235 lines
7.3 KiB
R
235 lines
7.3 KiB
R
# BIN-PHYLO-Data_preparation.R
|
|
#
|
|
# Purpose: A Bioinformatics Course:
|
|
# R code accompanying the BIN-PHYLO-Data_preparation unit.
|
|
#
|
|
# Version: 1.0
|
|
#
|
|
# 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 ...
|
|
#
|
|
# ==============================================================================
|
|
|
|
|
|
#TOC> ==========================================================================
|
|
#TOC>
|
|
#TOC> Section Title Line
|
|
#TOC> ---------------------------------------------------
|
|
#TOC> 1 Preparations 41
|
|
#TOC> 2 Fetching sequences 78
|
|
#TOC> 3 Multiple Sequence Alignment 119
|
|
#TOC> 4 Reviewing and Editing Alignments 138
|
|
#TOC> 4.1 Masking workflow 154
|
|
#TOC>
|
|
#TOC> ==========================================================================
|
|
|
|
|
|
# = 1 Preparations ========================================================
|
|
|
|
|
|
# You need to reload your protein database, including changes that might have
|
|
# been made to the reference files. If you have worked with the prerequiste
|
|
# units, you should have a script named "makeProteinDB.R" that will create the
|
|
# myDB object with a protein and feature database. Ask for advice if not.
|
|
source("makeProteinDB.R")
|
|
|
|
# Load packages we need
|
|
|
|
if (! require(Biostrings, quietly=TRUE)) {
|
|
if (! exists("biocLite")) {
|
|
source("https://bioconductor.org/biocLite.R")
|
|
}
|
|
biocLite("Biostrings")
|
|
library(Biostrings)
|
|
}
|
|
# Package information:
|
|
# library(help = Biostrings) # basic information
|
|
# browseVignettes("Biostrings") # available vignettes
|
|
# data(package = "Biostrings") # available datasets
|
|
|
|
|
|
if (! require(msa, quietly=TRUE)) {
|
|
if (! exists("biocLite")) {
|
|
source("https://bioconductor.org/biocLite.R")
|
|
}
|
|
biocLite("msa")
|
|
library(msa)
|
|
}
|
|
# Package information:
|
|
# library(help = msa) # basic information
|
|
# browseVignettes("msa") # available vignettes
|
|
# data(package = "msa") # available datasets
|
|
|
|
|
|
# = 2 Fetching sequences ==================================================
|
|
|
|
|
|
# myDB contains the ten Mbp1 orthologues from the reference species and the Mbp1
|
|
# RBM for MYSPE. We will construct a phylogenetic tree from the proteins' APSES
|
|
# domains. You have annotated their ranges as a feature. The following code
|
|
# retrieves the sequences from myDB. You have seen similar code in other units.
|
|
|
|
sel <- grep("^MBP1_", myDB$protein$name)
|
|
(proNames <- myDB$protein$name[sel])
|
|
(proIDs <- myDB$protein$ID[sel])
|
|
|
|
(sel <- myDB$feature$ID[myDB$feature$name == "APSES fold"])
|
|
(fanIDs <- myDB$annotation$ID[myDB$annotation$proteinID %in% proIDs & # %in% !
|
|
myDB$annotation$featureID == sel]) # == !
|
|
# Why?
|
|
APSI <- character(length(fanIDs))
|
|
|
|
for (i in seq_along(fanIDs)) {
|
|
sel <- myDB$annotation$ID == fanIDs[i] # get the feature row index
|
|
proID <- myDB$annotation$proteinID[sel] # get its protein ID
|
|
start <- myDB$annotation$start[sel] # get start ...
|
|
end <- myDB$annotation$end[sel] # ... and end
|
|
|
|
sel <- myDB$protein$ID == proID # get the protein row index ...
|
|
# ... and the sequence
|
|
APSI[i] <- substring(myDB$protein$sequence[sel], start, end)
|
|
names(APSI)[i] <- (myDB$protein$name[sel])
|
|
}
|
|
|
|
head(APSI)
|
|
|
|
# Let's add the E.coli Kila-N domain sequence as an outgroup, for rooting our
|
|
# phylogenetic tree (see the unit's Wiki page for details on the sequence).
|
|
|
|
APSI <- c(APSI,
|
|
"IDGEIIHLRAKDGYINATSMCRTAGKLLSDYTRLKTTQEFFDELSRDMGIPISELIQSFKGGRPENQGTWVHPDIAINLAQ")
|
|
names(APSI)[length(APSI)] <- "KILA_ESCCO"
|
|
tail(APSI)
|
|
|
|
|
|
# = 3 Multiple Sequence Alignment =========================================
|
|
|
|
# This vector of sequences with named elements fulfills the requirements to be
|
|
# imported as a Biostrings object - an AAStringSet - which we need as input for
|
|
# the MSA algorithms in Biostrings.
|
|
#
|
|
|
|
APSESSet <- AAStringSet(APSI)
|
|
APSESMsa <- msaMuscle(APSESSet, order = "aligned")
|
|
|
|
# Nb. msaMuscle() sometimes fails - reproducibly, but I am not sure why. If
|
|
# that happens in your case, just use msaClustalOmega() instead.
|
|
|
|
# inspect the alignment.
|
|
writeALN(APSESMsa)
|
|
|
|
# What do you think? Is this a good alignment for phylogenetic inference?
|
|
|
|
|
|
# = 4 Reviewing and Editing Alignments ====================================
|
|
|
|
|
|
# Head back to the Wiki page for this unit and read up on the background
|
|
# first.
|
|
|
|
# Let's mask out all columns that have observations for
|
|
# less than 1/3 of the sequences in the dataset. This
|
|
# means they have more than round(nrow(msaSet) * (2/3))
|
|
# hyphens in a column.
|
|
#
|
|
# We take all sequences, split them into single
|
|
# characters, and put them into a matrix. Then we
|
|
# go through the matrix, column by column and decide
|
|
# whether we want to include that column.
|
|
|
|
# == 4.1 Masking workflow ==================================================
|
|
|
|
# get the length of the alignment
|
|
(lenAli <- APSESMsa@unmasked@ranges@width[1])
|
|
|
|
# initialize a matrix that can hold all characters
|
|
# individually
|
|
msaMatrix <- matrix(character(nrow(APSESMsa) * lenAli),
|
|
ncol = lenAli)
|
|
|
|
# assign the correct rownames
|
|
rownames(msaMatrix) <- APSESMsa@unmasked@ranges@NAMES
|
|
for (i in 1:nrow(APSESMsa)) {
|
|
msaMatrix[i, ] <- unlist(strsplit(as.character(APSESMsa@unmasked[i]), ""))
|
|
}
|
|
|
|
# inspect the result
|
|
msaMatrix[1:7, 1:14]
|
|
|
|
# Now let's make a logical vector with an element for each column that selects
|
|
# which columns should be masked out.
|
|
|
|
# The number of hyphens in a column is easy to count. Consider:
|
|
|
|
msaMatrix[ , 20]
|
|
msaMatrix[ , 20] == "-"
|
|
sum(msaMatrix[ , 20] == "-")
|
|
|
|
# Thus filling our logical vector is simple:
|
|
|
|
# initialize a mask
|
|
colMask <- logical(ncol(msaMatrix))
|
|
|
|
# define the threshold for rejecting a column
|
|
limit <- round(nrow(APSESMsa) * (2/3))
|
|
|
|
# iterate over all columns, and write TRUE if there are less-or-equal to "limit"
|
|
# hyphens, FALSE if there are more.
|
|
for (i in 1:ncol(msaMatrix)) {
|
|
count <- sum(msaMatrix[ , i] == "-")
|
|
colMask[i] <- count <= limit # FALSE if less-or-equal to limit, TRUE if not
|
|
}
|
|
|
|
# inspect the mask
|
|
colMask
|
|
|
|
# How many positions were masked?
|
|
sum(colMask)
|
|
|
|
cat(sprintf("We are masking %4.2f %% of alignment columns.\n",
|
|
100 * (1 - (sum(colMask) / length(colMask)))))
|
|
|
|
|
|
# Next, we use colMask to remove the masked columns from the matrix
|
|
# in one step:
|
|
maskedMatrix <- msaMatrix[ , colMask]
|
|
|
|
# check:
|
|
ncol(maskedMatrix)
|
|
|
|
# ... then collapse each row of single characters back into a string ...
|
|
APSESphyloSet <- character()
|
|
for (i in 1:nrow(maskedMatrix)) {
|
|
APSESphyloSet[i] <- paste(maskedMatrix[i, ], collapse="")
|
|
}
|
|
names(APSESphyloSet) <- rownames(maskedMatrix)
|
|
|
|
# inspect ...
|
|
writeALN(APSESphyloSet)
|
|
|
|
# As you see, we have removed a three residue insertion from MBP1_NEUCR, and
|
|
# several indels from the KILA_ESCCO outgroup sequence.
|
|
|
|
|
|
# We save the aligned, masked domains to a file in multi-FASTA format.
|
|
writeMFA(APSESphyloSet, myCon = "APSESphyloSet.mfa")
|
|
|
|
|
|
|
|
|
|
# [END]
|