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.

• 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.

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.

• 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.

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.

• 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.