This title includes a number of Open Access chapters. This book provides a valuable and informative overview of the field of bioinformatics and statistical methodology. The chapters show various approaches to bias correction and error estimation, as well as quantitative methods for genome and proteome analysis. The text covers RN
This title includes a number of Open Access chapters. This book provides a valuable and informative overview of the field of bioinformatics and statistical methodology. The chapters show various approaches to bias correction and error estimation, as well as quantitative methods for genome and proteome analysis. The text covers RN
Dr. Yu Liu is a bioinformatician with special interest in next-gen sequencing and its applications. His specialties are molecular biology, DNA sequence analysis, next-gen sequencing application on gene expression analysis and comparative genomics, and microarray gene expression analysis. He is the director of the Bioinformatics Resource Center at the University of Wisconsin-Madison. He has a master's degree in computer science from the University of Wisconsin-Madison, a master's degree in developmental biology from the Chinese Academy of Science, and PhD in molecular biology from The Ohio State University.
Inhaltsangabe
Introduction. Part I: RNA-Seq. The Bench Scientist's Guide to Statistical Analysis of RNA-Seq Data. Assembly of Non-Unique Insertion Content Using Next-Generation Sequencing. RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome. Part II: Microarray. A Regression System for Estimation of Errors Introduced by Confocal Imaging into Gene Expression Data In Situ. SPACE: An Algorithm to Predict and Quantify Alternatively Spliced Isoforms Using Microarrays. Link-Based Quantitative Methods to Identify Differentially Coexpressed Genes and Gene Pairs. Dimension Reduction with Gene Expression Data Using Targeted Variable Importance Measurement. Part III: GWAS. Genome-Wide Association Study of Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis in Europe. Genotyping Common and Rare Variation Using Overlapping Pool Sequencing. Learning Genetic Epistasis Using Bayesian Network Scoring Criteria. Combined Analysis of Three Genome-Wide Association Studies on vWF and FVIII Plasma Levels. Part IV: Proteomics. Statistical Methods for Quantitative Mass Spectrometry Proteomic Experiments with Labeling. MRCQuant: An Accurate LC-MS Relative Isotopic Quantification Algorithm on TOF Instruments. Index.
Introduction. Part I: RNA-Seq. The Bench Scientist's Guide to Statistical Analysis of RNA-Seq Data. Assembly of Non-Unique Insertion Content Using Next-Generation Sequencing. RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome. Part II: Microarray. A Regression System for Estimation of Errors Introduced by Confocal Imaging into Gene Expression Data In Situ. SPACE: An Algorithm to Predict and Quantify Alternatively Spliced Isoforms Using Microarrays. Link-Based Quantitative Methods to Identify Differentially Coexpressed Genes and Gene Pairs. Dimension Reduction with Gene Expression Data Using Targeted Variable Importance Measurement. Part III: GWAS. Genome-Wide Association Study of Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis in Europe. Genotyping Common and Rare Variation Using Overlapping Pool Sequencing. Learning Genetic Epistasis Using Bayesian Network Scoring Criteria. Combined Analysis of Three Genome-Wide Association Studies on vWF and FVIII Plasma Levels. Part IV: Proteomics. Statistical Methods for Quantitative Mass Spectrometry Proteomic Experiments with Labeling. MRCQuant: An Accurate LC-MS Relative Isotopic Quantification Algorithm on TOF Instruments. Index.
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