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Section I Foundations of Phylogenomics |
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Chapter 1 What is Phylogenomics? |
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3 | (12) |
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Phylogenomics and Bioinformatics |
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3 | (2) |
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Bioinformatics Tools for Finding Patterns in Biological Experiments |
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5 | (2) |
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The rise of phylogenomics |
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5 | (2) |
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Sub-Branches of Phylogenomics |
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7 | (1) |
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8 | (1) |
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Basic Computational Tools in Phylogenomics |
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8 | (1) |
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Statistics Help Compare Genetic Sequences and Generate Phylogenetic Trees |
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8 | (2) |
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Parametric Statistics Are Derived from Distributions |
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10 | (1) |
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Nonparametric Statistical Analyses Are useful in Many Situations |
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10 | (1) |
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Maximum Likelihood and Bayesian Analysis Are Standard Statistical Methods used in Phylogenomics |
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11 | (1) |
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Key Attributes of Phylogenomicists |
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11 | (2) |
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13 | (1) |
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Recom mendations for students |
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13 | (1) |
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13 | (1) |
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13 | (2) |
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Chapter 2 The Biology and Sequencing of Genetic Information: dna, rna, and Proteins |
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15 | (18) |
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15 | (2) |
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Dna molecules efficiently transmit information |
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15 | (1) |
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Dna is synthesized by specific pairing |
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15 | (2) |
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DNA can mutate and lead to descent with modification |
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17 | (1) |
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Polymerase chain reaction (PCR) is a milestone development |
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17 | (1) |
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17 | (6) |
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Proteins are linear polymers of amino acids |
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17 | (1) |
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Proteins have multiple levels of structure |
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18 | (3) |
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Translation of DNA to amino acids is accomplished by the genetic code |
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21 | (2) |
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Reading frame in nucleic acid sequences |
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23 | (1) |
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23 | (4) |
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Nucleic acid sequencing methods are increasingly powerful |
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23 | (1) |
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Next-generation sequencing allows for rapid analysis of genomes |
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24 | (2) |
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Other applications of next-generation sequencing |
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26 | (1) |
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Alternatives to Whole Genome Sequencing |
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27 | (1) |
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Single-nucleotide polymorphisms (SNPs) differ at one position in a designated DNA sequence |
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27 | (1) |
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27 | (1) |
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27 | (1) |
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Analyzing Gene Expression |
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28 | (2) |
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RNA-Seq is a method for obtaining transcriptomic data |
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29 | (1) |
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30 | (1) |
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Recommendations for Students |
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30 | (1) |
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30 | (1) |
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31 | (2) |
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Chapter 3 Evolutionary Principles: Populations and Trees |
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33 | (16) |
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Darwin, Wallace, and Evolutionary Theory |
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33 | (2) |
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33 | (1) |
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Darwin's ideas lacked a valid genetic mechanism |
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34 | (1) |
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The study of evolution can be divided into microevolution and macroevolution |
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34 | (1) |
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35 | (1) |
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Population genetics focuses on microevolution |
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35 | (1) |
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Advances in molecular techniques led to new thinking in evolutionary biology |
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35 | (1) |
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Codon changes and usage can provide insights into natural selection |
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36 | (1) |
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Microevolutionary studies often rely on computational modeling |
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36 | (5) |
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38 | (1) |
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Macroevolution studies rely heavily on systematics and phylogenetics |
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38 | (1) |
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Relationships and systematics |
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38 | (1) |
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There are several approaches to tree building |
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38 | (1) |
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38 | (2) |
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Phylogenetics can help establish homology |
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40 | (1) |
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41 | (2) |
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The definition of species is heavily debated |
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41 | (1) |
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Defining species phylogenetically |
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42 | (1) |
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Updates on Darwinian Evolution |
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43 | (2) |
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Punctuated equilibrium suggests that not all evolution is gradual |
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43 | (1) |
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Epigenetic changes are caused by influences outside of the genetic system |
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44 | (1) |
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45 | (1) |
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45 | (1) |
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45 | (1) |
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45 | (1) |
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46 | (3) |
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Chapter 4 Data Storage--The Basics |
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49 | (14) |
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Databases and Phylogenomics |
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49 | (2) |
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DNA sequences are stored in large international databases |
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50 | (1) |
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Specific data sets may be held in special repositories |
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50 | (1) |
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These databases offer free access and availability for scientific inquiry |
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51 | (1) |
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Information Retrieval from the NCBI Database |
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51 | (10) |
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Publications are archived in the PubMed database |
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52 | (1) |
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Working with molecular sequences stored in GenBank |
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52 | (6) |
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Whole genomes are accessible on the Genome Page |
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58 | (3) |
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61 | (1) |
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Recommendations for students |
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61 | (1) |
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61 | (1) |
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61 | (2) |
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Chapter 5 Sequence Alignment and Searching Sequence Databases |
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63 | (18) |
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Homology of Genes, Genomic Regions, and Proteins |
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63 | (5) |
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Genomes can diverge by speciation and by duplication |
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63 | (1) |
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Sequence alignment is an important procedure in phylogenomics |
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64 | (1) |
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Basic, paired nucleic acid sequence alignment |
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65 | (1) |
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Basic, paired protein sequence alignment |
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66 | (2) |
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Dynamic programming and sequence alignment |
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68 | (9) |
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Database Searching via Pairwise Alignments: The Basic Local Alignment Search Tool |
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68 | (1) |
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BLAST identifies highly similar sequences |
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69 | (1) |
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BLAST is optimized for searching large databases |
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69 | (2) |
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There are multiple types of BLAST for nucleotide and amino acid sequences |
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71 | (1) |
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BLAST searches are easy to do |
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72 | (3) |
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Whole genome alignments can also be performed |
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75 | (2) |
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77 | (1) |
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Recommendations for Students |
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78 | (1) |
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78 | (1) |
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79 | (2) |
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Chapter 6 Multiple Alignments |
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81 | (10) |
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Multiple Sequence Alignment |
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81 | (4) |
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Changing Alignment Parameters |
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85 | (1) |
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Multiple optimal alignments may exist |
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85 | (1) |
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Specialized Alignment Programs |
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86 | (1) |
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Choosing an Alignment Program |
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86 | (2) |
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Automated alignment results are frequently adjusted "by eye" |
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87 | (1) |
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Alignment programs can be compared by use of benchmark data sets |
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88 | (1) |
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Dynamic versus Static Alignment |
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88 | (1) |
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89 | (1) |
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Recommendations for Students |
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89 | (1) |
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89 | (1) |
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90 | (1) |
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Chapter 7 Genome Sequencing and Annotation |
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91 | (10) |
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Whole Genome Sequencing (WGS) |
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91 | (4) |
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Next-generation sequencing |
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91 | (1) |
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The nuts and bolts of assembly |
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92 | (3) |
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Gene Finding and Annotation |
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95 | (3) |
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Gene finding can be accomplished via extrinsic, ab initio, and comparative approaches |
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96 | (1) |
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Gene functional annotation |
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97 | (1) |
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97 | (1) |
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98 | (1) |
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Recommendations for Students |
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98 | (1) |
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98 | (1) |
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99 | (2) |
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Chapter 8 Genomics Databases: Genomes and Transcriptomes |
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101 | (14) |
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Genome Information Is Stored in Multiple Locations |
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101 | (7) |
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BioSample/BioProject/Short Read Archives (SRA) store archival information for projects used in broader genomics research archived in INSDC |
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101 | (7) |
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Data Archiving and Databases Outside of the INSCD System |
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108 | (6) |
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Organismal-focused genome and transcriptome databases |
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110 | (4) |
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114 | (1) |
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Recommendations for Students |
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114 | (1) |
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114 | (1) |
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114 | (1) |
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Chapter 9 Amplicon Databases: Bold and Bacterial 16S rDNA Databases |
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115 | (18) |
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Dna Barcoding and the Bold Database |
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115 | (6) |
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115 | (1) |
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Taxonomy and speciation studies involve the species delimitation |
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116 | (1) |
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DNA taxonomy and DNA barcoding |
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116 | (1) |
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Character-based or distance-based approaches to DNA barcoding result in identification of species |
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117 | (1) |
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Is there enough information in a single gene to do DNA barcoding? |
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118 | (1) |
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Potential new species are flagged by DNA barcoding |
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119 | (1) |
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120 | (1) |
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121 | (7) |
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Amplicon sequencing, microbiomes, metagenomics, and eDNA |
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121 | (3) |
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Databases are used to identify the species in a microbiome, metagenome, and eDNA sample |
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124 | (1) |
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Classifiers for identifying microbial species in eDNA, microbiome studies, and metagenomics |
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125 | (3) |
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128 | (1) |
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Recommendations for students |
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128 | (1) |
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128 | (1) |
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129 | (4) |
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Section III Phylogenetic/Phylogenomic Analysis |
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Chapter 10 Introduction to Tree Building |
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133 | (14) |
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Phylogenetic Tree Building Overview |
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133 | (4) |
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Which phylogenetic method should be used? |
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134 | (1) |
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The number of trees grows with each additional taxon |
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134 | (1) |
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Trees can be rooted by several methods |
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135 | (2) |
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137 | (3) |
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Character states in molecular data may include the presence of genes and the sequence of nucleotides or amino acids |
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137 | (1) |
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Some discrete and numerical character states are ordered |
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137 | (1) |
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Characters can be weighted relative to one another |
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137 | (1) |
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Which characters should be used? |
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138 | (1) |
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A matrix for demonstrating phylogenetic analysis |
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139 | (1) |
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Basics of Parsimony Analysis |
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140 | (4) |
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Fitch's algorithm uses set theory |
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141 | (3) |
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144 | (1) |
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144 | (1) |
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Recommendations for Students |
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145 | (1) |
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145 | (1) |
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145 | (2) |
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Chapter 11 Distance and Clustering |
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147 | (10) |
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Corrections for Multiple Hits May Be introduced |
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149 | (1) |
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Corrections using Evolutionary Models |
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149 | (5) |
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Neighbor joining is a stepwise-based approach to tree-building |
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151 | (3) |
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Minimum Evolution Uses Minimal Distance as a Criterion to Choose the Best Solution among Multiple Trees |
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154 | (1) |
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155 | (1) |
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Recommendations for students |
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156 | (1) |
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156 | (1) |
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156 | (1) |
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Chapter 12 Maximum Likelihood |
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157 | (10) |
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Transformation and Probability Matrices |
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161 | (3) |
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Character weighting schemes |
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161 | (1) |
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Likelihood analysis incorporate probability matrices |
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162 | (2) |
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164 | (1) |
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Recommendations for Students |
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164 | (1) |
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164 | (1) |
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165 | (2) |
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Chapter 13 Search Strategies and Robustness |
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167 | (10) |
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So Many Trees, So Little Time |
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167 | (4) |
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167 | (2) |
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Selection of a starting tree |
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169 | (1) |
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Peaks in tree space can be reached by branch swapping |
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169 | (1) |
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Moving from local optimality peaks to peaks with higher optimality |
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170 | (1) |
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Robustness of Phylogenetic Trees |
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171 | (1) |
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Bremer Support Estimates Robustness of a Node |
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171 | (1) |
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Resampling to Determine Node Robustness |
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172 | (2) |
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Bootstrapping assesses node robustness by resampling with replacement |
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172 | (2) |
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Jackknifing assesses node robustness by resampling without replacement |
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174 | (1) |
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Parametric bootstrapping applies a distribution model to the data |
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174 | (1) |
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Resampling Gene Partitions |
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174 | (1) |
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175 | (1) |
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Recommendations for Students |
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176 | (1) |
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176 | (1) |
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176 | (1) |
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Chapter 14 Rate Heterogeneity, Long Branch Attraction, and Likelihood Models |
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177 | (10) |
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177 | (1) |
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178 | (5) |
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Rate heterogeneity and invariant sites (I) |
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178 | (1) |
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Rate heterogeneity and the gamma distribution (rorG) |
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179 | (1) |
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Combining the invariant-sites parameter and a gamma distribution |
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180 | (1) |
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Other methods accommodating rate heterogeneity |
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180 | (3) |
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Comparing Likelihood Models |
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183 | (1) |
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Programs can compare models |
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183 | (1) |
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184 | (1) |
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Recommendations for students |
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185 | (1) |
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185 | (1) |
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185 | (2) |
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Chapter 15 Bayesian Approaches in Phylogenetics |
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187 | (12) |
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187 | (6) |
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Generating a distribution of trees is an important application of the Bayesian approach |
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189 | (2) |
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What do we need from a Bayesian phylogenetic analysis? |
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191 | (1) |
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MCMC is critical to the success of Bayesian analysis |
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192 | (1) |
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Bayesian Parameters in a Phylogenetic Context |
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193 | (4) |
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Model selection can be utilized on any biologically meaningful partition |
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194 | (1) |
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194 | (1) |
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More MCMC generations improves results at an increased computational cost |
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195 | (1) |
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Assessing the efficiency of a Bayesian phylogenetic analysis |
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196 | (1) |
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Interpreting posterior probabilities of clades |
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196 | (1) |
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197 | (1) |
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Recommendations for Students |
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197 | (1) |
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198 | (1) |
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198 | (1) |
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Chapter 16 Incongruence of Gene Trees |
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199 | (14) |
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199 | (5) |
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Taxonomic congruence via supertrees |
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200 | (1) |
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Character congruence via total evidence supermatrices |
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201 | (1) |
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Assessments of incongruence can help decide what to concatenate |
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202 | (1) |
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The incongruence length difference test |
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202 | (2) |
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Likelihood tests for incongruence |
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204 | (1) |
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Fork indices provide measures of tree similarity |
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204 | (1) |
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Robinson-Foulds Metric and Subtree Prune-and-Regraft Distance (spr distance) |
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204 | (2) |
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The Gene Tree/Species Tree Problem |
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206 | (2) |
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Examples of incomplete lineage sorting in closely related taxa |
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207 | (1) |
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Coalescence and the gene tree/species tree problem |
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208 | (1) |
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208 | (1) |
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Programs That Consider Nonvertical Evolution and Incomplete Lineage Sorting to Infer Phytogeny |
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209 | (2) |
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Coalescence programs use both gene trees and species trees as input |
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209 | (1) |
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Programs that consider horizontal gene transfer generate nets and webs |
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210 | (1) |
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211 | (1) |
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Recommendations for students |
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211 | (1) |
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211 | (1) |
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212 | (1) |
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Chapter 17 Phylogenetic Programs and Websites |
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213 | (12) |
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Website Summaries of Programs |
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213 | (1) |
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214 | (4) |
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216 | (1) |
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Bayesian phylogenetic inference programs |
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217 | (1) |
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217 | (1) |
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218 | (1) |
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218 | (1) |
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Tree Visualization Programs |
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218 | (1) |
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All Purpose Websites and Software Companies |
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218 | (3) |
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Programming Languages and Packages |
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221 | (1) |
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221 | (1) |
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Recommendations for Students |
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221 | (1) |
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221 | (1) |
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221 | (4) |
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Section IV Population Genomics |
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Chapter 18 Population Genetics and Genomes |
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225 | (20) |
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High-Throughput Methods and Population Genetics |
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225 | (1) |
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Kimura and Lewontin contributed important new ways to think about genes in nature |
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225 | (1) |
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The Hardy-Weinberg theorem has been extended in modern population genetics |
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226 | (1) |
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DNA Variation among individuals |
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226 | (5) |
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Single-nucleotide polymorphisms (SNPs) |
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227 | (1) |
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Microsatellir.es provide another analytical tool for species where SNPs are less abundant |
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228 | (2) |
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RAD markers are a source of data for modern population genomics |
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230 | (1) |
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Extending Basic Population Genetics to DNA Sequences |
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231 | (4) |
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Tajima's D distinguishes between sequences evolving neutrally and those evolving non-neutrally using allele frequencies |
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231 | (1) |
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F statistics measure the degree of isolation of entities |
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232 | (1) |
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There are two approaches to estimating population-level statistics |
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233 | (2) |
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FST and related measures have four major uses in evolutionary biology |
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235 | (1) |
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235 | (1) |
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Population-Level Techniques: Mismatch Distribution Analysis, STRUCTURE Analysis, Principle Components Analysis, and Analysis Platforms |
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236 | (7) |
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Mismatch distribution analysis compares haplotype data of populations |
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237 | (1) |
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Structure analysis reveals substructure and genetic cross talk |
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237 | (1) |
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Principle components and genomic data |
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238 | (3) |
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Population genomics analysis platforms |
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241 | (2) |
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243 | (1) |
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243 | (1) |
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244 | (1) |
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Chapter 19 Population Genomics Approaches |
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245 | (18) |
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Genome-Wide Association Studies |
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245 | (2) |
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A simple example illustrates the association technique |
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246 | (1) |
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The National Human Genomics Research institute maintains a database of genome-wide association studies |
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247 | (1) |
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Programs That Can Perform GWAS Analyses |
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247 | (1) |
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Role of the Coalescent in Population Genetics |
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247 | (4) |
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The coalescent addresses the time for an allele to coalesce and the variation in populations under drift |
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248 | (1) |
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The coalescent in practice explores a large number and a broadly representative sample of plausible genealogical scenarios |
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248 | (1) |
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High-quality DNA sequence data from a random sample constitute the best input for a coalescence analysis |
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249 | (1) |
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Importance sampling and correlated sampling are used to generate a collection of simulated genealogies |
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249 | (1) |
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Programs for coalescence analysis include BEAST and Lamarc |
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250 | (1) |
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Genetic Hitchhiking and Selective Sweeps |
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251 | (9) |
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Selective sweeps are detected in four basic ways |
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252 | (2) |
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Empirical examples of selective sweeps include boxers, flies, and humans |
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254 | (1) |
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Hard and soft sweeps produce different effects in the genome |
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255 | (1) |
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Genome-wide scans to address population genetic and evolutionary questions |
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256 | (2) |
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Phylogenetic shadowing identifies regulatory elements in DNA sequences |
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258 | (1) |
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Regions of the human genome experience accelerated evolution |
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258 | (1) |
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Regions that are both strongly conserved and rapidly deleted are of interest |
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259 | (1) |
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260 | (1) |
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260 | (1) |
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261 | (2) |
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Chapter 20 Detecting Natural Selection: The Basics |
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263 | (14) |
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Analyzing DNA Sequences for Natural Selection |
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263 | (12) |
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DNA sequences can be examined for silent and replacement changes |
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263 | (2) |
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Several variables affect the detection of natural selection at the genomic level |
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265 | (2) |
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Approximate methods of determining dN/dS |
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267 | (1) |
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Basic dN and dS calculations begin with counting the observed number of changes |
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268 | (1) |
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Scaling for redundancy and getting the number of potential substitutions is necessary for determining dN/dS |
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268 | (3) |
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Pathways of codon change are an important element in calculating dN/dS |
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271 | (2) |
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Codon change pathways can be used to account for redundancy |
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273 | (2) |
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275 | (1) |
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Recom mendations for Students |
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275 | (1) |
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275 | (1) |
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276 | (1) |
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Chapter 21 Refining the Approach to Natural Selection at the Molecular Level |
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277 | (18) |
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Accounting for Multiple Hits in DNA Sequences for dN/dS Measures |
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277 | (1) |
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The Jukes-Cantor conversion corrects for multiple hits |
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277 | (1) |
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Estimating Natural Selection Requires Adjusting the calculation of Sequence Changes |
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278 | (1) |
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Expanding the Search for Natural Selection at the Molecular Level |
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278 | (8) |
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Statistical tests of significance are required at various levels |
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278 | (1) |
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279 | (1) |
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279 | (1) |
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Natural selection is variable across protein components and across time |
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280 | (1) |
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Examples of nonuniformity are seen in Drosophila and in the BRCA1 gene |
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280 | (2) |
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Maximum likelihood approaches are implemented in selection studies at the molecular level |
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282 | (1) |
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Statistical tests using dN and dS |
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283 | (1) |
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There are caveats when detecting selection at the molecular level |
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284 | (1) |
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Transcriptomics and whole genome sequencing has opened the way for searches for natural selection at an unprecedented level |
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285 | (1) |
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286 | (4) |
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Codon selection bias can be calculated manually or by various analytical methods |
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286 | (2) |
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Codon usage bias usually occurs in cellular housekeeping genes and varies among species |
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288 | (2) |
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290 | (1) |
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Recommendations for Students |
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290 | (1) |
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290 | (1) |
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291 | (4) |
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Section V Phylogenomics in Action |
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Chapter 22 Constructing Phylogenomic Matrices |
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295 | (16) |
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Why Choose the Programs We Focus On? |
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295 | (2) |
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Formatting matrices for population genomics analysis |
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295 | (2) |
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Formatting Arlequin Files |
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297 | (1) |
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Formatting STRUCTURE Files |
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297 | (1) |
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297 | (2) |
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299 | (1) |
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300 | (1) |
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Constructing phylogenomic matrices |
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300 | (1) |
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Determining Orthology and Constructing Individual Gene Matrices |
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301 | (1) |
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Concatenating Individual Gene Alignments |
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302 | (1) |
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Partitions and Partitioning |
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302 | (1) |
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Formatting Partitions in paup* and MrBayes (NEXUS) |
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302 | (3) |
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Formatting Partitions in PHYLIP |
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305 | (1) |
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Formatting Partitions in Rax ml and iQtree |
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305 | (1) |
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Formatting Partitions in TNT |
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305 | (1) |
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306 | (1) |
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Web-Based Programs for Formatting Phylogenomic Matrices |
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306 | (1) |
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Recommendations for students |
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307 | (1) |
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307 | (1) |
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308 | (3) |
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Chapter 23 Phylogenomics and the Tree of Life |
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311 | (20) |
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Problems with Phylogenomic Studies |
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312 | (1) |
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Supertrees or Supermatrices |
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313 | (3) |
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Grafting supertree approach |
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314 | (1) |
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Matrix representation approach |
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315 | (1) |
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Divide-and-conquer approach |
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316 | (1) |
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Examples of Phylogenomic Studies |
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316 | (10) |
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Shallow targeted sequencing of over 70,000 eukaryotes recovers major eukaryotic groups |
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316 | (1) |
|
Whole genome microbial phylogenomics |
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317 | (1) |
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Specific problems in bacterial phylogenomics |
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317 | (2) |
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Does a tree of life really exist for bacteria? |
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319 | (1) |
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319 | (1) |
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The deep relationships of Metazoa |
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320 | (2) |
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322 | (4) |
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Yeast and Drosophila Represent Examples of Concatenation and Lineage Sorting Problems in Phylogenomics |
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326 | (2) |
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Coalescence Can Partially Solve the Problem of Incongruence |
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328 | (1) |
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328 | (1) |
|
Recommendations for students |
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328 | (1) |
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329 | (1) |
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329 | (2) |
|
Chapter 24 Comparative Genomics |
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331 | (16) |
|
Characterizing Genomes by Orthology |
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331 | (6) |
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Clusters of orthologous groups is a method that enables identification of orthologs of genes across multiple species |
|
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332 | (1) |
|
Single linkage clustering compares genes in a cross-species context based on sequence |
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332 | (1) |
|
A presence/absence matrix is constructed via single linkage clustering |
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333 | (4) |
|
Comparative Genomics Approaches |
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337 | (6) |
|
Venn diagrams, EDGAR, and Sungear visualize the overlap of genes from two or more genomes |
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337 | (2) |
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339 | (1) |
|
Genome content analysis was first accomplished for bacterial genomes |
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339 | (2) |
|
Caveats with genome content analysis in phylogenetic analysis |
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341 | (1) |
|
Using genome content in evolutionary studies |
|
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342 | (1) |
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343 | (1) |
|
Recommendations for Students |
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343 | (1) |
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344 | (1) |
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344 | (3) |
|
Chapter 25 Environmental DNA (eDNA) |
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347 | (12) |
|
Any Environment Can Be Examined for Its Microbial Makeup |
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347 | (1) |
|
Amplicon Sequencing, Microbiomes, Metagenomics, and eDNA |
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348 | (8) |
|
The next-generation approach |
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349 | (1) |
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350 | (1) |
|
Data management--processing |
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350 | (1) |
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350 | (2) |
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352 | (1) |
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352 | (1) |
|
Making ecological/environmental inferences |
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352 | (3) |
|
Caveats and recommendations |
|
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355 | (1) |
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356 | (1) |
|
Recommendations for students |
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356 | (1) |
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357 | (1) |
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357 | (2) |
|
Chapter 26 Phylogenomic Approaches to Understanding Gene Function and Evolution |
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359 | (14) |
|
Transcription-Based Approaches |
|
|
360 | (4) |
|
Transcriptomics is used for class comparison, prediction, and discovery |
|
|
360 | (1) |
|
Data are transformed for use in dendrograms and other clustering techniques |
|
|
361 | (1) |
|
Specific next-generation sequencing approaches applied to transcriptome analysis |
|
|
362 | (1) |
|
Transcriptomic approaches are useful in evolutionary and phylogenomic studies |
|
|
363 | (1) |
|
Protein-Protein interactions |
|
|
364 | (7) |
|
Generating data for protein-protein interaction research |
|
|
364 | (1) |
|
|
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364 | (1) |
|
|
|
365 | (1) |
|
Computational methods for examining protein-protein interactions |
|
|
365 | (1) |
|
Model organism gene and protein function can be studied by Web-based approaches like ENCODE |
|
|
365 | (1) |
|
Functional phylogenomics employs common ancestry to infer protein function |
|
|
366 | (1) |
|
Phylogenomic gene partitioning can be used to explore function |
|
|
367 | (1) |
|
A gene presence/absence matrix was employed to examine evolution in the major metazoan lineages |
|
|
367 | (1) |
|
Transcript sequences and phytogeny can be used to study plant function |
|
|
368 | (1) |
|
Gene function clustering in Caenorhabditis elegans from RNA interference phenotypes |
|
|
368 | (1) |
|
Gene ontology facilitates the comparison of genes |
|
|
369 | (2) |
|
|
|
371 | (1) |
|
Recommendations for Students |
|
|
372 | (1) |
|
|
|
372 | (1) |
|
|
|
372 | (1) |
| Index |
|
373 | |
Lisainfo e-raamatute kohta