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Bioinformatics Methods in Clinical Research

  • Fester Einband
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Covering the latest developments in clinical omics, this volume details the algorithms currently used in publicly available softwa... Weiterlesen
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Beschreibung

Covering the latest developments in clinical omics, this volume details the algorithms currently used in publicly available software tools. It looks at statistics, algorithms, automated data retrieval, and experimental consideration in the various omics areas.

This volume discusses the latest developments in clinical proteomics and describes in detail the algorithms used in publicly available software tools. It should be considered as a proteomics-bioinformatics resource and offers the opportunity to understand the details of the various publicly available algorithms. The book should not only be considered as a pure bioinformatics resource filled with complex equations but it aims to describe the background of the biology and experimental methods. However, detailed experimental protocols will only be referenced. The pro and cons of the various experimental methods in relation to data analysis will be reviewed as well. In other words the intention is to make a connection between theory and practice. Practical examples showing results from the software tools will in some cases be given. The book is divided into five sections: Section I: A detailed discussion on algorithms implemented in software tools for assignment of MS and MS/MS spectra to peptides and proteins. The most popular tools for searching mass spectrometry data is currently commercial tools like Mascot and Seaquest. These tools provide the most basic analysis of mass spectrometry data however; by using the publicly available tools one can often move further with the data analysis and mass spectrometry data has more flavors than for example micro array data where the gene IDs is given directly by the array software. The problem is that mass spectrometry data acquisition methods are different depending on the specific task and produce slightly different data types. For example, the samples can be enriched for specific modifications and mass spectrometry settings can be optimized for the specific modification. Making specific algorithms for each type of spectra data will be a major task which deserves more attention. The different publicly available software tools have already some specialization for specific tasks and the different tools have both pro and cons in specific cases. Understanding the details and the philosophy behind the algorithms helps in deciding which tool is best for a specific search task. Section II: Starts with an overview of the different quantitative proteomics strategies which discusses the pro and cons of the different labeling strategies, labeling versus non-labeling, model system (cell or tissue types), and mass spectrometers in relation to quantitative proteomics. The consecutive contribution describes quantitative algorithms used in publicly available tools. Algorithms for label free quantitation by LC-MS intensity profiling, stable isotope labeling and MS, and quantitation from 2D-gels will be covered. Section III: Is titled "Finding biomarkers in MS data". The word biomarker has different meaning depending on the context in which it is used. It is here used in a clinical context and should be interpreted as: "a substance whose specific detection level indicates a particular cellular or clinical state". In theory one could easily imagine cases where one biomarker has several detection levels intervals that indicate various sates. A even more complex example would be a set of biomarkers and their corresponding set of dectection levels intervals could be used for classifying a specific cellular or clinical state. In complex cases more elaborat models based on machine learning is essential. This section therefor starts with a gentle introduction to machine learning followed by examples of useful algorithms and mehtods for classification based on mass spectrometry spectra. Section IV: The proteomics mass spectrometry data storage problem is still not solved to a satisfactory degree. Parsing the data into the databases is in some cases problematic. Other problems such database schemas that do not fully encapsulate the result from mass spectrometry data are also evident. However, the most common type of data and results can be stored in current proteomics databases. < Section V: System biology is the study of the interactions between the components of a biological system. System biology is a broad field involving data storage, controlled vocabulary, data mining, interaction studies, data correlation, and modeling of biochemical pathways. The data input comes from various omics fields such as genomics, transcriptomics, proteomics, interactomics and metabolmics. Notice that metabolomics can be further divided in to subcategories such as peptidomics, glycomics, and lipidomics. The discussion in this section will be restricted to system biology in relation to proteomics. It will describe how to relate the proteomics result to result obtained in other omics fields and how one can automatically obtain functional annotation of the identified proteins.

Fully updated overview on machine learning techniques applied to biological problems

Includes detailed information about current standards for a number of clinical diseases

Presents details on data analysis strategies in genomics, transcriptomics, proteomics and metabolomics

Summarizes statistical methods and tools for enrichment/depletion analysis

Provides a comprehensive coverage of biomedical text mining

Includes supplementary material: sn.pub/extras



Inhalt
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 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

Produktinformationen

Titel: Bioinformatics Methods in Clinical Research
Untertitel: Methods, Applications, and Tools
Editor:
EAN: 9781603271936
ISBN: 1603271937
Format: Fester Einband
Herausgeber: Springer-Verlag GmbH
Anzahl Seiten: 390
Gewicht: 917g
Größe: H265mm x B189mm x T33mm
Jahr: 2009
Untertitel: Englisch
Auflage: 2010

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