Ecological Genomics

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Cover; Half Title Page; Title Page; Copyright Page; Dedication Page; About the Author; Table of Contents; Preface; Chapter 1 Ecological Genomics: An Introduction; 1.1. Introduction; 1.2. Why Ecological Genomics?; 1.3. Genomics Revolution; 1.4. Conclusions; Chapter 2 Biopython: Bioinformatics Analysis; 2.1. An Introduction; 2.2. Quick Start; 2.3. Sequence Objects; 2.4. Sequences and Alphabets; 2.5. Sequences Act Like Strings; 2.6. Slicing a Sequence; 2.7. Turning Seq Objects Into Strings; 2.8. Concatenating or Adding Sequences; 2.9. Changing Case

2.10. Nucleotide Sequences and (Reverse) Complements2.11. Transcription; 2.12. Translation; 2.13. Translation Tables; 2.14. Comparing Seq Objects; 2.15. MutableSeq Objects; 2.16. UnknownSeq Objects; 2.17. Working With Strings Directly; 2.18. Conclusions; Chapter 3 Python For Processing Ecological Data; 3.1. DataFrames In Pandas; 3.2. Reading CSV Data Using Pandas; 3.3. So What's a DataFrame?; 3.4. Exploring Our Species Survey Data; 3.5. Calculating Statistics From Data in a Pandas DataFrame; 3.6. Groups in Pandas; 3.7. Quickly Creating Summary Counts in Pandas; 3.8. Basic Math Functions

3.9. Quick & Easy Plotting Data Using Pandas3.10. Indexing and Slicing in Python; 3.11. Extracting Range Based Subsets: Slicing; 3.12. Slicing Subsets of Rows in Python; 3.13. Copying Objects vs Referencing Objects in Python; 3.14. Slicing Subsets of Rows and Columns in Python; 3.15. Python Syntax -- Summary; 3.16. Concatenating DataFrames; 3.17. Writing Out Data to CSV; 3.18. Joining DataFrames; 3.19. Identifying Join Keys; 3.20. Inner Joins; 3.21. Left Joins; 3.22. Other Join Types; 3.23. Conclusions; Chapter 4 Genomics; 4.1. Genes and Genomes; 4.2. DNA and Gene Transcription

4.3. Gene Translation and the Genetic Code4.4. NGS Analysis of Genomes; 4.5. Sequence Analysis in R and Bioconductor; 4.6. String in R Base; Chapter 5 NGS Sequence Analysis; 5.1. Phred Scores; 5.2. Sequence and Quality Data: Quality Scale X String Set; 5.3. Processing FASTQ Files With ShortRead; 5.4. Conclusions; Chapter 6 Population Genetics -- A Computational Approach; 6.1. Genetic Diversity In A Population -- Hardy-Weinberg Principle; 6.2. Genetic Differentiation From SSR Data; 6.3. Estimation; 6.4. Confidence Intervals; 6.5. Analysis of Molecular Variance (AMOVA); 6.6. Other Implementations

6.7. Discriminant Analyzes of Principal Components (DAPC)6.8. Genetic Distances From SNP Data; 6.9. Conclusions; Chapter 7 Population Genomics; 7.1. Opening and Examining the Dataset; 7.2. VCF File Structure; 7.3. The Meta Region; 7.4. The Gt Region; 7.5. vcfR Package; 7.6. Converting VCF Data to a Genlight Object; 7.7. Using ChromR to Locate Unusual Features in a Genome; 7.8. Genetic Differentiation; 7.9. GBS Analysis; 7.10. Population Genetic Analyzes for GBS Data; 7.11. DAPC; 7.12. DAPC Analysis of Phytophthora Ramorum From Forests and Nurseries; 7.13. Conclusions; References

Chapter 8 Populations Strata: Subsetting, Clone Correction and Structure

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