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  • 1
    Book
    Book
    Analyzing microarray gene expression data / (2004)
    Hoboken, NJ :Wiley-Interscience,
    Details
    UID:
    almahu_BV017522815
    Format: XX, 320 S. : , Ill., graph. Darst.
    ISBN: 0-471-22616-5
    Series Statement: Wiley series in probability and statistics
    Language: English
    Subjects: Biology
    RVK:
    WC 4460
    RVK:
    WC 7000
    Keywords: Genexpression ; Microarray
    URL: Inhaltsverzeichnis
    URL: Inhaltsverzeichnis
    URL: Inhaltsverzeichnis
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  • 2
    Online Resource
    Online Resource
    Advances in statistical bioinformatics : models and integrative inference for high-throughput data (2013)
    Cambridge :Cambridge University Press,
    Details
    UID:
    almafu_9959234314402883
    Format: 1 online resource (xv, 481 pages) : , digital, PDF file(s).
    Edition: 1st ed.
    ISBN: 1-139-89118-9 , 1-107-24941-4 , 1-107-24858-2 , 1-299-70751-3 , 1-107-25107-9 , 1-107-25024-2 , 1-139-22644-4 , 1-107-24775-6
    Content: Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation.
    Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , ""Contents""; ""List of Contributors""; ""Preface""; ""1 An Introduction to Next-Generation Biological Platforms""; ""Virginia Mohlere, Wenting Wang, and Ganiraju Manyam""; ""1.1 Introduction""; ""1.2 The Biology of Gene Silencing""; ""1.2.1 DNA Methylation""; ""1.2.2 RNA Interference""; ""1.3 High-Throughput Profiling""; ""1.3.1 Molecular Inversion Probe Arrays""; ""1.3.2 Array Comparative Genomic Hybridization (aCGH)""; ""1.3.3 Genome-Wide Association Studies""; ""1.3.4 Reverse-Phase Protein Array""; ""1.4 Next-Generation Sequencing""; ""1.4.1 Whole-Genome and Whole-Exome Sequencing"" , ""1.4.2 ChIP-Seq""""1.4.3 RNA-Seq""; ""1.4.4 BS-seq""; ""1.5 NGS Data Management and Analysis""; ""1.6 Platform Integration""; ""Acknowledgments""; ""References""; ""References""; ""2 An Introduction to The Cancer Genome Atlas""; ""Bradley M. Broom and Rehan Akbani""; ""2.1 Introduction""; ""2.2 History and Goals of the TCGA Project""; ""2.3 Sample Collection and Processing""; ""2.3.1 Step 1: Tissue Collection""; ""2.3.2 Step 2: Quality Control and DNA/RNA Extraction""; ""2.3.3 Step 3: Molecular Profiling and Sequencing""; ""2.3.4 Step 4: Data Collection and Public Distribution"" , ""2.3.5 Step 5: Data Analysis""""2.4 Data Processing, Storage, and Access""; ""2.4.1 TCGA Barcodes and UUIDs""; ""2.4.2 The Data Coordinating Center""; ""2.4.3 Data Access Matrix""; ""2.4.4 Bulk Download""; ""2.4.5 HTTP""; ""2.4.6 CGHub""; ""2.4.7 Sample and Data Relationship Format (SDRF) and Investigation Description Format (IDF) Files""; ""2.4.8 File Format""; ""2.4.9 Version""; ""2.5 Tools for Visualizing and Analyzing TCGA Data""; ""2.5.1 cBio Cancer Genomics Portal""; ""2.5.2 MBatch Portal""; ""2.5.3 Next-Generation Clustered Heat Maps""; ""2.5.4 Regulome Explorer"" , ""2.5.5 Integrative Genome Viewer""""2.5.6 Cancer Genomics Browser""; ""2.6 Summary""; ""Acknowledgments""; ""References""; ""References""; ""3 DNA Variant Calling in Targeted Sequencing Data""; ""Wenyi Wang, Yu Fan, and Terence P. Speed""; ""3.1 Introduction""; ""3.2 Background""; ""3.2.1 Single-Nucleotide Variation""; ""3.2.2 Long Padlock Probes""; ""3.2.3 Array-Based Resequencing""; ""3.3 Sequence Robust Multiarray Analysis""; ""3.3.1 Quality Control""; ""3.3.2 Variant Calling""; ""3.4 Application of SRMA""; ""3.4.1 Candidate Gene Study for Mitochondrial Diseases"" , ""3.4.2 Validation Results""""3.4.3 Biological Findings""; ""3.5 Conclusion""; ""Appendix""; ""References""; ""References""; ""4 Statistical Analysis of Mapped Reads from mRNA-Seq Data""; ""Ernest Turro and Alex Lewin""; ""4.1 Background""; ""4.1.1 RNA Biology""; ""4.1.2 RNA Technology""; ""4.2 Mapping and Assembly Strategies""; ""4.2.1 De Novo Assembly of the Transcriptome""; ""4.2.2 Genome-Guided Assembly of the Transcriptome""; ""4.2.3 Alignment to a Reference Transcriptome""; ""4.3 Modeling Expression Levels""; ""4.3.1 Poisson Model for Expression Quantification""; ""4.4 Normalization"" , ""4.4.1 RPKM Normalization"" , English
    Additional Edition: ISBN 1-107-02752-7
    Additional Edition: ISBN 1-107-24154-5
    Language: English
    URL: Volltext
    URL: Inhaltstext
    URL: https://doi.org/10.1017/CBO9781139226448
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    Online Resource
    Online Access
    HU Berlin
    FU Berlin
    UB Potsdam
  • 3
    Online Resource
    Online Resource
    Bayesian inference for gene expression and proteomics / (2006)
    Cambridge :Cambridge University Press,
    Details
    UID:
    almafu_9960119705502883
    Format: 1 online resource (xviii, 437 pages) : , digital, PDF file(s).
    ISBN: 0-511-58458-X
    Content: The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.
    Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , English
    Additional Edition: ISBN 1-107-63698-1
    Additional Edition: ISBN 0-521-86092-X
    Language: English
    URL: https://doi.org/10.1017/CBO9780511584589
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    HU Berlin
    FU Berlin
  • 4
    Online Resource
    Online Resource
    Analyzing microarray gene expression data / (2005)
    Hoboken, N.J. :Wiley-Interscience,
    Details
    UID:
    almafu_9959328108602883
    Format: 1 online resource (xx, 320 pages) : , illustrations (some color)
    ISBN: 9780471728429 , 047172842X , 9780471726128 , 0471726125
    Note: "Published Online: 28 JAN 2005"--Details page. , Microarrays in Gene Expression Studies -- Cleaning and Normalization -- Some Cluster Analysis Methods -- Clustering of Tissue Samples -- Screening and Clustering of Genes -- Discriminant Analysis -- Supervised Classification of Tissue Samples -- Linking Microarray Data with Survival Analysis.
    Additional Edition: Print version: McLachlan, Geoffrey J., 1946- Analyzing microarray gene expression data. Hoboken, N.J. : Wiley-Interscience, ©2004 ISBN 9780471226161
    Language: English
    Keywords: Electronic books. ; Electronic books. ; Electronic books.
    DOI: 10.1002/047172842X
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/047172842X
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/047172842X
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/047172842X
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    Online Resource
    Open Access Request
    HU Berlin
    FU Berlin
  • 5
    Online Resource
    Online Resource
    Advances in statistical bioinformatics : models and integrative inference for high-throughput data (2013)
    Cambridge ; : Cambridge University Press,
    Details
    UID:
    almahu_9948318392602882
    Format: xv, 481 p. : , ill.
    Edition: Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.
    Content: "Chapter 1 An introduction to next-generation biological platforms Virginia Mohlere, Wenting Wang, and Ganiraju Manyam The University of Texas. MD Anderson Cancer Center 1.1 Introduction When Sanger and Coulson first described a reliable, efficient method for DNA sequencing in 1975 (Sanger and Coulson, 1975), they made possible the full sequencing of both genes and entire genomes. Although the method was resource-intensive, many institutions invested in the necessary equipment, and Sanger sequencing remained the standard for the next 30 years. Refinement of the process increased read lengths from around 25 to 2 Mohlere, Wang, and Manyam almost 750 base pairs (Schadt et al., 2010, fig. 1). While this greatly increased efficiency and reliability, the Sanger method still required not only large equipment but significant human investment, as the process requires the work of several people. This prompted researchers and companies such as Applied Biosystems to seek improved sequencing techniques and instruments. Starting in the late 2000s, new instruments came on the market that, although they actually decreased read length, lessened run time and could be operated more easily with fewer human resources (Schadt et al., 2010). Despite discoveries that have illuminated new therapeutic targets, clarified the role of specific mutations in clinical response, and yielded new methods for diagnosis and predicting prognosis (Chin et al., 2011), the initial promise of genomic data has largely remained so far unfulfilled. The difficulties are numerous"--
    Language: English
    Keywords: Electronic books.
    URL: Click to View
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    Online Resource
    HU Berlin
  • 6
    Online Resource
    Online Resource
    Bayesian inference for gene expression and proteomics (2006)
    Cambridge : Cambridge University Press
    Details
    UID:
    gbv_883390906
    Format: 1 Online-Ressource (xviii, 437 pages) , digital, PDF file(s)
    ISBN: 9780511584589
    Content: The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions
    Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015)
    Additional Edition: ISBN 9780521860925
    Additional Edition: ISBN 9781107636989
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9780521860925
    Language: English
    DOI: 10.1017/CBO9780511584589
    URL: Volltext
    URL: Inhaltstext
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    Online Resource
    Online Access
    Open Access Request
    UB Potsdam
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