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ویرایش:
نویسندگان: Paradis. Emmanuel,
سری:
ISBN (شابک) : 9781138608184
ناشر: CRC Press LLC
سال نشر: 2020
تعداد صفحات: 0
زبان: English
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 10 مگابایت
در صورت تبدیل فایل کتاب Population Genomics with R به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب ژنومیک جمعیت با R نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Cover Half Title Title Page Copyright Page Dedication Table of Contents Preface Symbol Description 1: Introduction 1.1 Heredity, Genetics, and Genomics 1.2 Principles of Population Genomics 1.2.1 Units 1.2.2 Genome Structures 1.2.3 Mutations 1.2.4 Drift and Selection 1.3 R Packages and Conventions 1.4 Required Knowledge and Other Readings 2: Data Acquisition 2.1 Samples and Sampling Designs 2.1.1 How Much DNA in a Sample? 2.1.2 Degraded Samples 2.1.3 Sampling Designs 2.2 Low-Throughput Technologies 2.2.1 Genotypes From Phenotypes 2.2.2 DNA Cleavage Methods 2.2.3 Repeat Length Polymorphism 2.2.4 Sanger and Shotgun Sequencing 2.2.5 DNA Methylation and Bisulfite Sequencing 2.3 High-Throughput Technologies 2.3.1 DNA Microarrays 2.3.2 High-Throughput Sequencing 2.3.3 Restriction Site Associated DNA 2.3.4 RNA Sequencing 2.3.5 Exome Sequencing 2.3.6 Sequencing of Pooled Individuals 2.3.7 Designing a Study With HTS 2.3.8 The Future of DNA Sequencing 2.4 File Formats 2.4.1 Data Files 2.4.2 Archiving and Compression 2.5 Bioinformatics and Genomics 2.5.1 Processing Sanger Sequencing Data With sangerseqR 2.5.2 Read Mapping With Rsubread 2.5.3 Managing Read Alignments With Rsamtools 2.6 Simulation of High-Throughput Sequencing Data 2.7 Exercises 3: Genomic Data in R 3.1 What is an R Data Object? 3.2 Data Classes for Genomic Data 3.2.1 The Class \"loci\" (pegas) 3.2.2 The Class \"genind\" (adegenet) 3.2.3 The Classes \"SNPbin\" and \"genlight\" (adegenet) 3.2.4 The Class \"SnpMatrix\" (snpStats) 3.2.5 The Class \"DNAbin\" (ape) 3.2.6 The Classes \"XString\" and \"XStringSet\" (Biostrings) 3.2.7 The Package SNPRelate 3.3 Data Input and Output 3.3.1 Reading Text Files 3.3.2 Reading Spreadsheet Files 3.3.3 Reading VCF Files 3.3.4 Reading PED and BED Files 3.3.5 Reading Sequence Files 3.3.6 Reading Annotation Files 3.3.7 Writing Files 3.4 Internet Databases 3.5 Managing Files and Projects 3.6 Exercises 4: Data Manipulation 4.1 Basic Data Manipulation in R 4.1.1 Subsetting, Replacement, and Deletion 4.1.2 Commonly Used Functions 4.1.3 Recycling and Coercion 4.1.4 Logical Vectors 4.2 Memory Management 4.3 Conversions 4.4 Case Studies 4.4.1 Mitochondrial Genomes of the Asiatic Golden Cat 4.4.2 Complete Genomes of the Fruit Fly 4.4.3 Human Genomes 4.4.4 Influenza H1N1 Virus Sequences 4.4.5 Jaguar Microsatellites 4.4.6 Bacterial Whole Genome Sequences 4.4.7 Metabarcoding of Fish Communities 4.5 Exercises 5: Data Exploration and Summaries 5.1 Genotype and Allele Frequencies 5.1.1 Allelic Richness 5.1.2 Missing Data 5.2 Haplotype and Nucleotide Diversity 5.2.1 The Class \"Haplotype\" 5.2.2 Haplotype and Nucleotide Diversity From DNA Se-quences 5.3 Genetic and Genomic Distances 5.3.1 Theoretical Background 5.3.2 Hamming Distance 5.3.3 Distances From DNA Sequences 5.3.4 Distances From Allele Sharing 5.3.5 Distances From Microsatellites 5.4 Summary by Groups 5.5 Sliding Windows 5.5.1 DNA Sequences 5.5.2 Summaries With Genomic Positions 5.5.3 Package SNPRelate 5.6 Multivariate Methods 5.6.1 Matrix Decomposition 5.6.1.1 Eigendecomposition 5.6.1.2 Singular Value Decomposition 5.6.1.3 Power Method and Random Matrices 5.6.2 Principal Component Analysis 5.6.2.1 Adegenet 5.6.2.2 SNPRelate 5.6.2.3 FlashpcaR 5.6.3 Multidimensional Scaling 5.7 Case Studies 5.7.1 Mitochondrial Genomes of the Asiatic Golden Cat 5.7.2 Complete Genomes of the Fruit Fly 5.7.3 Human Genomes 5.7.4 Influenza H1N1 Virus Sequences 5.7.5 Jaguar Microsatellites 5.7.6 Bacterial Whole Genome Sequences 5.7.7 Metabarcoding of Fish Communities 5.8 Exercises 6: Linkage Disequilibrium and Haplotype Structure 6.1 Why Linkage Disequilibrium is Important? 6.2 Linkage Disequilibrium: Two Loci 6.2.1 Phased Genotypes 6.2.1.1 Theoretical Background 6.2.1.2 Implementation in Pegas 6.2.2 Unphased Genotypes 6.3 More Than Two Loci 6.3.1 Haplotypes From Unphased Genotypes 6.3.1.1 The Expectation–Maximization Algorithm 6.3.1.2 Implementation in haplo.stats 6.3.2 Locus-Specific Imputation 6.3.3 Maps of Linkage Disequilibrium 6.3.3.1 Phased Genotypes With pegas 6.3.3.2 SNPRelate 6.3.3.3 snpStats 6.4 Case Studies 6.4.1 Complete Genomes of the Fruit Fly 6.4.2 Human Genomes 6.4.3 Jaguar Microsatellites 6.5 Exercises 7: Population Genetic Structure 7.1 Hardy–Weinberg Equilibrium 7.2 F-Statistics 7.2.1 Theoretical Background 7.2.2 Implementations in pegas and in mmod 7.2.3 Implementations in snpStats and in SNPRelate 7.3 Trees and Networks 7.3.1 Minimum Spanning Trees and Networks 7.3.2 Statistical Parsimony 7.3.3 Median Networks 7.3.4 Phylogenetic Trees 7.4 Multivariate Methods 7.4.1 Principles of Discriminant Analysis 7.4.2 Discriminant Analysis of Principal Components 7.4.3 Clustering 7.4.4 Maximum Likelihood Methods 7.4.5 Bayesian Clustering 7.5 Admixture 7.5.1 Likelihood Method 7.5.2 Principal Component Analysis of Coancestry 7.5.3 A Second Look at F-Statistics 7.6 Case Studies 7.6.1 Mitochondrial Genomes of the Asiatic Golden Cat 7.6.2 Complete Genomes of the Fruit Fly 7.6.3 Influenza H1N1 Virus Sequences 7.6.4 Jaguar Microsatellites 7.7 Exercises 8: Geographical Structure 8.1 Geographical Data in R 8.1.1 Packages and Classes 8.1.2 Calculating Geographical Distances 8.2 A Third Look at F-Statistics 8.2.1 Hierarchical Components of Genetic Diversity 8.2.2 Analysis of Molecular Variance 8.3 Moran I and Spatial Autocorrelation 8.4 Spatial Principal Component Analysis 8.5 Finding Boundaries Between Populations 8.5.1 Spatial Ancestry (tess3r) 8.5.2 Bayesian Methods (Geneland) 8.6 Case Studies 8.6.1 Complete Genomes of the Fruit Fly 8.6.2 Human Genomes 8.7 Exercises 9: Past Demographic Events 9.1 The Coalescent 9.1.1 The Standard Coalescent 9.1.2 The Sequential Markovian Coalescent 9.1.3 Simulation of Coalescent Data 9.2 Estimation of Θ 9.2.1 Heterozygosity 9.2.2 Number of Alleles 9.2.3 Segregating Sites 9.2.4 Microsatellites 9.2.5 Trees 9.3 Coalescent-Based Inference 9.3.1 Maximum Likelihood Methods 9.3.2 Analysis of Markov Chain Monte Carlo Outputs 9.3.3 Skyline Plots 9.3.4 Bayesian Methods 9.4 Heterochronous Samples 9.5 Site Frequency Spectrum Methods 9.5.1 The Stairway Method 9.5.2 CubSFS 9.5.3 Popsicle 9.6 Whole-Genome Methods (psmcr) 9.7 Case Studies 9.7.1 Mitochondrial Genomes of the Asiatic Golden Cat 9.7.2 Complete Genomes of the Fruit Fly 9.7.3 Influenza H1N1 Virus Sequences 9.7.4 Bacterial Whole Genome Sequences 9.8 Exercises 10: Natural Selection 10.1 Testing Neutrality 10.1.1 Simple Tests 10.1.2 Selection in Protein-Coding Sequences 10.2 Selection Scans 10.2.1 A Fourth Look at F-Statistics 10.2.2 Association Studies (LEA) 10.2.3 Principal Component Analysis (pcadapt) 10.2.4 Scans for Selection With Extended Haplotypes 10.2.5 FST Outliers 10.3 Time-Series of Allele Frequencies 10.4 Case Studies 10.4.1 Mitochondrial Genomes of the Asiatic Golden Cat 10.4.3 Influenza H1N1 Virus Sequences 10.4.2 Complete Genomes of the Fruit Fly 10.5 Exercises A: Installing R Packages B: Compressing Large Sequence Files C: Sampling of Alleles in a Population D: Glossary Bibliography Index