BIN-PHYLO-Tree building

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Building Phylogenetic Trees

(Calculating phylogenetic trees; tree visualization)


 


Abstract:

Building phylogenetic trees in theory - and with phylip in R.

Objectives:
This unit will ...

  • ... introduce the concepts and algorithms used to build phylogenetic trees;
  • ... teach how to compute a maximum likelihood tree with the PHYLIP proml program in R;

Outcomes:
After working through this unit you ...

  • ... are familar with concepts and algorithms used to build phylogenetic trees;
  • ... have computed a phylogenetic tree of Mbp1 orthologue APSES domains with the PHYLIP proml program via the RPhylip:: package.

Deliverables:

  • Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
  • Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
  • Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.

    • Prerequisites:
    You need the following preparation before beginning this unit. If you are not familiar with this material from courses you took previously, you need to prepare yourself from other information sources:

    • Evolution: Theory of evolution; variation, neutral drift and selection.

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


     



     



     


    Evaluation

    Evaluation: NA

    This unit is not evaluated for course marks.

    Contents

    Task:

    Kapli et al. (2020) Phylogenetic tree building in the genomic age. Nat Rev Genet 21:428-444. (pmid: 32424311)

    [ PubMed ] [ DOI ] Knowing phylogenetic relationships among species is fundamental for many studies in biology. An accurate phylogenetic tree underpins our understanding of the major transitions in evolution, such as the emergence of new body plans or metabolism, and is key to inferring the origin of new genes, detecting molecular adaptation, understanding morphological character evolution and reconstructing demographic changes in recently diverged species. Although data are ever more plentiful and powerful analysis methods are available, there remain many challenges to reliable tree building. Here, we discuss the major steps of phylogenetic analysis, including identification of orthologous genes or proteins, multiple sequence alignment, and choice of substitution models and inference methodologies. Understanding the different sources of errors and the strategies to mitigate them is essential for assembling an accurate tree of life.


     

    The result of the tree construction is a decision about the most likely evolutionary relationships. Fundamentally, tree-construction programs decide which sequences had common ancestors.

    "Distance based" and "Parsimony based" methods are fast, but less acurate.

    Distance based phylogeny programs start by using sequence comparisons to estimate evolutionary distances:

    • they apply a model of evolution such as a mutation data matrix, to calculate a score for each pair of sequences,
    • this score is stored in a "distance matrix" ...
    • ... and used to estimate a tree that groups sequences with close relationships together. (e.g. by using an NJ, Neigbor Joining, algorithm).

    They are fast, can work on large numbers of sequences, but are less accurate if genes evolve at different rates.


    Parsimony based phylogeny programs build a tree that minimizes the number of mutation events that are required to get from a common ancestral sequence to all observed sequences. They take all columns into account, not just a single number per sequence pair, as the Distance Methods do. For closely related sequences they work very well, but they construct inaccurate trees when they can't make good estimates for the required number of sequence changes.


    "Maximum Likelihood" and "Bayesian" methods are accurate, but can take up very significant computational resources.

    ML, or Maximum Likelihood methods attempt to find the tree for which the observed sequences would be the most likely under a particular evolutionary model. They are based on a rigorous statistical framework and yield the most robust results. But they are also quite compute intensive and a tree of the size that we are building in this assignment is a challenge for the resources of common workstation (runs about an hour on my computer). If the problem is too large, one may split a large problem into smaller, obvious subtrees (e.g. analysing orthologues as a group, only including a few paralogues for comparison) and then merge the smaller trees; this way even very large problems can become tractable.

    ML methods suffer less from "long-branch attraction" - the phenomenon that weakly similar sequences can be grouped inappropriately close together in a tree due to spuriously shared differences.


    Bayesian methods don't estimate the tree that gives the highest likelihood for the observed data, but find the most probable tree, given the data that has been observed. If you think this sounds conceptually similar to ML methods, then you are not wrong. However, the approaches employ very different algorithms. And Bayesian methods need a "prior" on trees before observation.


    Calculating trees

     

    In this section we perform the actual phylogenetic calculation.


     

    Task:

    • Download the PHYLIP suite of programs from the Phylip homepage and install it on your computer.


     

    Task:

     
    • Open RStudio and load the ABC-units R project. If you have loaded it before, choose File → Recent projects → ABC-Units. If you have not loaded it before, follow the instructions in the RPR-Introduction unit.
    • Choose Tools → Version Control → Pull Branches to fetch the most recent version of the project from its GitHub repository with all changes and bug fixes included.
    • Type init() if requested.
    • Open the file BIN-PHYLO-Tree_building.R and follow the instructions.


     

    Note: take care that you understand all of the code in the script. Evaluation in this course is cumulative and you may be asked to explain any part of code.


     


     


    Further reading, links and resources

    Tuimala, Jarno (2006) A primer to phylogenetic analysis using the PHYLIP package.  
    (pmid: None) [ Source URL ] The purpose of this tutorial is to demonstrate how to use PHYLIP, a collection of phylogenetic analysis software, and some of the options that are available. This tutorial is not intended to be a course in phylogenetics, although some phylogenetic concepts will be discussed briefly. There are other books available which cover the theoretical sides of the phylogenetic analysis, but the actual data analysis work is less well covered. Here we will mostly deal with molecular sequence data analysis in the current PHYLIP version 3.66.

    Notes


     


    About ...
     
    Author:

    Boris Steipe <boris.steipe@utoronto.ca>

    Created:

    2017-08-05

    Modified:

    2020-09-25

    Version:

    1.1

    Version history:

    • 1.1 2020 Maintenance
    • 1.0 First live version.
    • 0.1 First stub

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