Biol375 2019 and EEB BootCamp 2020: Difference between pages

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<center>'''Molecular Evolution''' (BIOL 375.00/790.64/793.03, Fall 2019)</center>
<center>Bioinformatics Boot Camp for Ecology & Evolution: '''Pathogen Evolutionary Genomics'''</center>
<center>'''Instructor:''' Dr Weigang Qiu, Professor, Department of Biological Sciences </center>
<center>Thursday, Aug 6, 2020, 2 - 3:30pm</center>
<center>'''Room:''' 926 HN (Seminar Room, North Building)</center>
<center>'''Instructors:''' Dr Weigang Qiu & Ms Saymon Akther</center>
<center>'''Hours:''' Mon. & Thur 4:10-5:25 pm</center>
<center>'''Email:''' weigang@genectr.hunter.cuny.edu</center>
<center>'''Office Hours:''' Belfer Research Building ([https://www.google.com/maps/place/413+E+69th+St,+New+York,+NY+10021/@40.7655886,-73.9561743,17z/data=!3m1!4b1!4m2!3m1!1s0x89c258c3d235f76f:0x4f3d0d5d8a78fe6?hl=en Google Map]) BB-402; Fridays 3-5pm or by appointment</center>
<center>'''Lab Website:''' http://diverge.hunter.cuny.edu/labwiki/</center>
<center>'''Course Website:''' http://diverge.hunter.cuny.edu/labwiki/Biol375_2019</center>
<center>
<center>christopher.panlasigui47@myhunter.cuny.edu</center>
{| class="wikitable"
----
[[File:Borreliabase-screenshot-1.png|350px|thumbnail]]
==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. [http://www.sinauer.com/molecular-and-genome-evolution.html 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==
* [http://www.ncbi.nlm.nih.gov/ NCBI sequence databases]
* R Tools
** R source: download & install from [https://mirrors.nics.utk.edu/cran/ a mirror site]
** R Studio: [https://www.rstudio.com/ download & install]
** APE package
* [http://phylogeny.fr/ A Molecular Phylogeny Web Server]
* [http://www.evolgenius.info/evolview/ 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, <font color="red">do NOT copy anyone else's work, or let others copy your work</font>. 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)
{| class="wikitable sortable mw-collapsible"
! Assignment 1 (10 pts; Due next class 9/5)
|-
|-
|
! Lyme Disease (Borreliella) !! CoV Genome Tracker !! Coronavirus evolutuon
* (10 pts) Pre-test: Full credits will be given as long as each question is answered with some reasoning. In other words, it will NOT be graded on being right or wrong. It's an assessment tool, to be compared with later test outcomes to show teaching/learning results. [[File:Pretest.pdf|thumbnail]]
|}
* 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 <font color="red">(Bring your own computer)</font>
** Lecture slides: [[File:Intro-2019.pdf|thumbnail]]
{| class="wikitable sortable mw-collapsible"
! Assignment 2 (5 pts; Due: next session)
|-
|-
| R exercises
| [[File:Lp54-gain-loss.png|300px|thumbnail| Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)]] ||
# Install R & R-studio (see "Links for phylogenetic tools" above)
[[File:Cov-screenshot-1.png|300px|thumbnail| [http://cov.genometracker.org/ Haplotype network] ]]
# Open R-studio and install the "ape" package using the "Packages"->"Install" menu, located within the lower right window
||
# Type in the console window (lower left) the following commands (one at a time, wait for the prompt ">" to appear before proceed to the next command; quit & restart R-studio if stuck):
[[File:Cov-screenshot-2.png|300px|thumbnail| Spike protein alignment ]]
## library(ape)
## tr <- read.tree(text = "(monkey:0.09672,((tarsier:0.18996,lemur:0.14790)0.999:0.09005,(macaque:0.18524,(gibbon:0.10388,(orang-utan:0.09481,(human:0.03391,(gorilla:0.06135,chimpanzee:0.05141):0.01580)0.316:0.05381)1.000:0.03019)0.978:0.05616)0.997:0.05042)0.965:0.09672);")
## plot(tr)
# Export the tree graph using the "Export"->"Save as PDF" or "Save as Image" menu in the lower right window
# Exit R studio by typing the command "q()" and type "y" to answer the question for saving the R session
# Copy & paste the tree image into your document to be handed in
|}
|}
* 9/9 (M). Intro to trees
</center>
** Go over pre-test questions
----
** In-class exercise 1 (5 pts)
 
** Introduction to tree
==Case studies from Qiu Lab==
* 9/12 (TH).  Intro to trees (continued)
* [http://borreliabase.org Comparative genomics of worldwide Lyme disease pathogens]
** In-class exercise 2. (5 pts)
* [http://cov.genometracker.org Covid-19 Genome Tracker]
** 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: [[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>


===Part 2. Analysis of Trait Evolution===
==CoV genome data set==
* 9/26 (TH). Traits & trait matrix
* N=565 SARS-CoV-2 genomes collected during January & February 2020. Data source & acknowledgement [http://gisaid.org GIDAID] (<em>Warning: You need to acknowledge GISAID if you reuse the data in any publication</em>)
** Textbook Chapter 5, pages 180-183
* Download file: [http://diverge.hunter.cuny.edu/~weigang/qiu-akther.tar.gz data file]
** R demo I (by Chris)
* Create a directory, unzip, & un-tar
<syntaxhighlight lang='bash'>
mkdir QiuAkther
mv cov-camp.tar.gz QiuAkther/
cd QiuAkther
tar -tzf cov-camp.tar.gz # view files
tar -xzf cov-camp.tar.gz # un-zip & un-tar
</syntaxhighlight>
* View files
<syntaxhighlight lang='bash'>
<syntaxhighlight lang='bash'>
# iris dataset exercise
file TCS.jar
# load libraries
ls -lrt # long list, in reverse timeline
library(tidyverse)
less Jan-Feb.mafft # an alignment of 565 CoV2 genomes in FASTA format; "q" to quit
library(datasets)
less cov-565strains-617snvs.phy # non-gapped SNV alignment in PHYLIP format
data('iris')
wc hap.txt # geographic origins
 
head hap.txt
# summary of data
wc group.txt # color assignment
summary(iris)
cat group.txt
glimpse(iris)
less cov-565strains.gml # graph file (output)
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>
</syntaxhighlight>
{| class="wikitable sortable mw-collapsible"
|- style="background-color:lightsteelblue;"
! Assignment #3 (5 pts; Due next session)
|- style="background-color:white;"
|Watch [http://media.hhmi.org/biointeractive/films/OriginSpecies-Lizards.html Origin of Species: Lizards in an Evolutionary Tree]. Provide short answer (1-3 sentences) to each of the following three questions.
# What are the two hypotheses explaining the origin of different ecomorphs of lizards on Caribbean Islands?
# What is the expected phylogeny under each hypothesis?
# Which hypothesis is supported by the phylogeny of actual DNA sequences?
|}
* 10/3 (TH). Homoplasy & consistency
** R Demo (part 2) (Crhis)
{| class="wikitable sortable mw-collapsible"
|- style="background-color:lightsteelblue;"
! Assignment #4 (5 pts; Due next session)
|- style="background-color:white;"
|
# 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 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.
# Determine which hypothesis ("Multiple origin" or "Single origin" of ecomorphs) is more supported by the mtDNA tree. Explain.
|}
* 10/7 (M). Parsimony reconstruction (Chapter 5).
** Textbook Chapter 5, pages 188-191
* 10/10 (TH). Parsimony reconstruction (Continued)
** In-Class Exercise 4 (Due next session)
* 10/16 (Wed. Monday Schedule). Genome & gene structure (Chapter 3)
** [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622293/ | Graur et al (2013). "On the immotality of television sets"]
* 10/17 (TH). Review & Practices. Lecture Slides
** In-Class Exercise 5. Pretest Part 2 (molecular phylogenetics in forensics)
* 10/21 (M). '''Midterm Exam 2'''


===Part 3. Tree Algorithms===
==Bioinformatics Tools & Learning Goals==
* 10/24 (TH). BLAST & Alignments (Chapter 3. pages 93-100). In-class exercise: Run BLAST; show alignment & explain E-value
* BpWrapper: commandline tools for sequence, alignment, and tree manipulations (based on BioPerl).
* 10/28 (M). Genetic distances & Sequence-evolutionary models (Chapter 3, pages 79-88). In-class exercise: Poisson simulation & explain
** [https://github.com/bioperl/p5-bpwrapper Github Link]
* 10/31 (TH). Maximum parsimony (Chapter 5, pages 191-194). In-class exercise: parsimony scores
** [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2074-9/figures/1 Flowchart from publication]
* 11/4 (M). Distance methods (Chapter 5, pages 184-187). In class exercise: use APE package to calculate genetic distances
* Haplotype network with TCS [https://pubmed.ncbi.nlm.nih.gov/11050560/ PubMed link]
* 11/7 (TH). Likelihood & Bayesian methods; Tree Testing (Chapter 5, pages 194-198).  
* Web-interactive visualization with [http://D3js.org D3js]
* 11/11 (M). Review (Chapter 5, pages 207-209). Review exercises. Lecture slides: [[File:Part-3-tree-construction-small-2018.pdf|thumbnail]]
** [https://github.com/sairum/tcsBU Github link]
* 11/14 (TH). '''Midterm Exam 3'''
** [https://cibio.up.pt/software/tcsBU/index.html Web tool]
** [https://academic.oup.com/bioinformatics/article/32/4/627/1744448 Paper]


===Part 4. Mechanisms of molecular evolution===
==Tutorial==
* 11/18 (M). Mechanism of molecular evolution: Overview (pages 35-38) & Rates of nucleotide substitutions (pages 111-125).
* 2-2:30: Introduction on pathogen phylogenomics
* 11/21 (TH). Ka/Ks test of natural selection (pg 116-124). In-class exercise
* 2:30-2:45: Demo: sequence manipulation with BpWrapper
* 11/25 (M). In-class computer exercise:
<syntaxhighlight lang='bash'>
{| class="wikitable sortable mw-collapsible"
bioseq --man
|- style="background-color:lightsteelblue;"
bioseq -n Jan-Feb.mafft
! Final project (20 pts). Due: 12/6, Thursday)
bioaln --man
|- style="background-color:white;"
bioaln -n -i'fasta' Jan-Feb.mafft
|
bioaln -l -i'fasta' Jan-Feb.mafft
# Calculate genetic distances
bioaln -n -i'phylip' cov-565strains-617snvs.phy
## 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"
bioaln -l -i'phylip' cov-565strains-617snvs.phy
## 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)
FastTree -nt cov-565strains-617snvs.phy > cov.dnd
## Load library: library(ape)
biotree --man
## Read alignment: mt = read.FASTA("anoles-aligned.txt")
biotree -n cov.dnd
## Calculate raw distance: mt.raw = dist.dna(mt, model = "raw")
biotree -l cov.dnd
## Apply Juke-Cantor (one-parameter model) correction: mt.jc = dist.dna(mt, model = "JC")
<syntaxhighlight>
## Apply Kimura(two-parameter model, for Ts and Tv) correction: mt.k80 = dist.dna(mt, model = "K80")
* 2:45-3:00: build haplotype network with TCS
## 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)
<syntaxhighlight lang='bash'>
## Add a 1:1 line: abline(0,1, col = "red")
java -jar -Xmx1g TCS.jar
## Add K80 distances: points(mt.raw, mt.k80, pch = 3, col = "blue")
<syntaxhighlight>
## 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")
* 3:00-3:15: interactive visualization with BuTCS
## Export an PDF and print a copy
* 3:15-3:30: Q & A
## 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)
## In R studio, install & load the "ape" and "phangnorm" libraries
### Obtain a neighbor-joining tree using K80 model: tree.nj = NJ(mt.k80)
### Plot a midpoint rooted tree: plot(midpoint(tree.nj))
### Add a scale bar: add.scale.bar()
### Print tree and answer this question: what does the distance represent? What is the unit?
## Obtain a maximum parsimony tree
### Convert object to a different class: aln.phy = as.phyDat(mt)
### Search maximum parsimony tree.mp = optim.parsimony(tree.nj, aln.phy)
### Get tree distance: tree.mp = acctran(tree.mp, aln.phy)
### Plot tree: plot(midpoint(tree.mp))
### Add a scale bar: add.scale.bar()
### Print tree and answer the question: what does the distance represent? What is the unit?
## Compare the two trees and explain the differences in these two methods: Which one uses full sequence information and why?
# Bootstrap analysis
## 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)
## 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)
## Plot tree: plot(tr, no.margin = T)
## Add bootstrap values as node labels: nodelabels(boot.trees, bg= "white")
## Explain (1) Does bootstrap test for tree precision or tree accuracy? (2) What does a bootstrap value of 80% mean?
|}
* 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]]
* 12/9 (M). (Last Lecture) Review & Course evaluations. Final review slides: [[File:Final-review-2018.pdf|thumbnail]]
** '''Submit your Teacher's Evaluation''', using either:
** 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]
* Date & Time to be determined: '''Comprehensive Final  Exam'''

Revision as of 07:23, 26 July 2020

Bioinformatics Boot Camp for Ecology & Evolution: Pathogen Evolutionary Genomics
Thursday, Aug 6, 2020, 2 - 3:30pm
Instructors: Dr Weigang Qiu & Ms Saymon Akther
Email: weigang@genectr.hunter.cuny.edu
Lab Website: http://diverge.hunter.cuny.edu/labwiki/
Lyme Disease (Borreliella) CoV Genome Tracker Coronavirus evolutuon
Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)
Haplotype network
Spike protein alignment

Case studies from Qiu Lab

CoV genome data set

  • N=565 SARS-CoV-2 genomes collected during January & February 2020. Data source & acknowledgement GIDAID (Warning: You need to acknowledge GISAID if you reuse the data in any publication)
  • Download file: data file
  • Create a directory, unzip, & un-tar
mkdir QiuAkther
mv cov-camp.tar.gz QiuAkther/
cd QiuAkther
tar -tzf cov-camp.tar.gz # view files
tar -xzf cov-camp.tar.gz # un-zip & un-tar
  • View files
file TCS.jar
ls -lrt # long list, in reverse timeline
less Jan-Feb.mafft # an alignment of 565 CoV2 genomes in FASTA format; "q" to quit
less cov-565strains-617snvs.phy # non-gapped SNV alignment in PHYLIP format
wc hap.txt # geographic origins
head hap.txt
wc group.txt # color assignment
cat group.txt
less cov-565strains.gml # graph file (output)

Bioinformatics Tools & Learning Goals

Tutorial

  • 2-2:30: Introduction on pathogen phylogenomics
  • 2:30-2:45: Demo: sequence manipulation with BpWrapper

<syntaxhighlight lang='bash'> bioseq --man bioseq -n Jan-Feb.mafft bioaln --man bioaln -n -i'fasta' Jan-Feb.mafft bioaln -l -i'fasta' Jan-Feb.mafft bioaln -n -i'phylip' cov-565strains-617snvs.phy bioaln -l -i'phylip' cov-565strains-617snvs.phy FastTree -nt cov-565strains-617snvs.phy > cov.dnd biotree --man biotree -n cov.dnd biotree -l cov.dnd <syntaxhighlight>

  • 2:45-3:00: build haplotype network with TCS

<syntaxhighlight lang='bash'> java -jar -Xmx1g TCS.jar <syntaxhighlight>

  • 3:00-3:15: interactive visualization with BuTCS
  • 3:15-3:30: Q & A
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