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