Biol375 2019 - Revision history

imported>Weigang: /* Part 4. Mechanisms of molecular evolution */

2019-12-09T16:37:56Z

Part 4. Mechanisms of molecular evolution

← Older revision		Revision as of 16:37, 9 December 2019
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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-~~2018~~.pdf\|thumbnail]]		* 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'''		* Dec 16 (Monday) 4-6pm: '''Comprehensive Final Exam'''

imported>Lab: change package spelling to phangorn

2019-12-05T21:53:53Z

change package spelling to phangorn

← Older revision		Revision as of 21:53, 5 December 2019
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	### (2) What is the key difference between the K80 and JC models		### (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 "~~phangnorm~~" libraries		## In R studio, install & load the "ape" and "phangorn" libraries
	### Obtain a neighbor-joining tree using K80 model: tree.nj <- NJ(mt.k80)		### 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))

imported>Weigang: /* Part 4. Mechanisms of molecular evolution */

2019-11-26T17:40:16Z

Part 4. Mechanisms of molecular evolution

← Older revision		Revision as of 17:40, 26 November 2019
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	# 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)		## Download & load library: library(ape)
			## In RStudio, set working directory to the same one containing alignemnt ("Session" -> "Set Working Directory" -> "Choose Directory")
	## Read alignment: mt <- read.FASTA("anoles-aligned.txt")		## Read alignment: mt <- read.FASTA("anoles-aligned.txt")
	## Calculate raw distance: mt.raw <- dist.dna(mt, model = "raw")		## Calculate raw distance: mt.raw <- dist.dna(mt, model = "raw")
	## Apply Juke-Cantor (one-parameter model) correction: mt.jc <- dist.dna(mt, model = "JC")		## 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")		## 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")

imported>Weigang: /* Part 4. Mechanisms of molecular evolution */

2019-11-23T19:11:16Z

Part 4. Mechanisms of molecular evolution

imported>Weigang: /* Part 4. Mechanisms of molecular evolution */

2019-11-15T01:38:42Z

Part 4. Mechanisms of molecular evolution

← Older revision		Revision as of 01:38, 15 November 2019
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	** 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]
	* ~~Date & Time to be determined~~: '''Comprehensive Final Exam'''		* Dec 16 (Monday) 4-6pm: '''Comprehensive Final Exam'''

imported>Weigang: /* Part 3. Tree Algorithms */

2019-11-14T21:04:38Z

Part 3. Tree Algorithms

← Older revision		Revision as of 21:04, 14 November 2019
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	** Maximum parsimony (Chapter 5, pages 191-194). In-class exercise: parsimony scores		** Maximum parsimony (Chapter 5, pages 191-194). In-class exercise: parsimony scores
	** Likelihood & Bayesian methods;		** Likelihood & Bayesian methods;
	** Bonus assignment II (5 pts):		** Bonus assignment II (5 pts, Due 11/18, Monday):
	{\| class="wikitable"		{\| class="wikitable"
	\|-		\|-
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	\|}		\|}

	* 11/11 (M~~). Review (Chapter 5, pages 207-209~~).		* 11/11 (M).
	** Tree Testing (Chapter 5, pages 194-198).		** Tree Testing (Chapter 5, pages 194-198).
	* 11/14 (TH).		* 11/14 (TH).
	** Review exercises.		** Review exercises (Chapter 5, pages 207-209) .
	* 11/18 (M). '''3rd Mid-term exam'''		* 11/18 (M). '''3rd Mid-term exam'''

imported>Weigang: /* Part 3. Tree Algorithms */

2019-11-08T15:17:38Z

Part 3. Tree Algorithms

← Older revision		Revision as of 15:17, 8 November 2019
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	* The ~~follow~~ two graphs ~~plot~~ 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		* 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) 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>		* 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		* Explain why HKY is the best model for the data
	\| [[File:Lk-plot-label.png\|thumbnail\|use ~~label~~]] \|\| [[File:Lk-plot-color.png\|thumbnail\|use ~~legend~~]]		\| [[File:Lk-plot-label.png\|thumbnail\|Hint: use geom_label()]] \|\| [[File:Lk-plot-color.png\|thumbnail\|Hint: use geom_point()]]
	\|}		\|}

imported>Weigang: /* Part 3. Tree Algorithms */

2019-11-08T15:15:15Z

Part 3. Tree Algorithms

← Older revision		Revision as of 15:15, 8 November 2019
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	\|		\|
	[[File:Lk-plot-label.png\|thumbnail\|use label]]		* The follow two graphs plot 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) 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>
	[[File:Lk-plot-color.png\|thumbnail\|use legend]]		* Explain why HKY is the best model for the data
			\| [[File:Lk-plot-label.png\|thumbnail\|use label]] \|\| [[File:Lk-plot-color.png\|thumbnail\|use legend]]
	\|}		\|}
	~~Plot likelihood value & explain why HKY85 is the best model~~
	* 11/11 (M). Review (Chapter 5, pages 207-209).		* 11/11 (M). Review (Chapter 5, pages 207-209).
	** Tree Testing (Chapter 5, pages 194-198).		** Tree Testing (Chapter 5, pages 194-198).

imported>Weigang: /* Part 3. Tree Algorithms */

2019-11-08T15:08:43Z

Part 3. Tree Algorithms

← Older revision		Revision as of 15:08, 8 November 2019
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	** Maximum parsimony (Chapter 5, pages 191-194). In-class exercise: parsimony scores		** Maximum parsimony (Chapter 5, pages 191-194). In-class exercise: parsimony scores
	** Likelihood & Bayesian methods;		** Likelihood & Bayesian methods;
	** Bonus assignment II (5 pts): Plot likelihood value & explain why HKY85 is the best model		** Bonus assignment II (5 pts):
			{\| class="wikitable"
			\|-
			\|
			[[File:Lk-plot-label.png\|thumbnail\|use label]]
			\|\|
			[[File:Lk-plot-color.png\|thumbnail\|use legend]]
			\|}
			Plot likelihood value & explain why HKY85 is the best model
	* 11/11 (M). Review (Chapter 5, pages 207-209).		* 11/11 (M). Review (Chapter 5, pages 207-209).
	** Tree Testing (Chapter 5, pages 194-198).		** Tree Testing (Chapter 5, pages 194-198).

imported>Weigang: /* Part 3. Tree Algorithms */

2019-11-08T14:44:44Z

Part 3. Tree Algorithms

← Older revision		Revision as of 14:44, 8 November 2019
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	* 11/14 (TH).		* 11/14 (TH).
	** Review exercises.		** Review exercises.
			* 11/18 (M). '''3rd Mid-term exam'''

	===Part 4. Mechanisms of molecular evolution===		===Part 4. Mechanisms of molecular evolution===

← Older revision		Revision as of 19:11, 23 November 2019
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	{\| class="wikitable sortable mw-collapsible"		{\| class="wikitable sortable mw-collapsible"
	\|- style="background-color:lightsteelblue;"		\|- style="background-color:lightsteelblue;"
	! Final project (20 pts). Due: 12/6, ~~Thursday~~)		! Final project (20 pts). Due: 12/9, Monday)
	\|- style="background-color:white;"		\|- style="background-color:white;"
	\|		\|
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	## 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"
	## 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)
	## ~~Load~~ library: library(ape)		## Download & load library: library(ape)
	## Read alignment: mt = read.FASTA("anoles-aligned.txt")		## Read alignment: mt <- read.FASTA("anoles-aligned.txt")
	## Calculate raw distance: mt.raw = dist.dna(mt, model = "raw")		## Calculate raw distance: mt.raw <- dist.dna(mt, model = "raw")
	## Apply Juke-Cantor (one-parameter model) correction: mt.jc = dist.dna(mt, model = "JC")		## 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")		## Apply Kimura(two-parameter model, for Ts and Tv) correction: mt.k80 <- dist.dna(mt, model = "K80")
	## 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")
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	## 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 "phangnorm" libraries		## In R studio, install & load the "ape" and "phangnorm" libraries
	### Obtain a neighbor-joining tree using K80 model: tree.nj = NJ(mt.k80)		### 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 = as.phyDat(mt)		### Convert object to a different class: aln.phy <- as.phyDat(mt)
	### Search maximum parsimony tree.mp = optim.parsimony(tree.nj, aln.phy)		### Search maximum parsimony tree.mp <- optim.parsimony(tree.nj, aln.phy)
	### Get tree distance: tree.mp = acctran(tree.mp, aln.phy)		### 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 = function(x) root(nj(dist.dna(x)), outgroup = c("Leiocephalus_barahonensis"), resolve.root = T)		## 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 = tree.fun(aln.fas)		## Calculate a tree: tr <- tree.fun(aln.fas)
	## Perform bootstrap for 100 pseudo-replicates: boot.trees = boot.phylo(tr, aln.fas, tree.fun, B=100, rooted =T)		## 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")