289 lines
10 KiB
R
289 lines
10 KiB
R
# BIN-ALI-Optimal_sequence_alignment.R
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-ALI-Optimal_sequence_alignment unit.
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#
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# Version: 1.0.1
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#
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# Date: 2017 09 - 2017 10
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.0.1 bugfix
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# 1.0 First 2017 live version.
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# 0.1 First code copied from 2016 material.
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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 Prepare 45
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#TOC> 2 Biostrings Pairwise Alignment 53
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#TOC> 2.1 Optimal global alignment 70
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#TOC> 2.2 Optimal local alignment 133
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#TOC> 3 APSES Domain annotation by alignment 157
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#TOC> 4 Update your database script 238
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#TOC>
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#TOC> ==========================================================================
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# = 1 Prepare =============================================================
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# You need to recreate the protein database that you have constructed in the
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# BIN-Storing_data unit.
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source("makeProteinDB.R")
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# = 2 Biostrings Pairwise Alignment =======================================
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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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# 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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# Biostrings stores sequences in "XString" objects. Once we have converted our
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# target sequences to AAString objects, the alignment itself is straightforward.
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# == 2.1 Optimal global alignment ==========================================
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# The pairwiseAlignment() function was written to behave
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# exactly like the functions you encountered on the EMBOSS server.
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# First: make AAString objects ...
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sel <- myDB$protein$name == "MBP1_SACCE"
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aaMBP1_SACCE <- AAString(myDB$protein$sequence[sel])
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sel <- myDB$protein$name == paste("MBP1_", biCode(MYSPE), sep = "")
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aaMBP1_MYSPE <- AAString(myDB$protein$sequence[sel])
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?pairwiseAlignment
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# ... and align.
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# Global optimal alignment with end-gap penalties is default.
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ali1 <- pairwiseAlignment(
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aaMBP1_SACCE,
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aaMBP1_MYSPE,
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substitutionMatrix = "BLOSUM62",
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gapOpening = 10,
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gapExtension = 0.5)
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str(ali1) # ... it's complicated
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# This is a Biostrings alignment object. But we can use Biostrings functions to
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# tame it:
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ali1
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writePairwiseAlignments(ali1) # That should look familiar
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# And we can make the internal structure work for us (@ is for classes as
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# $ is for lists ...)
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str(ali1@pattern)
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ali1@pattern
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ali1@pattern@range
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ali1@pattern@indel
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ali1@pattern@mismatch
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# or work with "normal" R functions
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# the alignment length
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nchar(ali1@pattern)
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# the number of identities
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sum(s2c(as.character(ali1@pattern)) ==
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s2c(as.character(ali1@subject)))
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# ... e.g. to calculate the percentage of identities
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100 *
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sum(s2c(as.character(ali1@pattern)) ==
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s2c(as.character(ali1@subject))) /
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nchar(ali1@pattern)
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# ... which should be the same as reported in the writePairwiseAlignments()
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# output. Awkward to type? Then it calls for a function:
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#
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percentID <- function(al) {
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# returns the percent-identity of a Biostrings alignment object
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return(100 *
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sum(s2c(as.character(al@pattern)) ==
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s2c(as.character(al@subject))) /
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nchar(al@pattern))
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}
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percentID(ali1)
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# == 2.2 Optimal local alignment ===========================================
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# Compare with local optimal alignment (like EMBOSS Water)
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ali2 <- pairwiseAlignment(
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aaMBP1_SACCE,
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aaMBP1_MYSPE,
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type = "local",
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substitutionMatrix = "BLOSUM62",
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gapOpening = 50,
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gapExtension = 10)
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writePairwiseAlignments(ali2) # This has probably only aligned the N-terminal
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# DNA binding domain - but that one has quite
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# high sequence identity:
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percentID(ali2)
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# == TASK: ==
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# Compare the two alignments. I have weighted the local alignment heavily
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# towards an ungapped alignment by setting very high gap penalties. Try changing
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# the gap penalties and see what happens: how does the number of indels change,
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# how does the length of indels change...
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# = 3 APSES Domain annotation by alignment ================================
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# In this section we define the MYSPE APSES sequence by performing a global,
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# optimal sequence alignment of the yeast APSES domain with the full length
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# protein sequence of the protein that was the most similar to the yeast APSES
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# domain.
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#
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# I have annotated the yeast APSES domain as a feature in the
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# database. To view the annotation, we can retrieve it via the proteinID and
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# featureID. Here is the yeast protein ID:
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(proID <- myDB$protein$ID[myDB$protein$name == "MBP1_SACCE"])
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# ... and if you look at the feature table, you can identify the feature ID
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(ftrID <- myDB$feature$ID[myDB$feature$name == "APSES fold"])
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# ... and with the two annotations we can get the corresponding ID from the
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# annotation table
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(fanID <- myDB$annotation$ID[myDB$annotation$proteinID == proID &
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myDB$annotation$featureID == ftrID])
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myDB$annotation[myDB$annotation$ID == proID &
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myDB$annotation$ID == ftrID, ]
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# The annotation record contains the start and end coordinates which we can use
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# to define the APSES domain sequence with a substr() expression.
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(start <- myDB$annotation$start[myDB$annotation$ID == fanID])
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(end <- myDB$annotation$end[myDB$annotation$ID == fanID])
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(apses <- substr(myDB$protein$sequence[myDB$protein$ID == proID],
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start,
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end))
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# Lots of code. But don't get lost. Let's recapitulate what we have done: we
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# have selected from the sequence column of the protein table the sequence whose
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# name is "MBP1_SACCE", and selected from the annotation table the start
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# and end coordinates of the annotation that joins an "APSES fold" feature with
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# the sequence, and used the start and end coordinates to extract a substring.
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# Let's convert this to an AAstring and assign it:
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aaMB1_SACCE_APSES <- AAString(apses)
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# Now let's align these two sequences of very different length without end-gap
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# penalties using the "overlap" type. "overlap" turns the
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# end-gap penalties off and that is crucially important since
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# the sequences have very different length.
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aliApses <- pairwiseAlignment(
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aaMB1_SACCE_APSES,
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aaMBP1_MYSPE,
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type = "overlap",
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substitutionMatrix = "BLOSUM62",
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gapOpening = 10,
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gapExtension = 0.5)
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# Inspect the result. The aligned sequences should be clearly
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# homologous, and have (almost) no indels. The entire "pattern"
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# sequence from QIYSAR ... to ... KPLFDF should be matched
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# with the "query". Is this correct?
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writePairwiseAlignments(aliApses)
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# If this is correct, you can extract the matched sequence from
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# the alignment object. The syntax is a bit different from what
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# you have seen before: this is an "S4 object", not a list. No
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# worries: as.character() returns a normal string.
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as.character(aliApses@subject)
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# Now, what are the aligned start and end coordinates? You can read them from
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# the output of writePairwiseAlignments(), or you can get them from the range of
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# the match.
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str(aliApses@subject@range)
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# start is:
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aliApses@subject@range@start
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# ... and end is:
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aliApses@subject@range@start + aliApses@subject@range@width - 1
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# = 4 Update your database script =========================================
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# Since we have this feature defined now, we can create a feature annotation
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# right away and store it in myDB. Follow the following steps carefully:
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#
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#
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# - Make a copy of the file "./data/refAnnotations.json" in your project
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# directory and give it a new name that corresponds to MYSPE - e.g. if
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# MYSPE is called "Crptycoccus neoformans", your file should be called
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# "CRYNEAnnotations.json"; in that case "MBP1_CRYNE" would also be the
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# "name" of your protein. Open the file in the RStudio editor and delete
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# all annotations but one for an "APSES fold". Edit that annotation to
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# correspond to the your MBP1_MYSPE protein and enter the start end end
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# coordinates you have just discovered for the APSES domain in your
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# sequence. Save your file.
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#
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# - Validate your file online at https://jsonlint.com/
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#
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# - Next, you need to update your "makeProteinDB.R" script to load the
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# annotation when you recreate the database. Open the script in the
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# RStudio ediotr, and add the following command at the end:
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#
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# myDB <- dbAddAnnotation(myDB, fromJSON("<MYSPE>Annotations.json"))
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#
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# - save the file and source() it:
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#
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# source("makeProteinDB.R")
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#
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# This should run without errors or warnings. If it doesn't work and you
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# can't figure out quickly what's happeneing, ask on the mailing list for
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# help.
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#
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# - Confirm
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# The following commands should retrieve the correct start and end
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# coordinates for the MBP1_MYSPE APSES domain:
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sel <- myDB$protein$name == paste("MBP1_", biCode(MYSPE), sep = "")
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aaMBP1_MYSPE <- AAString(myDB$protein$sequence[sel])
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(proID <- myDB$protein$ID[myDB$protein$name == "MBP1_<MYSSPE>"]) # <<< EDIT
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(ftrID <- myDB$feature$ID[myDB$feature$name == "APSES fold"])
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(fanID <- myDB$annotation$ID[myDB$annotation$proteinID == proID &
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myDB$annotation$featureID == ftrID])
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(start <- myDB$annotation$start[myDB$annotation$ID == fanID])
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(end <- myDB$annotation$end[myDB$annotation$ID == fanID])
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(apses <- substr(myDB$protein$sequence[myDB$protein$ID == proID],
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start,
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end))
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# [END]
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