron Beavis GutenTag: Software for automated sequence tag identification of peptides John Yates, III Manual analysis emulator Katheryn Resing Ascore for phosphorylation sites Steven Gygi SILVER Aldente: PEPTIDE MASS FINGERPRINTING TOOL Ron Appel Novel Peptide Identification using ESTs and Genomic Sequences Nathan Edwards Section II: Quantitative proteomics Overview of chemical labeling methods for quantitative proteomics Shabaz Mohammed Quantitative proteomics using SILAC Jens S. Andersen/Jakob Bunkenborg/Peter Mortensen MSquant ASAPRatio Li X-J XPRESS Han DK Quantitative algorithms in VEMS Rune Matthiesen Quantitation based on LC-MS intensity profiles Jennifer Listgarten Improving identification of protein complexes by using quantitative information Markus M?ller OpenMS Knut Reinert Challenges Related to Analysis of Protein Spot Volumes from Two-Dimensional Gel Electrophoresis Ellen Mosleth F?rgestad Quantitation from 2D gel spots Peter F. Lemkin SectionIII: Finding biomarkers in MS data Introduction: Classification by machine learning I?aki Inza inza Feature selection and machine learning with mass spectrometry data Susmita Datta Identification of biomarkers from mass spectrometry data using a 'common' peak approach Tadayoshi Fushiki Annotated regions of significance of SELDI-TOF-MS spectra for detecting protein biomarkers Yudi Pawitan Analysis of mass spectral serum profiles for biomarker selection Habtom W. Ressom Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data Hongyu Zhao A novel approach for clustering proteomics data using Bayesian fast Fourier transform Halima Bensmail A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS Martin McIntosh Semi-supervised LC/MS alignment for differential proteomics Bernd Fischer Perspective: A Program to Improve Protein Biomarker Discovery for Cancer Leland Hartwell Section IV: Data storage PRIDE: open source proteomics identifications database Phil Jones Data storage using CPAS Ted Holzman GPMDB: The Global Proteome Machine Organization Proteomics Database Data storage in dbVEMS PROTEIOS: an open source proteomics initiative Jari H?kkinen Database tool for differential peptide expression Mark K Titulaer Section V: System biology Ontologies and databases at EBI Sandra Orchard Towards understanding biological processes: a text mining approach Alberto