Biol375 2019: Difference between revisions
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** R Studio: [https://www.rstudio.com/ download & install] | ** R Studio: [https://www.rstudio.com/ download & install] | ||
** APE package | ** APE package | ||
** phangorn package | |||
* [http://phylogeny.fr/ A Molecular Phylogeny Web Server] | * [http://phylogeny.fr/ A Molecular Phylogeny Web Server] | ||
* [http://www.evolgenius.info/evolview/ EvolView: an online tree viewer] | * [http://www.evolgenius.info/evolview/ EvolView: an online tree viewer] | ||
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** Go over pre-test questions | ** Go over pre-test questions | ||
** In-class exercise 1 (5 pts) | ** In-class exercise 1 (5 pts) | ||
** Introduction to tree | ** Introduction to tree | ||
* 9/12 (TH). Intro to trees (continued) | * 9/12 (TH). Intro to trees (continued) | ||
** In-class exercise 2. (5 pts) | ** In-class exercise 2. (5 pts) | ||
Line 91: | Line 92: | ||
** Chapter 5. pages 199-200 (Figure 5.31) | ** Chapter 5. pages 199-200 (Figure 5.31) | ||
** In-class exercise 3. (5 pts, due next session) | ** In-class exercise 3. (5 pts, due next session) | ||
** Lecture Slides. | ** Lecture Slides: [[File:Part-1-tree-thinking-2019.pdf|thumbnail]] | ||
* 9/23 (M). 4:10 - 5:10pm '''Midterm Exam I''' <font color="red">Bring pencils, erasers, and a calculator</font> | * 9/23 (M). 4:10 - 5:10pm '''Midterm Exam I''' <font color="red">Bring pencils, erasers, and a calculator</font> | ||
===Part 2. | ===Part 2. Analysis of Trait Evolution=== | ||
* 9/26 (TH). Traits & trait matrix | * 9/26 (TH). Traits & trait matrix | ||
** Textbook Chapter 5, pages 180-183 | ** Textbook Chapter 5, pages 180-183 | ||
** R demo I (by Chris) | |||
<syntaxhighlight lang='bash'> | |||
# iris dataset exercise | |||
# load libraries | |||
library(tidyverse) | |||
library(datasets) | |||
data('iris') | |||
# summary of data | |||
summary(iris) | |||
glimpse(iris) | |||
iris %>% glimpse() | |||
# previewing data | |||
head(iris) | |||
# subsetting data | |||
slice(iris, 1:3) | |||
iris %>% slice(1:3) | |||
# grouping and subsetting data | |||
iris %>% | |||
group_by(Species) %>% | |||
slice(1:3) | |||
iris %>% | |||
group_by(Species) %>% | |||
summarise(average = mean(Sepal.Length)) | |||
# filtering data | |||
filter(iris, Species == 'versicolor') | |||
iris %>% | |||
filter(Species == 'versicolor') | |||
iris %>% | |||
filter(Sepal.Length >= 7) | |||
# OR operation | |||
iris %>% | |||
filter(Sepal.Length < 5 | Sepal.Length > 7) | |||
# check distribution using histogram | |||
ggplot(iris, aes(x = Sepal.Length)) + | |||
geom_histogram() | |||
# distribution by Species | |||
ggplot(iris, aes(x = Sepal.Length, fill = Species)) + | |||
geom_histogram(alpha = 0.5) | |||
# distribution by Species using facetwrap | |||
ggplot(iris, aes(x = Sepal.Length, color = Species)) + | |||
geom_histogram() + facet_wrap(~Species) | |||
# boxplot | |||
ggplot(iris, aes(y = Sepal.Length, x = Species)) + | |||
geom_boxplot() | |||
# boxplot with points | |||
ggplot(iris, aes(y = Sepal.Length, x = Species)) + | |||
geom_boxplot() + | |||
geom_jitter(size = 2, width = 0.1, alpha = 0.5, color = 'blue') | |||
# scatterplot | |||
ggplot(iris, aes(y = Sepal.Length, x = Petal.Length, color = Species)) + geom_point() | |||
</syntaxhighlight> | |||
{| class="wikitable sortable mw-collapsible" | {| class="wikitable sortable mw-collapsible" | ||
|- style="background-color:lightsteelblue;" | |- style="background-color:lightsteelblue;" | ||
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|} | |} | ||
* 10/3 (TH). Homoplasy & consistency | * 10/3 (TH). Homoplasy & consistency | ||
** Character & Character states | |||
** R Demo (part 2) (Crhis) | |||
{| class="wikitable sortable mw-collapsible" | |||
|- style="background-color:lightsteelblue;" | |||
! Bonus R Exercise (10 pts; Due 10/10, Thursday) | |||
|- style="background-color:white;" | |||
| | |||
# In R studio, load the tidyverse library and read the human gene data table with <code>hg <- read_tsv(file = "http://diverge.hunter.cuny.edu/~weigang/data-sets-for-biostat/hg.tsv2", col_name = T)</code> | |||
# Show commands and outputs for the following operations: | |||
## Show first three genes for each chromosome | |||
## Count the number of genes on each chromosome | |||
## Add a column called "Gene.Length" | |||
## Calculate the mean, max, and min gene length on each chromosome | |||
## Show distribution of gene length by a histogram (with binwidth=1e4) | |||
## Show above with log10 transformation | |||
## Show distribution of gene length on each chromosome (with facet_wrap) | |||
## Show distribution of gene length on each chromosome with a boxplot | |||
|} | |||
* 10/7 (M). Parsimony reconstruction (Chapter 5). | |||
** Textbook Chapter 5, pages 188-191 | |||
{| class="wikitable sortable mw-collapsible" | {| class="wikitable sortable mw-collapsible" | ||
|- style="background-color:lightsteelblue;" | |- style="background-color:lightsteelblue;" | ||
Line 113: | Line 199: | ||
| | | | ||
# Download or Copy/Paste [http://media.hhmi.org/biointeractive/activities/lizard/Anolis-DNA-sequences.txt the lizard DNA sequences] to your own computer and save the file as "lizard.txt" | # Download or Copy/Paste [http://media.hhmi.org/biointeractive/activities/lizard/Anolis-DNA-sequences.txt the lizard DNA sequences] to your own computer and save the file as "lizard.txt" | ||
# Align the DNA sequences [http://www.phylogeny.fr/one_task.cgi?task_type=muscle using this website] and save the aligned DNA file ("Output->Alignment in Fasta format") as "lizard-aligned.txt". Use "one-click" option to make a tree. | # Align the DNA sequences [http://www.phylogeny.fr/one_task.cgi?task_type=muscle using this website] and save the aligned DNA file ("Output->Alignment in Fasta format") as "lizard-aligned.txt". Use "one-click" option in the Phylogeny Analysis tab to make a tree. | ||
# Based on [http://media.hhmi.org/biointeractive/activities/lizard/Lizard-Cards-Color.pdf the lizard card], construct a character-state matrix for all lizard species. For each species, list its character state for each of the following two characters (as columns): (1) Geographic origin, and (2) Habitat. | # Based on [http://media.hhmi.org/biointeractive/activities/lizard/Lizard-Cards-Color.pdf the lizard card], construct a character-state matrix for all lizard species. For each species, list its character state for each of the following two characters (as columns): (1) Geographic origin, and (2) Habitat. | ||
# Construct a diagram by combining the tree and the character-state matrix, showing character states for each species on each row. | |||
# Determine which hypothesis ("Multiple origin" or "Single origin" of ecomorphs) is more supported by the mtDNA tree. Explain. | # Determine which hypothesis ("Multiple origin" or "Single origin" of ecomorphs) is more supported by the mtDNA tree. Explain. | ||
|} | |} | ||
* 10/10 (TH). Parsimony reconstruction (Continued) | * 10/10 (TH). Parsimony reconstruction (Continued) | ||
** In-Class Exercise 4 | ** In-Class Exercise 4 [[File:In-class-4.pdf|thumbnail]] | ||
** Lecture slides: [[File:Part-2-trait-evolution-2019-small.pdf|thumbnail]] | |||
* 10/16 (Wed. Monday Schedule). Genome & gene structure (Chapter 3) | * 10/16 (Wed. Monday Schedule). Genome & gene structure (Chapter 3) | ||
** Calculate consistency indices for lizard ecomorphs & geographic orgins | |||
** [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622293/ | Graur et al (2013). "On the immotality of television sets"] | ** [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622293/ | Graur et al (2013). "On the immotality of television sets"] | ||
* 10/17 (TH). Review & Practices. | * 10/17 (TH). Review & Practices. | ||
** In-Class Exercise | ** In-class exercise: hemoglobin gene structure [[File:In-class-5.pdf|thumbnail]] | ||
** In-Class Exercise: Pretest Part 2, [[File:Pretest-2.pdf|thumbnail]] | |||
* 10/21 (M). '''Midterm Exam 2''' | * 10/21 (M). '''Midterm Exam 2''' | ||
===Part 3. Tree Algorithms=== | ===Part 3. Tree Algorithms=== | ||
* 10/24 (TH). BLAST & Alignments (Chapter 3. pages 93-100). In-class exercise: Run BLAST; show alignment & explain E-value | * 10/24 (TH). (No Class) | ||
* 10/ | * 10/28 (M). | ||
* | ** BLAST & Alignments (Chapter 3. pages 93-100).In-class exercise: Run BLAST; show alignment & explain E-value | ||
* 11/4 (M). Distance methods (Chapter 5, pages 184-187). In class exercise: use APE package to calculate genetic distances | ** Genetic distances | ||
* 11/7 (TH). Likelihood & Bayesian methods; Tree Testing (Chapter 5, pages 194-198). | * 10/31 (TH). | ||
* 11/ | ** Sequence-evolutionary models (Chapter 3, pages 79-88). In-class exercise: Poisson simulation & explain | ||
* 11/ | ** Lecture slides: [[File:Part-3-tree-construction-2019.pdf|thumbnail]] | ||
* 11/4 (M). | |||
** Distance methods (Chapter 5, pages 184-187). In class exercise: use APE package to calculate genetic distances | |||
** In class exercise: calculate Jukes-Cantor distance of [http://slideplayer.com/slide/8016962/25/images/8/Example+of+DNA+sequence+alignment.jpg this DNA sequence alignment]. Note: Ignore gapped positions. | |||
* 11/7 (TH). | |||
** Maximum parsimony (Chapter 5, pages 191-194). In-class exercise: parsimony scores | |||
** Likelihood & Bayesian methods; | |||
** Bonus assignment II (5 pts, Due 11/18, Monday): | |||
{| class="wikitable" | |||
|- | |||
| | |||
* The two graphs show the log likelihoods (i.e., goodness of fit, or Prob(Data|Model)) of four nucleotide-substitution models for describing patterns of Human/Chimp DNA sequence divergence | |||
* Reproduce (with proper axis labels and custom size and shape for the points) one of the graphs using R/ggplot2. Read the data set using <code>lk <- read_csv("http://diverge.hunter.cuny.edu/~weigang/lk.csv")</code> | |||
* Explain why HKY is the best model for the data | |||
| [[File:Lk-plot-label.png|thumbnail|Hint: use geom_label()]] || [[File:Lk-plot-color.png|thumbnail|Hint: use geom_point()]] | |||
|} | |||
* 11/11 (M). | |||
** Tree Testing (Chapter 5, pages 194-198). | |||
* 11/14 (TH). | |||
** Review exercises (Chapter 5, pages 207-209) . | |||
* 11/18 (M). '''3rd Mid-term exam''' | |||
===Part 4. Mechanisms of molecular evolution=== | ===Part 4. Mechanisms of molecular evolution=== | ||
* 11/ | * 11/21 (TH). | ||
** Mechanism of molecular evolution: Overview (pages 35-38) & Rates of nucleotide substitutions (pages 111-125). | |||
* 11/25 (M). In-class computer exercise: | * 11/25 (M). In-class computer exercise: | ||
** Ka/Ks test of natural selection (pg 116-124). In-class exercise | |||
{| class="wikitable sortable mw-collapsible" | {| class="wikitable sortable mw-collapsible" | ||
|- style="background-color:lightsteelblue;" | |- style="background-color:lightsteelblue;" | ||
! Final project (20 pts). Due: 12/ | ! Final project (20 pts). Due: 12/9, Monday) | ||
|- style="background-color:white;" | |- style="background-color:white;" | ||
| | | | ||
# Calculate genetic distances | # Calculate genetic distances | ||
## Download or Copy/Paste [http://media.hhmi.org/biointeractive/activities/lizard/Anolis-DNA-sequences.txt the lizard DNA sequences] to your own computer and save the file as "anoles.txt" | ## Download or Copy/Paste [http://media.hhmi.org/biointeractive/activities/lizard/Anolis-DNA-sequences.txt the lizard DNA sequences] to your own computer and save the file as "anoles.txt" in a directory (e.g., "Document") | ||
## Align the DNA sequences [http://www.phylogeny.fr/one_task.cgi?task_type=muscle using this website] and save the aligned DNA file ("Output->Alignment in Fasta format") as "anoles-aligned.txt" (No need to print or submit the above two DNA sequence files; save them in a folder) | ## Align the DNA sequences [http://www.phylogeny.fr/one_task.cgi?task_type=muscle using this website] and save the aligned DNA file ("Output->Alignment in Fasta format") as "anoles-aligned.txt" (No need to print or submit the above two DNA sequence files; save them in a folder, e.g., "Document") | ||
## | ## Download & load library: library(ape) | ||
## Read alignment: mt | ## In RStudio, set working directory to the same one containing alignemnt ("Session" -> "Set Working Directory" -> "Choose Directory") | ||
## Calculate raw distance: mt.raw | ## Read alignment: mt <- read.FASTA("anoles-aligned.txt") | ||
## Apply Juke-Cantor (one-parameter model) correction: mt.jc | ## Calculate raw distance: mt.raw <- dist.dna(mt, model = "raw") | ||
## Apply Kimura(two-parameter model, for Ts and Tv) correction: mt.k80 | ## Apply Juke-Cantor (one-parameter model) correction: mt.jc <- dist.dna(mt, model = "JC") | ||
## Apply Kimura(two-parameter model, for Ts and Tv) correction: mt.k80 <- dist.dna(mt, model = "K80") to | |||
## Plot JC distance vs the raw distance: plot(mt.raw, mt.jc, xlab = "uncorrected distance (diff/site)", ylab = "corrected distance (sub/site)", xlim = c(0,0.4), ylim = c(0,0.5), las =1) | ## Plot JC distance vs the raw distance: plot(mt.raw, mt.jc, xlab = "uncorrected distance (diff/site)", ylab = "corrected distance (sub/site)", xlim = c(0,0.4), ylim = c(0,0.5), las =1) | ||
## Add a 1:1 line: abline(0,1, col = "red") | ## Add a 1:1 line: abline(0,1, col = "red") | ||
Line 158: | Line 270: | ||
## Add a legend: legend(0.05, 0.45, legend = c("JC (1-parameter)", "K80 (2-parameter)"), pch = c(1,3), col = c("black","blue"), bty = "n") | ## Add a legend: legend(0.05, 0.45, legend = c("JC (1-parameter)", "K80 (2-parameter)"), pch = c(1,3), col = c("black","blue"), bty = "n") | ||
## Export an PDF and print a copy | ## Export an PDF and print a copy | ||
## Use the graph to explain (1) Why it is necessary to correct for raw distances when comparing sequences from distantly related species; (2) What is the key difference between the K80 and JC models | ## Use the graph to explain | ||
### (1) Why it is necessary to correct for raw distances when comparing sequences from distantly related species; | |||
### (2) What is the key difference between the K80 and JC models | |||
# Comparison of distance and parsimony trees (review previous assignments for detailed R-Studio instructions) | # Comparison of distance and parsimony trees (review previous assignments for detailed R-Studio instructions) | ||
## In R studio, install & load the "ape" and " | ## In R studio, install & load the "ape" and "phangorn" libraries | ||
### Obtain a neighbor-joining tree using K80 model: tree.nj | ### Obtain a neighbor-joining tree using K80 model: tree.nj <- NJ(mt.k80) | ||
### Plot a midpoint rooted tree: plot(midpoint(tree.nj)) | ### Plot a midpoint rooted tree: plot(midpoint(tree.nj)) | ||
### Add a scale bar: add.scale.bar() | ### Add a scale bar: add.scale.bar() | ||
### Print tree and answer this question: what does the distance represent? What is the unit? | ### Print tree and answer this question: what does the distance represent? What is the unit? | ||
## Obtain a maximum parsimony tree | ## Obtain a maximum parsimony tree | ||
### Convert object to a different class: aln.phy | ### Convert object to a different class: aln.phy <- as.phyDat(mt) | ||
### Search maximum parsimony tree.mp | ### Search maximum parsimony tree.mp <- optim.parsimony(tree.nj, aln.phy) | ||
### Get tree distance: tree.mp | ### Get tree distance: tree.mp <- acctran(tree.mp, aln.phy) | ||
### Plot tree: plot(midpoint(tree.mp)) | ### Plot tree: plot(midpoint(tree.mp)) | ||
### Add a scale bar: add.scale.bar() | ### Add a scale bar: add.scale.bar() | ||
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# Bootstrap analysis | # Bootstrap analysis | ||
## aln.fas <- read.dna("anoles-aligned.txt", format ="fasta") | ## aln.fas <- read.dna("anoles-aligned.txt", format ="fasta") | ||
## Create a function for re-rooted distance tree: tree.fun | ## Create a function for re-rooted distance tree: tree.fun <- function(x) root(nj(dist.dna(x)), outgroup = c("Leiocephalus_barahonensis"), resolve.root = T) | ||
## Calculate a tree: tr | ## Calculate a tree: tr <- tree.fun(aln.fas) | ||
## Perform bootstrap for 100 pseudo-replicates: boot.trees | ## Perform bootstrap for 100 pseudo-replicates: boot.trees <- boot.phylo(tr, aln.fas, tree.fun, B=100, rooted =T) | ||
## Plot tree: plot(tr, no.margin = T) | ## Plot tree: plot(tr, no.margin = T) | ||
## Add bootstrap values as node labels: nodelabels(boot.trees, bg= "white") | ## Add bootstrap values as node labels: nodelabels(boot.trees, bg= "white") | ||
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* 12/2 (M). SNP statistics & gene frequency analysis: In-class exercises. | * 12/2 (M). SNP statistics & gene frequency analysis: In-class exercises. | ||
* 12/5 (TH) Genetic Drift (pages 47-49). Lecture slides: [[File:Part-4-evol-mechanism-2018.pdf|thumbnail]] | * 12/5 (TH) Genetic Drift (pages 47-49). Lecture slides: [[File:Part-4-evol-mechanism-2018.pdf|thumbnail]] | ||
* 12/9 (M). (Last Lecture) Review & Course evaluations. Final review slides: [[File:Final-review- | * 12/9 (M). (Last Lecture) Review & Course evaluations. Final review slides: [[File:Final-review-2019.pdf|thumbnail]] | ||
** '''Submit your Teacher's Evaluation''', using either: | ** '''Submit your Teacher's Evaluation''', using either: | ||
** Personal computer at [http://www.hunter.cuny.edu/te www.hunter.cuny.edu/te]; or, | ** Personal computer at [http://www.hunter.cuny.edu/te www.hunter.cuny.edu/te]; or, | ||
** Smartphone at [http://www.hunter.cuny.edu/mobilete www.hunter.cuny.edu/mobilete] | ** Smartphone at [http://www.hunter.cuny.edu/mobilete www.hunter.cuny.edu/mobilete] | ||
* | * Dec 16 (Monday) 4-6pm: '''Comprehensive Final Exam''' |
Latest revision as of 16:37, 9 December 2019
Course Description
Molecular evolution is the study of the change of DNA and protein sequences through time. Theories and techniques of molecular evolution are widely used in species classification, biodiversity, comparative genomics, and molecular epidemiology. Contents of the course include:
- Population genetics, which is a theoretical framework for understanding mechanisms of sequence evolution through mutation, recombination, gene duplication, genetic drift, and natural selection.
- Molecular systematics, which introduces statistical models of sequence evolution and methods for reconstructing species phylogeny.
- Bioinformatics, which provides hands-on training on data acquisition and the use of software tools for phylogenetic analyses.
This 3-credit course is designed for upper-level biology-major undergraduates. Hunter pre-requisites are BIOL203, and MATH150 or STAT113.
Textbooks
- (Required) Graur, 2016, Molecular and Genome Evolution, First Edition, Sinauer Associates, Inc. ISBN: 978-1-60535-469-9. Publisher's Website (Student discount: a 15% discount and receive free UPS standard shipping)
http://www.sinauer.com/molecular-and-genome-evolution.html)
- (Recommended) Baum & Smith, 2013. Tree Thinking: an Introduction to Phylogenetic Biology, Roberts & Company Publishers, Inc.
Learning Goals
- Be able to describe evolutionary relationships using phylogenetic trees
- Be able to use web-based as well as stand-alone software to infer phylogenetic trees
- Understand mechanisms of DNA sequence evolution
- Understand algorithms for building phylogenetic trees
Links for phylogenetic tools
- NCBI sequence databases
- R Tools
- R source: download & install from a mirror site
- R Studio: download & install
- APE package
- phangorn package
- A Molecular Phylogeny Web Server
- EvolView: an online tree viewer
Exams & Grading
- Bonus for full attendance & active participation in classroom discussions.
- Assignments. All assignments should be handed in as hard copies only. Email submission will not be accepted. Late submissions will receive 10% deduction (of the total grade) per day.
- Three Mid-term Exams (30 pts each)
- Comprehensive Final Exam (50 pts)
Academic Honesty
While students may work in groups and help each other for assignments, duplicated answers in assignments will be flagged and investigated as possible acts of academic dishonesty. To avoid being investigated as such, do NOT copy anyone else's work, or let others copy your work. At the least, rephrase using your own words. Note that the same rule applies regarding the use of textbook and online resources: copied sentences are not acceptable and will be considered plagiarism.
Hunter College regards acts of academic dishonesty (e.g., plagiarism, cheating on examinations, obtaining unfair advantage, and falsification of records and official documents) as serious offenses against the values of intellectual honesty. The College is committed to enforcing the CUNY Policy on Academic Integrity and will pursue cases of academic dishonesty according to the Hunter College Academic Integrity Procedures.
Course Schedule
Part 1. Tree Thinking
- 8/29 (TH). Overview & Introduction. Textbook Chapter: "Introduction" (pages 1-3)
Assignment 1 (10 pts; Due next class 9/5) |
---|
|
- 9/5 (TH). Introduction (Continued)
- R terminologies
- Object: variable that contains data (e.g., "iris")
- Object class: type of data (e.g., "data.frame", which is a table)
- Function: e.g., data(iris), which loads the data set called "iris"
- Function arguments: input and options (e.g., "iris" above)
- Tutorial: R & R-Studio (Bring your own computer)
- Lecture slides:
- R terminologies
Assignment 2 (5 pts; Due: next session) |
---|
R exercises
|
- 9/9 (M). Intro to trees
- Go over pre-test questions
- In-class exercise 1 (5 pts)
- Introduction to tree
- 9/12 (TH). Intro to trees (continued)
- In-class exercise 2. (5 pts)
- Textbook Chapter 5: "Molecular Phylogenetics" (pages 170-175; 201-202)
- 9/16 (M). Species Tree & Lineage Sorting.
- Textbook Chapter 5: "Molecular Phylogenetics" (pages 177-180).
- 9/19 (TH). Consensus Tree & Review.
- Chapter 5. pages 199-200 (Figure 5.31)
- In-class exercise 3. (5 pts, due next session)
- Lecture Slides:
- 9/23 (M). 4:10 - 5:10pm Midterm Exam I Bring pencils, erasers, and a calculator
Part 2. Analysis of Trait Evolution
- 9/26 (TH). Traits & trait matrix
- Textbook Chapter 5, pages 180-183
- R demo I (by Chris)
# iris dataset exercise
# load libraries
library(tidyverse)
library(datasets)
data('iris')
# summary of data
summary(iris)
glimpse(iris)
iris %>% glimpse()
# previewing data
head(iris)
# subsetting data
slice(iris, 1:3)
iris %>% slice(1:3)
# grouping and subsetting data
iris %>%
group_by(Species) %>%
slice(1:3)
iris %>%
group_by(Species) %>%
summarise(average = mean(Sepal.Length))
# filtering data
filter(iris, Species == 'versicolor')
iris %>%
filter(Species == 'versicolor')
iris %>%
filter(Sepal.Length >= 7)
# OR operation
iris %>%
filter(Sepal.Length < 5 | Sepal.Length > 7)
# check distribution using histogram
ggplot(iris, aes(x = Sepal.Length)) +
geom_histogram()
# distribution by Species
ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
geom_histogram(alpha = 0.5)
# distribution by Species using facetwrap
ggplot(iris, aes(x = Sepal.Length, color = Species)) +
geom_histogram() + facet_wrap(~Species)
# boxplot
ggplot(iris, aes(y = Sepal.Length, x = Species)) +
geom_boxplot()
# boxplot with points
ggplot(iris, aes(y = Sepal.Length, x = Species)) +
geom_boxplot() +
geom_jitter(size = 2, width = 0.1, alpha = 0.5, color = 'blue')
# scatterplot
ggplot(iris, aes(y = Sepal.Length, x = Petal.Length, color = Species)) + geom_point()
Assignment #3 (5 pts; Due next session) |
---|
Watch Origin of Species: Lizards in an Evolutionary Tree. Provide short answer (1-3 sentences) to each of the following three questions.
|
- 10/3 (TH). Homoplasy & consistency
- Character & Character states
- R Demo (part 2) (Crhis)
Bonus R Exercise (10 pts; Due 10/10, Thursday) |
---|
|
- 10/7 (M). Parsimony reconstruction (Chapter 5).
- Textbook Chapter 5, pages 188-191
Assignment #4 (5 pts; Due next session) |
---|
|
- 10/10 (TH). Parsimony reconstruction (Continued)
- In-Class Exercise 4
- Lecture slides:
- 10/16 (Wed. Monday Schedule). Genome & gene structure (Chapter 3)
- Calculate consistency indices for lizard ecomorphs & geographic orgins
- | Graur et al (2013). "On the immotality of television sets"
- 10/17 (TH). Review & Practices.
- In-class exercise: hemoglobin gene structure
- In-Class Exercise: Pretest Part 2,
- 10/21 (M). Midterm Exam 2
Part 3. Tree Algorithms
- 10/24 (TH). (No Class)
- 10/28 (M).
- BLAST & Alignments (Chapter 3. pages 93-100).In-class exercise: Run BLAST; show alignment & explain E-value
- Genetic distances
- 10/31 (TH).
- Sequence-evolutionary models (Chapter 3, pages 79-88). In-class exercise: Poisson simulation & explain
- Lecture slides:
- 11/4 (M).
- Distance methods (Chapter 5, pages 184-187). In class exercise: use APE package to calculate genetic distances
- In class exercise: calculate Jukes-Cantor distance of this DNA sequence alignment. Note: Ignore gapped positions.
- 11/7 (TH).
- Maximum parsimony (Chapter 5, pages 191-194). In-class exercise: parsimony scores
- Likelihood & Bayesian methods;
- Bonus assignment II (5 pts, Due 11/18, Monday):
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- 11/11 (M).
- Tree Testing (Chapter 5, pages 194-198).
- 11/14 (TH).
- Review exercises (Chapter 5, pages 207-209) .
- 11/18 (M). 3rd Mid-term exam
Part 4. Mechanisms of molecular evolution
- 11/21 (TH).
- Mechanism of molecular evolution: Overview (pages 35-38) & Rates of nucleotide substitutions (pages 111-125).
- 11/25 (M). In-class computer exercise:
- Ka/Ks test of natural selection (pg 116-124). In-class exercise
Final project (20 pts). Due: 12/9, Monday) |
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- 12/2 (M). SNP statistics & gene frequency analysis: In-class exercises.
- 12/5 (TH) Genetic Drift (pages 47-49). Lecture slides:
- 12/9 (M). (Last Lecture) Review & Course evaluations. Final review slides:
- Submit your Teacher's Evaluation, using either:
- Personal computer at www.hunter.cuny.edu/te; or,
- Smartphone at www.hunter.cuny.edu/mobilete
- Dec 16 (Monday) 4-6pm: Comprehensive Final Exam