An introduction to sitePath

In sitePath: Detection of site fixation in molecular evolution

knitr::opts_chunk$set(
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Introduction

The sitePath package does hierarchical search for fixation events given multiple sequence alignment and phylogenetic tree. These fixation events can be specific to a phylogenetic lineages or shared by multiple lineages. This is achieved by three major steps:

1. Import tree and sequence alignment
2. Resolve phylogenetic lineages
3. Hierarchical search for fixation and parallel mutations

Import data

There're various R packages for parsing phylogenetic tree and multiple sequence alignment files. For now, sitepath accepts phylo object and alignment object. Functions from ggtree and seqinr are able to handle most file formats.

Parse phylogenetic tree

The S3 phylo class is a common data structure for phylogenetic analysis in R. The CRAN package ape provides basic parsing function for reading tree files. The Bioconductor package ggtree provides more comprehensive parsing utilities.

library(sitePath)

tree_file <- system.file("extdata", "ZIKV.newick", package = "sitePath")
tree <- read.tree(tree_file)

It is highly recommended that the file stores a rooted tree as R would consider the tree is rooted by default and re-rooting the tree in R is difficult.

Also, we expect the tree to have no super long branches. A bad example is shown below:

bad_tree <- read.tree(system.file("extdata", "WNV.newick", package = "sitePath"))

ggtree::ggtree(bad_tree)

Parse and match sequence alignment

Most multiple sequence alignment format can be parsed by seqinr. There is a wrapper function for parsing and adding the sequence alignment.

alignment_file <- system.file("extdata", "ZIKV.fasta", package = "sitePath")
tree <- addMSA(tree, alignment_file, "fasta")

Phylogenetic lineages

The names in tree and alignment must be matched. We exploit polymorphism of each site to find the major branches. Before finding putative phylogenetic lineages, there involves a few more steps to evaluate the impact of threshold on result.

The impact of threshold on resolving lineages

In the current version, the resolving function only takes sequence similarity as one single threshold. The impact of threshold depends on the tree topology hence there is no universal choice. The function sneakPeak samples thresholds and calculates the resulting number of paths. The use of this function can be of great help in choosing the threshold.

preassessment <- sneakPeek(tree, makePlot = TRUE)

Choose a threshold

Use the return of the function lineagePath for downstream analysis. The choice of the threshold really depends. You can use the result from sneakPeak as a reference for threshold choosing. Here 0.05 is used as an example.

paths <- lineagePath(preassessment, similarity = 0.05)
paths

You can visualize the result.

plot(paths)

Mutation detection

Now you're ready to find fixation and parallel mutations.

Entropy minimization

The sitesMinEntropy function perform entropy minimization on every site for each lineage. The fixation and parallel mutations can be derived from the function's return value.

minEntropy <- sitesMinEntropy(paths)

Fixation mutations

The hierarchical search is done by fixationSites function. The function detects the site with fixation mutation.

fixations <- fixationSites(minEntropy)
fixations

To get the position of all the resulting sites, allSitesName can be used on the return of fixationSites and also other functions like SNPsites and parallelSites.

allSites <- allSitesName(fixations)
allSites

If you want to retrieve the result of a single site, you can pass the result of fixationSites and the site index to extractSite function. The output is a sitePath object which stores the tip names.

sp <- extractSite(fixations, 139)

It is also possible to retrieve the tips involved in the fixation of the site.

extractTips(fixations, 139)

Use plot on a sitePath object to visualize the fixation mutation of a single site. Alternatively, use plotSingleSite on an fixationSites object with the site specified.

plot(sp)
plotSingleSite(fixations, 139)

To have an overall view of the transition of fixation mutation, use plot on an fixationSites object.

plot(fixations)

Parallel mutation

Parallel mutation can be found by the parallelSites function. There are four ways of defining the parallel mutation: all, exact, pre and post. Here exact is used as an example.

paraSites <- parallelSites(minEntropy, mutMode = "exact")
paraSites

The result of a single site can be visualized by plotSingleSite function.

plotSingleSite(paraSites, 105)

Additional functions

This part is extra and experimental but might be useful when pre-assessing your data. We'll use an example to demonstrate.

Inspect one site

The plotSingleSite function will color the tree according to amino acids if you use the output of lineagePath function.

plotSingleSite(paths, 139)
plotSingleSite(paths, 763)

SNP sites

An SNP site could potentially undergo fixation event. The SNPsites function predicts possible SNP sites and the result could be what you'll expect to be fixation mutation. Also, a tree plot with mutation could be visualized with plotMutSites function.

snps <- SNPsites(tree)
plotMutSites(snps)
plotSingleSite(paths, snps[4])
plotSingleSite(paths, snps[5])

Session info {.unnumbered}

sessionInfo()

sitePath documentation built on April 17, 2021, 6:05 p.m.