240 lines
8.0 KiB
R
240 lines
8.0 KiB
R
# tocID <- "BIN-PHYLO-Data_preparation.R"
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#
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# ---------------------------------------------------------------------------- #
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# PATIENCE ... #
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# Do not yet work wih this code. Updates in progress. Thank you. #
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# boris.steipe@utoronto.ca #
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# ---------------------------------------------------------------------------- #
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#
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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: 1.1
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#
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# Date: 2017 10 - 2019 01
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.1 Change from require() to requireNamespace(),
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# use <package>::<function>() idiom throughout,
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# use Biocmanager:: not biocLite()
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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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#TOC> ==========================================================================
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#TOC>
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#TOC> Section Title Line
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#TOC> ---------------------------------------------------------
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#TOC> 1 Preparations 44
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#TOC> 2 Fetching sequences 76
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#TOC> 3 Multiple Sequence Alignment 117
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#TOC> 4 Reviewing and Editing Alignments 136
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#TOC> 4.1 Masking workflow 152
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#TOC>
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#TOC> ==========================================================================
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# = 1 Preparations ========================================================
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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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# Load packages we need
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if (! requireNamespace("BiocManager", quietly = TRUE)) {
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install.packages("BiocManager")
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}
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if (! requireNamespace("Biostrings", quietly = TRUE)) {
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BiocManager::install("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 (! requireNamespace("msa", quietly = TRUE)) {
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BiocManager::install("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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# = 2 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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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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# phylogenetic tree (see the unit's Wiki page for details on the sequence).
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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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# = 3 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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APSESSet <- Biostrings::AAStringSet(APSI)
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APSESMsa <- msa::msaMuscle(APSESSet, order = "aligned")
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# Nb. msaMuscle() sometimes fails - reproducibly, but I am not sure why. If
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# that happens in your case, just use msaClustalOmega() instead.
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# inspect the alignment.
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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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# = 4 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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# 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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# means they have more than round(nrow(msaSet) * (2/3))
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# hyphens in a column.
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#
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# We take all sequences, split them into single
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# characters, and put them into a matrix. Then we
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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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# == 4.1 Masking workflow ==================================================
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# get the length of the alignment
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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(APSESMsa) * lenAli),
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ncol = lenAli)
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# assign the correct rownames
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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[1:7, 1:14]
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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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# 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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# Thus filling our logical vector is simple:
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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(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 - i.e. TRUE columns will be used fr analysis
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# and FALSE columns will be rejected.
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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 # TRUE if less-or-equal to limit, FALSE 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 are being kept?
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sum(colMask)
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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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# Next, we use colMask to remove the masked columns from the matrix
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# in one step:
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maskedMatrix <- msaMatrix[ , colMask]
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# check:
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ncol(maskedMatrix)
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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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APSESphyloSet[i] <- paste(maskedMatrix[i, ], collapse="")
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}
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names(APSESphyloSet) <- rownames(maskedMatrix)
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# inspect ...
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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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# We save the aligned, masked domains to a file in multi-FASTA format.
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writeMFA(APSESphyloSet, myCon = "APSESphyloSet.mfa")
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# [END]
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