add PHYLO units
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-PHYLO-Data_preparation unit.
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#
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# Version: 0.1
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# Version: 1.0
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#
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# Date: 2017 08 28
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# Date: 2017 10. 31
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.0 First 2017 version
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# 0.1 First code copied from 2016 material.
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#
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#
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# TODO:
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#
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#
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# == DO NOT SIMPLY source() THIS FILE! =======================================
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#
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# If there are portions you don't understand, use R's help system, Google for an
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# answer, or ask your instructor. Don't continue if you don't understand what's
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# going on. That's not how it works ...
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# ==============================================================================
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# = 1 ___Section___
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# ==============================================================================
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# PART ONE: Choosing sequences
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# ==============================================================================
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# Start by loading libraries. You already have the packages installed.
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library(Biostrings)
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library(msa)
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library(stringr)
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# What is the latest version of myDB that you have saved?
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list.files(pattern = "myDB.*")
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# ... load it (probably myDB.05.RData - if not, change the code below).
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load("myDB.05.RData")
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# The database contains the ten Mbp1 orthologues from the reference species
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# and the Mbp1 RBM for MYSPE.
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#
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# We will construct a phylogenetic tree from the proteins' APSES domains.
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# You have annotated their ranges as a feature.
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# ==============================================================================
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# Collect APSES domain sequences from your database. The function
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# dbGetFeatureSequence() retrieves the sequence that is annotated for a feature
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# from its start and end coordinates. Try:
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# = 1 Preparations ========================================================
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dbGetFeatureSequence(myDB, "MBP1_SACCE", "APSES fold")
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# Lets put all APSES sequences into a vector:
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APSESnames <- myDB$protein$name[grep("^MBP1_", myDB$protein$name)]
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APSES <- character(length(APSESnames))
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# You need to reload your protein database, including changes that might have
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# been made to the reference files. If you have worked with the prerequiste
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# units, you should have a script named "makeProteinDB.R" that will create the
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# myDB object with a protein and feature database. Ask for advice if not.
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source("makeProteinDB.R")
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for (i in 1:length(APSESnames)) {
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APSES[i] <- dbGetFeatureSequence(myDB, APSESnames[i], "APSES fold")
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# Load packages we need
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if (! require(Biostrings, quietly=TRUE)) {
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if (! exists("biocLite")) {
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source("https://bioconductor.org/biocLite.R")
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}
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biocLite("Biostrings")
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library(Biostrings)
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}
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# Package information:
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# library(help = Biostrings) # basic information
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# browseVignettes("Biostrings") # available vignettes
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# data(package = "Biostrings") # available datasets
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if (! require(msa, quietly=TRUE)) {
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if (! exists("biocLite")) {
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source("https://bioconductor.org/biocLite.R")
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}
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biocLite("msa")
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library(msa)
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}
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# Package information:
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# library(help=msa) # basic information
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# browseVignettes("msa") # available vignettes
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# data(package = "msa") # available datasets
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if (! require(stringr, quietly=TRUE)) {
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install.packages("stringr")
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library(stringr)
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}
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# Package information:
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# library(help=stringr) # basic information
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# browseVignettes("stringr") # available vignettes
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# data(package = "stringr") # available datasets
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# = 1 Fetching sequences
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# myDB contains the ten Mbp1 orthologues from the reference species and the Mbp1
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# RBM for MYSPE. We will construct a phylogenetic tree from the proteins' APSES
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# domains. You have annotated their ranges as a feature. The following code
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# retrieves the sequences from myDB. You have seen similar code in other units.
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sel <- grep("^MBP1_", myDB$protein$name)
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(proNames <- myDB$protein$name[sel])
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(proIDs <- myDB$protein$ID[sel])
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(sel <- myDB$feature$ID[myDB$feature$name == "APSES fold"])
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(fanIDs <- myDB$annotation$ID[myDB$annotation$proteinID %in% proIDs & # %in% !
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myDB$annotation$featureID == sel]) # == !
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# Why?
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APSI <- character(length(fanIDs))
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for (i in seq_along(fanIDs)) {
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sel <- myDB$annotation$ID == fanIDs[i] # get the feature row index
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proID <- myDB$annotation$proteinID[sel] # get its protein ID
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start <- myDB$annotation$start[sel] # get start ...
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end <- myDB$annotation$end[sel] # ... and end
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sel <- myDB$protein$ID == proID # get the protein row index ...
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# ... and the sequence
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APSI[i] <- substring(myDB$protein$sequence[sel], start, end)
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names(APSI)[i] <- (myDB$protein$name[sel])
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}
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# Let's name the rows of our vector with the BiCode part of the protein name.
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# This is important so we can keep track of which sequence is which. We use the
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# gsub() funcion to substitute "" for "MBP1_", thereby deleting this prefix.
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names(APSES) <- gsub("^MBP1_", "", APSESnames)
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# inspect the result: what do you expect? Is this what you expect?
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head(APSES)
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head(APSI)
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# Let's add the E.coli Kila-N domain sequence as an outgroup, for rooting our
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# phylogegetic tree (see the Assignment Course Wiki page for details on the
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# sequence).
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# phylogenetic tree (see the unit's Wiki page for details on the sequence).
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APSES[length(APSES) + 1] <-
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"IDGEIIHLRAKDGYINATSMCRTAGKLLSDYTRLKTTQEFFDELSRDMGIPISELIQSFKGGRPENQGTWVHPDIAINLAQ"
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names(APSES)[length(APSES)] <- "ESCCO"
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APSI <- c(APSI,
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"IDGEIIHLRAKDGYINATSMCRTAGKLLSDYTRLKTTQEFFDELSRDMGIPISELIQSFKGGRPENQGTWVHPDIAINLAQ")
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names(APSI)[length(APSI)] <- "KILA_ESCCO"
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tail(APSI)
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# ==============================================================================
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# PART TWO: Multiple sequence alignment
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# ==============================================================================
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# = 1 Multiple Sequence Alignment
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# This vector of sequences with named elements fulfills the requirements to be
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# imported as a Biostrings object - an AAStringSet - which we need as input for
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# the MSA algorithms in Biostrings.
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#
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APSESSeqSet <- AAStringSet(APSES)
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APSESMsaSet <- msaMuscle(APSESSeqSet, order = "aligned")
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APSESSet <- AAStringSet(APSI)
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APSESMsa <- msaMuscle(APSESSet, order = "aligned")
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# inspect the alignment.
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writeSeqSet(APSESMsaSet, format = "ali")
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writeALN(APSESMsa)
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# What do you think? Is this a good alignment for phylogenetic inference?
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# ==============================================================================
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# PART THREE: reviewing and editing alignments
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# ==============================================================================
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# Head back to the assignment 7 course wiki page and read up on the background
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# = 1 Reviewing and Editing Alignments
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# Head back to the Wiki page for this unit and read up on the background
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# first.
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#
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# Let's mask out all columns that have observations for
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# less than 1/3 of the sequences in the dataset. This
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@ -116,82 +146,55 @@ writeSeqSet(APSESMsaSet, format = "ali")
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# go through the matrix, column by column and decide
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# whether we want to include that column.
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# Step 1. Go through this by hand...
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# = 1.1 Masking workflow
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# get the length of the alignment
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lenAli <- APSESMsaSet@unmasked@ranges@width[1]
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(lenAli <- APSESMsa@unmasked@ranges@width[1])
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# initialize a matrix that can hold all characters
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# individually
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msaMatrix <- matrix(character(nrow(APSESMsaSet) * lenAli),
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msaMatrix <- matrix(character(nrow(APSESMsa) * lenAli),
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ncol = lenAli)
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# assign the correct rownames
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rownames(msaMatrix) <- APSESMsaSet@unmasked@ranges@NAMES
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for (i in 1:nrow(APSESMsaSet)) {
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seq <- as.character(APSESMsaSet@unmasked[i])
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msaMatrix[i, ] <- unlist(strsplit(seq, ""))
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rownames(msaMatrix) <- APSESMsa@unmasked@ranges@NAMES
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for (i in 1:nrow(APSESMsa)) {
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msaMatrix[i, ] <- unlist(strsplit(as.character(APSESMsa@unmasked[i]), ""))
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}
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# inspect the result
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msaMatrix[1:5, ]
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msaMatrix[1:7, 1:14]
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# Now let's make a logical vector with an element
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# for each column that selects which columns should
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# be masked out.
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# Now let's make a logical vector with an element for each column that selects
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# which columns should be masked out.
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# To count the number of elements in a vector, R has
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# the table() function. For example ...
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table(msaMatrix[ , 1])
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table(msaMatrix[ , 10])
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table(msaMatrix[ , 20])
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table(msaMatrix[ , 30])
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# The number of hyphens in a column is easy to count. Consider:
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msaMatrix[ , 20]
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msaMatrix[ , 20] == "-"
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sum(msaMatrix[ , 20] == "-")
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# Since the return value of table() is a named vector, where
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# the name is the element that was counted in each slot,
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# we can simply get the counts for hyphens from the
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# return value of table(). We don't even need to assign
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# the result to an intermediate variable, but we
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# can attach the selection via square brackets,
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# i.e.: ["-"], directly to the function call:
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table(msaMatrix[ , 1])["-"]
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# Thus filling our logical vector is simple:
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# ... to get the number of hyphens. And we can compare
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# whether it is eg. > 4.
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table(msaMatrix[ , 1])["-"] > 4
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# Thus filling our logical vector is really simple:
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# initialize the mask
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colMask <- logical(lenAli)
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# initialize a mask
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colMask <- logical(ncol(msaMatrix))
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# define the threshold for rejecting a column
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limit <- round(nrow(APSESMsaSet) * (2/3))
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limit <- round(nrow(APSESMsa) * (2/3))
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# iterate over all columns, and write TRUE if there are less-or-equal to "limit"
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# hyphens, FALSE if there are more.
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for (i in 1:lenAli) {
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count <- table(msaMatrix[ , i])["-"]
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if (is.na(count)) { # No hyphen
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count <- 0
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}
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colMask[i] <- count <= limit
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for (i in 1:ncol(msaMatrix)) {
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count <- sum(msaMatrix[ , i] == "-")
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colMask[i] <- count <= limit # FALSE if less-or-equal to limit, TRUE if not
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}
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# inspect the mask
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colMask
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# How many positions were masked? R has a simple trick
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# to count the number of TRUE and FALSE in a logical
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# vector. If a logical TRUE or FALSE is converted into
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# a number, it becomes 1 or 0 respectively. If we use
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# the sum() function on the vector, the conversion is
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# done implicitly. Thus ...
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# How many positions were masked?
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sum(colMask)
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# ... gives the number of TRUE elements.
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cat(sprintf("We are masking %4.2f %% of alignment columns.\n",
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100 * (1 - (sum(colMask) / length(colMask)))))
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@ -203,46 +206,22 @@ maskedMatrix <- msaMatrix[ , colMask]
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# check:
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ncol(maskedMatrix)
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# ... then collapse each row back into a sequence ...
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apsMaskedSeq <- character()
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# ... then collapse each row of single characters back into a string ...
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APSESphyloSet <- character()
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for (i in 1:nrow(maskedMatrix)) {
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apsMaskedSeq[i] <- paste(maskedMatrix[i, ], collapse="")
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APSESphyloSet[i] <- paste(maskedMatrix[i, ], collapse="")
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}
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names(apsMaskedSeq) <- rownames(maskedMatrix)
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# ... and read it back into an AAStringSet object
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apsMaskedSet <- AAStringSet(apsMaskedSeq)
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names(APSESphyloSet) <- rownames(maskedMatrix)
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# inspect ...
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writeSeqSet(apsMaskedSet, format = "ali")
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writeALN(APSESphyloSet)
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# As you see, we have removed a three residue insertion from MBP1_NEUCR, and
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# several indels from the KILA_ESCCO outgroup sequence.
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# Step 2. Turn this code into a function...
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# Even though the procedure is simple, doing this more than once is tedious and
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# prone to errors. I have assembled the steps we just went through into a
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# function maskSet() and put it into the utilities.R file, from where it has
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# been loaded when you started this sesssion.
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maskSet
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# Check that the function gives identical results
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# to what we did before by hand:
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identical(apsMaskedSet, maskSet(APSESMsaSet))
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# The result must be TRUE. If it's not TRUE you have
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# an error somewhere.
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# We save the aligned, masked domains to a file in multi-FASTA format.
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writeSeqSet(maskSet(APSESMsaSet), file = "APSES.mfa", format = "mfa")
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# = 1 Tasks
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writeMFA(APSESphyloSet, myCon = "APSESphyloSet.mfa")
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@ -3,46 +3,70 @@
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-PHYLO-Tree_analysis unit.
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#
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# Version: 0.1
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# Version: 1.0
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#
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# Date: 2017 08 28
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# Date: 2017 10. 31
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.0 First 2017 version
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# 0.1 First code copied from 2016 material.
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#
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#
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# TODO:
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#
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#
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# == DO NOT SIMPLY source() THIS FILE! =======================================
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#
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# If there are portions you don't understand, use R's help system, Google for an
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# answer, or ask your instructor. Don't continue if you don't understand what's
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# going on. That's not how it works ...
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#
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# ==============================================================================
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# = 1 ___Section___
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# ==============================================================================
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# PART FIVE: Tree analysis
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# ==============================================================================
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# A Entrez restriction command
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cat(paste(paste(c(myDB$taxonomy$ID, "83333"), "[taxid]", sep=""), collapse=" OR "))
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if (!require(Rphylip, quietly=TRUE)) {
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install.packages("Rphylip")
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library(Rphylip)
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}
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# Package information:
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# library(help = Rphylip) # basic information
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# browseVignettes("Rphylip") # available vignettes
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# data(package = "Rphylip") # available datasets
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# The Common Tree from NCBI
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# Download the EDITED phyliptree.phy
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commonTree <- read.tree("phyliptree.phy")
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# Read the species tree that you have created at the phyloT Website:
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fungiTree <- read.tree("fungiTree.txt")
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plot(fungiTree)
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# The tree produced by phyloT contains full length species names, but it would
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# be more convenient if it had bicodes instead.
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str(fungiTree)
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# The species names are in a vector $tip.label of this list. We can use bicode()
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# to shorten them - but note that they have underscores as word separators. Thus
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# we will use gsub("-", " ", ...) to replace the underscores with spaces.
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for (i in seq_along(fungiTree$tip.label)) {
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fungiTree$tip.label[i] <- biCode(gsub("_", " ", fungiTree$tip.label[i]))
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}
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# Plot the tree
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plot(commonTree, cex=1.0, root.edge=TRUE, no.margin=TRUE)
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nodelabels(text=commonTree$node.label, cex=0.6, adj=0.2, bg="#D4F2DA")
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plot(fungiTree, cex=1.0, root.edge=TRUE, no.margin=TRUE)
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nodelabels(text=orgTree$node.label, cex=0.6, adj=0.2, bg="#D4F2DA")
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# Note that you can use the arrow buttons in the menu above the plot to scroll
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# back to plots you have created earlier - so you can reference back to the
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# species tree.
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# === Visualizing your tree ====================================================
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# = 1 Tree Analysis
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# 1.1 Visualizing your tree
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# The trees that are produced by Rphylip are stored as an object of class
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# "phylo". This is a class for phylogenetic trees that is widely used in the
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# community, practically all R phylogenetics packages will options to read and
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@ -51,21 +75,25 @@ nodelabels(text=commonTree$node.label, cex=0.6, adj=0.2, bg="#D4F2DA")
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# trees in Newick format and visualize them elsewhere.
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# The "phylo" class object is one of R's "S3" objects and methods to plot and
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# print it have been added to the system. You can simply call plot(<your-tree>)
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# and R knows what to do with <your-tree> and how to plot it. The underlying
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# function is plot.phylo(), and documentation for its many options can by found
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# by typing:
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# print it have been defined with the Rphylip package, and the package ape that
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# Rphylip has loaded. You can simply call plot(<your-tree>) and R knows what to
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# do with <your-tree> and how to plot it. The underlying function is
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# plot.phylo(), and documentation for its many options can by found by typing:
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?plot.phylo
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# We load the APSES sequence tree that you produced in the
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||||
# BIN-PHYLO-Tree_building unit:
|
||||
load(file = "APSEStreeRproml.RData")
|
||||
|
||||
plot(apsTree) # default type is "phylogram"
|
||||
plot(apsTree, type="unrooted")
|
||||
plot(apsTree, type="fan", no.margin = TRUE)
|
||||
|
||||
# rescale to show all of the labels:
|
||||
# record the current plot parameters ...
|
||||
tmp <- plot(apsTree, type="fan", no.margin = TRUE, plot=FALSE)
|
||||
# ... and adjust the plot limits for a new plot
|
||||
# record the current plot parameters by assigning them to a variable ...
|
||||
(tmp <- plot(apsTree, type="fan", no.margin = TRUE, plot=FALSE))
|
||||
# ... and adjust the plot limits for a new plot:
|
||||
plot(apsTree,
|
||||
type="fan",
|
||||
x.lim = tmp$x.lim * 1.8,
|
||||
@ -94,7 +122,7 @@ Ntip(apsTree)
|
||||
# Finally, write the tree to console in Newick format
|
||||
write.tree(apsTree)
|
||||
|
||||
# === Rooting Trees ============================================================
|
||||
# = 1.1 Rooting Trees
|
||||
|
||||
# In order to analyse the tree, it is helpful to root it first and reorder its
|
||||
# clades. Contrary to documentation, Rproml() returns an unrooted tree.
|
||||
@ -110,9 +138,9 @@ plot(apsTree)
|
||||
nodelabels(cex=0.5, frame="circle")
|
||||
tiplabels(cex=0.5, frame="rect")
|
||||
|
||||
# The outgroup of the tree is tip "8" in my sample tree, it may be a different
|
||||
# The outgroup of the tree is tip "11" in my sample tree, it may be a different
|
||||
# number in yours. Substitute the correct node number below for "outgroup".
|
||||
apsTree <- root(apsTree, outgroup = 8, resolve.root = TRUE)
|
||||
apsTree <- root(apsTree, outgroup = 11, resolve.root = TRUE)
|
||||
plot(apsTree)
|
||||
is.rooted(apsTree)
|
||||
|
||||
@ -140,24 +168,24 @@ plot(apsTree, cex=0.7, root.edge=TRUE)
|
||||
nodelabels(text="MRCA", node=12, cex=0.5, adj=0.8, bg="#ff8866")
|
||||
|
||||
|
||||
# === Rotating Clades ==========================================================
|
||||
# = 1.1 Rotating Clades
|
||||
|
||||
# To interpret the tree, it is useful to rotate the clades so that they appear
|
||||
# in the order expected from the cladogram of species.
|
||||
|
||||
# We can either rotate around individual internal nodes:
|
||||
# We can either rotate around individual internal nodes ...
|
||||
layout(matrix(1:2, 1, 2))
|
||||
plot(apsTree, no.margin=TRUE, root.edge=TRUE)
|
||||
nodelabels(node=17, cex=0.7, bg="#ff8866")
|
||||
plot(rotate(apsTree, node=17), no.margin=TRUE, root.edge=TRUE)
|
||||
nodelabels(node=17, cex=0.7, bg="#88ff66")
|
||||
# Note that the species at the bottom of the clade descending from node
|
||||
# 17 is now plotted at the top.
|
||||
layout(matrix(1), widths=1.0, heights=1.0)
|
||||
|
||||
# ... or we can plot the tree so it corresponds as well as possible to a
|
||||
# predefined tip ordering. Here we use the ordering that NCBI Global Tree
|
||||
# returns for the reference species - we have used it above to make the vector
|
||||
# apsMbp1Names. You inserted your MYSPE name into that vector - but you should
|
||||
# move it to its correct position in the cladogram.
|
||||
# predefined tip ordering. Here we use the ordering that phyloT has returned
|
||||
# for the species tree.
|
||||
|
||||
# (Nb. we need to reverse the ordering for the plot. This is why we use the
|
||||
# expression [nOrg:1] below instead of using the vector directly.)
|
||||
@ -165,9 +193,9 @@ layout(matrix(1), widths=1.0, heights=1.0)
|
||||
nOrg <- length(apsTree$tip.label)
|
||||
|
||||
layout(matrix(1:2, 1, 2))
|
||||
plot(commonTree,
|
||||
plot(fungiTree,
|
||||
no.margin=TRUE, root.edge=TRUE)
|
||||
nodelabels(text=commonTree$node.label, cex=0.5, adj=0.2, bg="#D4F2DA")
|
||||
nodelabels(text=fungiTree$node.label, cex=0.5, adj=0.2, bg="#D4F2DA")
|
||||
|
||||
plot(rotateConstr(apsTree, apsTree$tip.label[nOrg:1]),
|
||||
no.margin=TRUE, root.edge=TRUE)
|
||||
@ -175,16 +203,9 @@ add.scale.bar(length=0.5)
|
||||
layout(matrix(1), widths=1.0, heights=1.0)
|
||||
|
||||
# Study the two trees and consider their similarities and differences. What do
|
||||
# you expect? What do you find?
|
||||
#
|
||||
|
||||
# Print the two trees on one sheet of paper, write your name and student number,
|
||||
# and bring it to class as your deliverable for this assignment. Also write two
|
||||
# or three sentences about if/how the gene tree matches the species tree or not.
|
||||
|
||||
|
||||
# = 1 Tasks
|
||||
|
||||
# you expect? What do you find? Note that this is not a "mixed" gene tree yet,
|
||||
# since it contains only a single gene for the species we considered. All of the
|
||||
# branch points in this tree are speciation events.
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,33 +1,33 @@
|
||||
# ___ID___ .R
|
||||
# BIN-PHYLO-Tree_building.R
|
||||
#
|
||||
# Purpose: A Bioinformatics Course:
|
||||
# R code accompanying the ___ID___ unit.
|
||||
# R code accompanying the BIN-PHYLO-Tree_building unit.
|
||||
#
|
||||
# Version: 0.1
|
||||
# Version: 1.0
|
||||
#
|
||||
# Date: 2017 08 28
|
||||
# Date: 2017 10. 31
|
||||
# Author: Boris Steipe (boris.steipe@utoronto.ca)
|
||||
#
|
||||
# Versions:
|
||||
# 1.0 First 2017 version
|
||||
# 0.1 First code copied from 2016 material.
|
||||
|
||||
#
|
||||
#
|
||||
# TODO:
|
||||
#
|
||||
#
|
||||
# == DO NOT SIMPLY source() THIS FILE! =======================================
|
||||
|
||||
#
|
||||
# If there are portions you don't understand, use R's help system, Google for an
|
||||
# answer, or ask your instructor. Don't continue if you don't understand what's
|
||||
# going on. That's not how it works ...
|
||||
|
||||
#
|
||||
# ==============================================================================
|
||||
|
||||
# = 1 ___Section___
|
||||
|
||||
# ==============================================================================
|
||||
# PART FOUR: Calculating trees
|
||||
# ==============================================================================
|
||||
|
||||
# = 1 Calculating Trees
|
||||
|
||||
|
||||
# Follow the instructions found at phylip's home on the Web to install. If you
|
||||
# are on a Windows computer, take note of the installation directory.
|
||||
@ -82,16 +82,15 @@ if (!require(Rphylip, quietly=TRUE)) {
|
||||
# Confirm that the settings are right.
|
||||
PROMLPATH # returns the path
|
||||
list.dirs(PROMLPATH) # returns the directories in that path
|
||||
list.files(PROMLPATH) # lists the files
|
||||
list.files(PROMLPATH) # lists the files [1] "proml" "proml.command"
|
||||
|
||||
# If "proml" is NOT among the files that the last command returns, you
|
||||
# can't continue.
|
||||
# can't continue. Ask on the mailing list for advice.
|
||||
|
||||
# Now read the mfa file you have saved, as a "proseq" object with the
|
||||
# read.protein() function of the RPhylip package:
|
||||
# Now read the mfa file you have saved in the BIB-PHYLO-Data_preparation unit,
|
||||
# as a "proseq" object with the read.protein() function of the RPhylip package:
|
||||
|
||||
apsIn <- read.protein("APSES.mfa")
|
||||
apsIn <- read.protein("~/Desktop/APSES_HISCA.mfa")
|
||||
apsIn <- read.protein("APSESphyloSet.mfa")
|
||||
|
||||
# ... and you are ready to build a tree.
|
||||
|
||||
@ -103,15 +102,14 @@ apsIn <- read.protein("~/Desktop/APSES_HISCA.mfa")
|
||||
|
||||
apsTree <- Rproml(apsIn, path=PROMLPATH)
|
||||
|
||||
|
||||
|
||||
# A quick first look:
|
||||
|
||||
plot(apsTree)
|
||||
|
||||
# save your tree:
|
||||
save(apsTree, file = "APSEStreeRproml.RData")
|
||||
|
||||
|
||||
# = 1 Tasks
|
||||
# If this did not work, ask for advice.
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user