ABC-INT-Phylogeny

Integrator Unit: Phylogeny

(Integrator unit: calculate and analyse a phylogenetic tree)

 

Abstract:

This page integrates material from the learning units for working with multiple sequence alignments, and building and analysing phylogenetic trees, in a task for evaluation.

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Deliverables:

Integrator unit: Deliverables can be submitted for course marks. See below for details.

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Prerequisites:
This unit builds on material covered in the following prerequisite units:

• BIN-PHYLO-Tree_analysis (Analysing Phylogenetic Trees)

 

 

 

Evaluation

This "Integrator Unit" should be submitted for evaluation for a maximum of 13 marks if one of the written deliverables is chosen, resp. 24 marks if you choose this for your oral test.

Please note the evaluation types that are available as options for this unit.

Be mindful of the Marking rubrics.

If this is submitted for your oral test, please read the Oral test instructions before you begin.

If your submission includes R code, please read the Code submission instructions before you begin.

Once you have chosen an option ...

1. Create a new page on the student Wiki as a subpage of your User Page.
2. Put all of your writing to submit on this one page.
3. When you are done with everything, go to the Quercus Assignments page and open the appropriate Integrator Unit assignment. Paste the URL of your Wiki page into the form, and click on Submit Assignment.

Do not change your Wiki page after you have submitted your assignment, until it has been graded.

 

Interview option

Identify a laboratory whose work includes constructing and evaluating phylogenetic trees. Get in touch with the PI, a postdoc or senior graduate student in the laboratory and interview them by eMail. Make sure they understand that this is a for-credit assignment in a course you are taking. Devise meaningful questions to find out:

• why this work is important;
• what methods they employ;
• in particular, how they define reference species, create alignments, compute phylogenetic trees, estimate significance and prepare figures for publication (get technical on that point: you need to report on software and key parameters);
• what they have recently learned;
• what the major challenges, current discussions, or controversies in their field are.
• write up your interview on a subpage of your User page of the Student Wiki;
• add background information that may be required to understand the methodology (assume the level of background knowledge appropriate for a student in this course);
• make sure that you have included important literature references.
• Follow up questions if additional clarification is needed.

• When you are done, submit the link to your page via Quercus as described above.

 

Literature research option

Navigate to the Phylogeny Literature Research Topics page on the Student Wiki.

• Pick a topic and enter your name in the table to claim it.
• Write a report on your research. Note: this is not a review, but a report. Think of a "whitepaper", not a publication. Write to a specialist technical audience and be specific to provide actionable information.
• write your report on a subpage of your User page of the Student Wiki;
• make sure that you have included all references in your report, and citations in an appropriate reference section. Use the {{#pmid:0000000}} template. References must be to the page where the information is found, not merely to a paper as a whole (where appropriate).

• When you are done, submit the link to your page via Quercus as described above.

 

Oral test option

• Work through the tasks described below. Remember to document your work in your journal, but there is no need to format this specially as a report.
• Part of your task will involve writing R code; refer to the Code submission instructions and link to your page from your Journal.
• Note that the work must be completed before your actual test date.

 

R code option

• Work through the tasks described in the scenario and develop code as required.
• Put your code and other documentation on a subpage of your User page on the Student Wiki;
• When you are done, submit the link to your page via Quercus as described above.

Contents

For the Report Option ...

Choose one of the two tasks below:

 

Does masking improve the tree?

 

Task:

1. Produce a phylogenetic tree from full-length Mbp1 orthologues for the reference species and MYSPE. Do not apply masking.
2. Produce a second phylogenetic tree from full-length Mbp1 orthologues for the reference species and MYSPE. Apply masking to delete all columns that have more then 2/3 gap-characters.
3. Determine which tree is "more correct" by calculating tree distances to the species tree.
4. Report your findings.

 

Does adding characters improve the tree?

 

Task:

1. Produce a phylogenetic tree only from APSES domains of Mbp1 orthologues for the reference species and MYSPE. Do not apply masking.
2. Produce a second phylogenetic tree from full-length Mbp1 orthologues for the reference species and MYSPE. Again, do not apply masking.
3. Determine which tree is "more correct" by calculating tree distances to the species tree.
4. Report your findings.

 

For the Oral Test Option ...

Interpret the full APSES tree.

 

Task:

• Produce a phylogenetic tree from APSES domains of all proteins in myDB. This includes all APSES domain proteins from MYSPE that you have found with PSI-BLAST. (Caution: the proml program may take quite long to compute this tree. Several hours.) You will find this bit of code useful to get you started:

library(msa)

# Align all sequences in the database + KILA_ESSCO
mySeq <- myDB$protein$sequence
names(mySeq) <- myDB$protein$name
mySeq <- c(mySeq,
           "IDGEIIHLRAKDGYINATSMCRTAGKLLSDYTRLKTTQEFFDELSRDMGIPISELIQSFKGGRPENQGTWVHPDIAINLAQ")
names(mySeq)[length(mySeq)] <- "KILA_ESCCO"

mySeqMSA <- msaClustalOmega(AAStringSet(mySeq)) # too many sequences for MUSCLE


# get the sequence of the SACCE APSES domain
sel <- myDB$protein$name == "MBP1_SACCE"
proID <- myDB$protein$ID[sel]

sel <- myDB$feature$ID[myDB$feature$name == "APSES fold"]
fanID <- myDB$annotation$ID[myDB$annotation$proteinID == proID &
                            myDB$annotation$featureID == sel]
start <- myDB$annotation$start[fanID]
end   <- myDB$annotation$end[fanID]

SACCEapses <- substring(myDB$protein$sequence[proID], start, end)

# extract the APSES domains from the MSA
APSESmsa <- fetchMSAmotif(mySeqMSA, SACCEapses)

# Produce the phylogenetic tree ...

• Interpret the tree with two objectives.

• (A) how many APSES domain proteins did the last common ancestor (LCA) of all fungi have?
• (B) what is the evolutionary history of the APSES domain proteins in MYSPE? Were genes lost? Did duplications occur?

• Annotate your tree. Be prepared to screen-share your annotation document during the test and to discuss your interpretation.

 

For the R-code option ...

Does adding species improve the tree?

 

Task:

• Produce a MSA from APSES domains of all proteins in myDB.

library(msa)

# Align all sequences in the database + KILA_ESSCO
mySeq <- myDB$protein$sequence
names(mySeq) <- myDB$protein$name
mySeq <- c(mySeq,
           "IDGEIIHLRAKDGYINATSMCRTAGKLLSDYTRLKTTQEFFDELSRDMGIPISELIQSFKGGRPENQGTWVHPDIAINLAQ")
names(mySeq)[length(mySeq)] <- "KILA_ESCCO"

mySeqMSA <- msaClustalOmega(AAStringSet(mySeq)) # too many sequences for MUSCLE


# get the sequence of the SACCE APSES domain
sel <- myDB$protein$name == "MBP1_SACCE"
proID <- myDB$protein$ID[sel]

sel <- myDB$feature$ID[myDB$feature$name == "APSES fold"]
fanID <- myDB$annotation$ID[myDB$annotation$proteinID == proID &
                            myDB$annotation$featureID == sel]
start <- myDB$annotation$start[fanID]
end   <- myDB$annotation$end[fanID]

SACCEapses <- substring(myDB$protein$sequence[proID], start, end)

# extract the APSES domains from the MSA
APSESmsa <- fetchMSAmotif(mySeqMSA, SACCEapses)

• Write an R-script that does the following:
  • pick ten random sequences plus the Mbp1 orthologues plus KILA_ESCCO
  • remove all other sequences from the alignment
  • mask all columns that have more then 80% gap characters
  • produce a phylogenetic tree from this input data
  • drop all tips from your tree that are not Mbp1 orthologues and not KILA_ESCCO. This code will be useful:

# assuming your new tree is called "allApsTree"
sel <- ! (allApsTree$tip.label %in% fungiTree$tip.label)
newTree <- drop.tip(allApsTree, allApsTree$tip.label[sel])

• Is this tree more similar to fungiTree than apsTree was?
• Submit your script, significant data, and the results. Interpret the outcome.

 

Further reading, links and resources

Notes

 

About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2020-10-01

Version:

1.2

Version history:

• 1.2 2020 Updates
• 1.1 Corrected posted marks, which were not consistent with the description in the syllabus.
• 1.0 First live version
• 0.1 First stub

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