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…mehr
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(TM) 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.
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 emulatorKatheryn Resing Ascore for phosphorylation sites Steven Gygi SILVER Steven Gygi 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 Jens S. Andersen/Jakob Bunkenborg/Peter Mortensen 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 Müller OpenMS Knut Reinert Challenges Related to Analysis of Protein Spot Volumes from Two-Dimensional Gel Electrophoresis Ellen Mosleth Færgestad Quantitation from 2D gel spots Peter F. Lemkin SectionIII: Finding biomarkers in MS data Introduction: Classification by machine learning Iñaki 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 Ron Beavis Data storage in dbVEMS Rune Matthiesen PROTEIOS: an open source proteomics initiative Jari Häkkinen 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
From the reviews:
"This volume of Methods in Molecular Biology provides an overview of the recent improvements in the use of bioinformatics in clinical research. ... All chapters provide pertinent examples. ... The book is intended primarily for scientists involved in biomedical informatics (computational biologists, biostatisticians, computer scientists) who are active in the field of clinical research. This includes both first-time and seasoned students and biomedical researchers who are interested in using bioinformatics. ... It also presents a number of valuable approaches currently in use." (Ruben Mestril, Doody's Review Service, April, 2010)
"This book 'Bioinformatics Methods in Clinical Research edited by Rune Matthiesen' contains sixteen chapters, and results from scientific cooperation of academics, specialists in the main areas of investigation on bioinformatics methods, and applications in clinical research from centers and universities of Europe such as Denmark, Spain, Portugal as well as Canada and the United States of North America. ... several specific fields are discussed including biomarkers in breast cancer and computational methods for two-dimensional analyses in gels." (Sílvia Regina Cavani Jorge Santos, Brazilian Journal of Pharmaceutical Sciences, Spring, 2011)
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