Phylogenetics Tutorial 1: 1. Overview 2. Installation 3. Data 4. - - PowerPoint PPT Presentation

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Phylogenetics Tutorial 1: 1. Overview 2. Installation 3. Data 4. - - PowerPoint PPT Presentation

May 23, 2023 103 likes •584 views

Finlay Maguire Making Phylogenies Faculty of Computer Science Phylogenetics Tutorial 1: 1. Overview 2. Installation 3. Data 4. Multiple Sequence Alignemnt 5. Trimming 6. Approximate ML Tree 7. Maximum-Likelihood Tree 8. Phylogenomics 1

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Phylogenetics Tutorial 1:

Making Phylogenies

Finlay Maguire

Faculty of Computer Science

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Table of contents

  • 1. Overview
  • 2. Installation
  • 3. Data
  • 4. Multiple Sequence Alignemnt
  • 5. Trimming
  • 6. Approximate ML Tree
  • 7. Maximum-Likelihood Tree
  • 8. Phylogenomics

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Overview

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Protein Phylogeny Aims

  • Get a protein
  • Using pairwise alignment to find potential homologs
  • Perform a multiple sequence alignment
  • Trim the alignment
  • Infer a NJ distance phylogeny
  • Infer an approximate Maximum Likelihood phylogeny
  • Infer an accurate Maximum Likelihood phylogeny
  • Compare the trees

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Core Genome Phylogeny

  • Get genomes
  • Find core genome
  • Extract SNPs
  • Infer a Maximum Likelihood phylogeny
  • Visualise Phylogeny

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Requirements

  • mafft
  • trimal
  • aliview
  • FastTree2
  • iqtree
  • FigTree
  • prokka
  • roary
  • snp-sites

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Installation

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miniconda

If you don’t have miniconda https://docs.conda.io/en/latest/miniconda.html

conda create -n phylo -c bioconda mafft trimal prokka fasttree iqtree roary snp-sites

conda activate phylo

  • r if older miniconda version:

source activate phylo

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Other tools

Unfortunately, not everything is in bioconda:

  • AliView

https://github.com/AliView/AliView/releases

  • FigTree

https://github.com/rambaut/figtree/releases

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Data

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Starting Sequence

Figure 1: High-quality protein reference database: swiss-prot http://www.uniprot.org

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Starting Sequence

Figure 2: Choose ‘Gene Ontology’ and ‘biological process’

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Starting Sequence

Figure 3: Go down to ‘detoxification’ and expand

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Starting Sequence

Figure 4: Select ‘8 results’ next to ‘detoxification of arsenic’

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Using BLAST to find related sequences

Figure 5: Select the C. elegans sequence and BLAST

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Using BLAST to find related sequences

Figure 6: Wait...

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Using BLAST to find related sequences

Figure 7: Download 10 sequences across a range of similarity

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Multiple Sequence Alignemnt

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MAFFT

mafft-linsi arsenic.faa > arsenic.afa

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Trimming

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Inspecting the alignment

java -jar aliview.jar

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TrimAL

trimal -nogaps -in arsenic.afa -out arsenic_nogaps.mask

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TrimAL

trimal -automated1 -in arsenic.afa -out arsenic_auto.mask

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Compare Trimming

java -jar aliview.jar

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Approximate ML Tree

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FastTree

FastTree -lg arsenic_auto.mask > arsenic_dist.tree

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Inspect

FigTree

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Maximum-Likelihood Tree

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IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

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IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

21

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IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

21

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IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

21

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IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

21

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IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

21

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SLIDE 35

IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

21

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SLIDE 36

IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

21

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IQ-Tree

  • Generate 100 parsimony trees
  • Optimise all 100 with lazy SPR moves
  • Collect resulting unique topologies and optimise branch lengths
  • Select top 20 by likelihood
  • Perform hill-climbing NNI (stochastic followed by hill-climbing)
  • n each and optimise
  • Retain top 5 topologies as candidate trees
  • Randomly perturb candidates (stochastic NNI) and optimise

(hill-climbing)

  • If new tree is better than top candidate, replace
  • If top candidate doesn’t change after 100 random perturbations

then output.

21

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Running IQ-Tree

iqtree -mset LG,JTT,WAG -s arsenic_auto.mask

Note: IQTree does output a neighbour joining distance tree too (.bionj).

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Inspect

FigTree

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Phylogenomics

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Get genomes

Download the 6 listeria genomes wget finlaymagui.re/assets/listeria_genomes.tar.gz tar xvf listeria_genomes.tar.gz

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Annotate genomes

For genome GCA000008258:

prokka --kingdom Bacteria --outdir prokka_GCA_000008285

  • -genus Listeria --locustag GCA_000008285

GCA_000008285.1_ASM828v1_genomic.fna

Repeat for all genomes

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Find shared parts

mkdir annotations cp */*.gff annotations roary -f core_genome -e -n -v annotations/*.gff

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Extract SNPs

snp-sites -o listeria_snps.fna core_genome/core_gene_alignment.aln

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Infer ML Phylogeny

iqtree -mset GTR -s listeria_snps.fna

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Visualise Tree

Figure 8: Roary Tutorial

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Questions?

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