In wuaipinglab/sitePath: Phylogeny-based sequence clustering with site polymorphism

knitr::opts_chunk$set(
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Introduction

The sitePath package is made for the high-throughput identification of fixed substitutions and parallel mutations in viruses from a single phylogenetic tree. This is achieved by three major steps:

1. Clustering phylogenetic terminals
2. Identifying phylogenetic pathways
3. Finding fixed and parallel mutations

Clustering phylogenetic terminals

The firs step is to import 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.

Import tree file

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 treeio 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) + ggplot2::ggtitle("Do not use a tree like this")

Import sequence alignment file

Most multiple sequence alignment format can be parsed by seqinr. There is a wrapper function for parsing and adding the sequence alignment. Set "cl.cores" in options to the number of cores you want to use for multiprocessing.

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

options(list("cl.cores" = 1)) # Set this bigger than 1 to use multiprocessing

paths <- addMSA(tree, msaPath = alignment_file, msaFormat = "fasta")

Clustering using site polymorphism

The addMSA function will match the sequence names in tree and alignment. Also, the function uses polymorphism of each site to cluster sequences for identifying phylogenetic pathways.

Identifying phylogenetic pathways

After importing the tree and sequence file, sitePath is ready to identify phylogenetic pathways.

The impact of threshold on resolving lineages

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 help choose the threshold.

preassessment <- sneakPeek(paths, makePlot = TRUE)

Choose a threshold

The default threshold is the first 'stable' value to produce the same number of phylogenetic pathways. You can directly use the return of addMSA if you want the default or choose other threshold by using function lineagePath. The choice of the threshold really depends. Here 18 is used as an example.

paths <- lineagePath(preassessment, 18)
paths

You can visualize the result.

plot(paths)

Finding fixed and parallel mutations

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:

plot(fixations)

Parallel mutations

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, minSNP = 1, mutMode = "exact")
paraSites

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

plotSingleSite(paraSites, 105)

To have an overall view of the parallel mutations:

plot(paraSites)

Miscellaneous

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(paths)
plotMutSites(snps)
plotSingleSite(paths, snps[4])
plotSingleSite(paths, snps[5])

Session info {.unnumbered}

sessionInfo()

wuaipinglab/sitePath documentation built on Sept. 26, 2022, 10:16 p.m.