Statistical Genomics:Methods and Protocols

图书简介

This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data.

Overview of Sequence Data Formats.- Integrative Exploratory Analysis of Two or More Genomic Datasets.- Study Design for Sequencing Studies.- Genomic Annotation Resources in R/Bioconductor.- The Gene Expression Omnibus Database.- A Practical Guide to the Cancer Genome Atlas (TCGA).- Working with Oligonucleotide Arrays.- Meta-Analysis in Gene Expression Studies.- Practical Analysis of Genome Contact Interaction Experiments.- Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data.- Variant Calling From Next Generation Sequence Data.- Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes.- NGS-QC Generator: A Quality Control System for ChIP-seq and Related Deep Sequencing-Generated Datasets.- Operating on Genomic Ranges Using BEDOPS.- GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality.- Visualizing Genomic Data using Gviz and Bioconductor.- Introducing Machine Learning Concepts with WEKA.- Experimental Design and Power Calculation for RNA-Seq Experiments.- It?s DE-licious: A Recipe for Differential Expression Analyses of RNA-Seq Experiments Using Quasi-Likelihood Methods in EdgeR.

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Statistical Genomics:Methods and Protocols（Methods in Molecular Biology） 医学遗传学

Statistical Genomics:Methods and Protocols（Methods in Molecular Biology）
医学遗传学