New unit, and update to annotation file logic
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@ -3,12 +3,14 @@
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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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# Version: 1.1
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#
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# Date: 2017 09 - 2017 10
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# Date: 2017 09 - 2017 11
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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 Update annotation file logic - it could already have been
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# prepared in the BIN-FUNC-Annotation unit.
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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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@ -26,16 +28,20 @@
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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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#TOC> Section Title Line
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#TOC> --------------------------------------------------------------------
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#TOC> 1 Prepare 48
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#TOC> 2 Biostrings Pairwise Alignment 56
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#TOC> 2.1 Optimal global alignment 73
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#TOC> 2.2 Optimal local alignment 136
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#TOC> 3 APSES Domain annotation by alignment 160
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#TOC> 4 Update your database script 241
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#TOC> 4.1 Preparing an annotation file ... 247
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#TOC> 4.1.1 If you HAVE NOT done the BIN-FUNC-Annotation unit 249
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#TOC> 4.1.2 If you HAVE done the BIN-FUNC-Annotation unit 292
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#TOC> 4.2 Execute and Validate 316
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#TOC>
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#TOC> ==========================================================================
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@ -236,38 +242,90 @@ aliApses@subject@range@start + aliApses@subject@range@width - 1
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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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# right away and store it in myDB.
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# == 4.1 Preparing an annotation file ... ==================================
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#
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# === 4.1.1 If you HAVE NOT done the BIN-FUNC-Annotation unit
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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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# You DON'T already have a file called "<MYSPE>-Annotations.json" in the
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# ./data/ directory:
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#
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# - Make a copy of the file "./data/refAnnotations.json" and put it in your
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# project directory.
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#
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# - Give it a name that is structured like "<MYSPE>-Annotations.json" - e.g.
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# if MYSPE is called "Crptycoccus neoformans", your file should be called
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# "CRYNE-Annotations.json" (and the "name" of your Mbp1 orthologue is
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# "MBP1_CRYNE").
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#
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# - Open the file in the RStudio editor and delete all blocks for
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# the Mbp1 protein annotations except the first one.
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#
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# - From that block, delete all lines except for the line that says:
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#
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# {"pName" : "MBP1_SACCE", "fName" : "APSES fold", "start" : "4", "end" : "102"},
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#
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# - Then delete the comma at the end of the line (your file will just have
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# this one annotation).
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#
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# - Edit that annotation: change MBP1_SACCE to MBP1_<MYSPE> and change the
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# "start" and "end" features to the coordinates you just discovered for the
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# APSES domain in your sequence.
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#
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# - 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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# - Update your "makeProteinDB.R" script to load your new
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# annotation when you recreate the database. Open the script in the
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# RStudio editor, 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 and close the file.
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#
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# Then SKIP the next section.
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#
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#
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# === 4.1.2 If you HAVE done the BIN-FUNC-Annotation unit
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#
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#
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# You DO already have a file called "<MYSPE>-Annotations.json" in the
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# ./data/ directory:
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#
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# - Open the file in the RStudio editor.
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#
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# - Below the last feature lines (but before the closing "]") add the
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# following feature line (without the "#")
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#
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# {"pName" : "MBP1_SACCE", "fName" : "APSES fold", "start" : "4", "end" : "102"}
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#
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# - Edit that annotation: change MBP1_SACCE to MBP1_<MYSPE> and change the
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# "start" and "end" features to the coordinates you just discovered for the
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# APSES domain in your sequence.
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#
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# - Add a comma after the preceding feature line.
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#
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# - 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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# == 4.2 Execute and Validate ==============================================
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#
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# - save the file and source() it:
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# - source() your database creation script:
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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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# can't figure out quickly what's happening, 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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# coordinates and sequence of 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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@ -276,7 +334,7 @@ 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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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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@ -284,5 +342,4 @@ aaMBP1_MYSPE <- AAString(myDB$protein$sequence[sel])
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end))
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# [END]
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@ -3,65 +3,160 @@
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-FUNC-Domain_annotation 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 11 13
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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 Live version 2017
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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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# = 1 SMART Domain annotations
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# Plot domain annotations as colored rectangles on a sequence.
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# Step one: enter your domain annotations as features into the database.
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#
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# == Update myDB
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# If the reference database has changed, we need to merge it in with myDB.
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load("myDB.03.RData") # load the previous version of myDB
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# the new version of refDB was loaded when you
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# pulled it from GitHub, and then typed init()
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myDB <- dbMerge(myDB) # merge the two databases and update myDB with the result
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save(myDB, file = "myDB.04.RData") # save the new version
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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 Update your database script 41
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#TOC> 1.1 Preparing an annotation file ... 47
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#TOC> 1.1.1 If you HAVE NOT done the BIN-ALI-Optimal_sequence_alignment unit 49
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#TOC> 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment 93
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#TOC> 1.2 Execute and Validate 119
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#TOC> 2 Plot Annotations 144
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#TOC>
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#TOC> ==========================================================================
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# == Update myDB
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# Every annotated feature requires its own entry in the database. You have added
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# the feature for the "APSES fold" before, so you can copy and edit that code
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# from your myCode.R script. Here is again the table of feature IDs:
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myDB$feature[ , c("ID", "name", "description")]
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# = 1 Update your database script =========================================
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# Add every SMART annotated feaure for MBP1_MYSPE to the database. If you make
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# mistakes, just reload the latest version (probably "myDB.04.RData"), then run
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# your corrected annotation script again. Execute ...
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myDB$proteinAnnotation
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# ... to confirm.
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# Since you have recorded domain features at the SMART database, we can store
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# the feature annotations in myDB.
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# == 1.1 Preparing an annotation file ... ==================================
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#
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# Once you are sure your annotations are correct, save the database again.
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save(myDB, file = "myDB.05.RData") # save the new version
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# === 1.1.1 If you HAVE NOT done the BIN-ALI-Optimal_sequence_alignment unit
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#
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# Now let's plot the annotations.
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#
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# You DON'T already have a file called "<MYSPE>-Annotations.json" in the
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# ./data/ directory:
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#
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# - Make a copy of the file "./data/refAnnotations.json" and put it in your
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# project directory.
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#
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# - Give it a name that is structured like "<MYSPE>-Annotations.json" - e.g.
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# if MYSPE is called "Crptycoccus neoformans", your file should be called
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# "CRYNE-Annotations.json" (and the "name" of your Mbp1 orthologue is
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# "MBP1_CRYNE").
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#
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# - Open the file in the RStudio editor and delete all blocks for
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# the Mbp1 protein annotations except the first one.
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#
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# - From that block, delete all lines that have annotations you did not
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# find in SMART for MBP1_MYSPE.
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#
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# - Make enough copies of the "Ankyrin fold" and "low complexity" region
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# lines to have a line for each feature you found.
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#
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# - Then delete the comma at the end of the last line.
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#
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# - Edit the annotations: change MBP1_SACCE to MBP1_<MYSPE> everywhere
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# and change the "start" and "end" features to the coordinates you
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# recorded in the SMART database.
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#
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# - 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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# - Update your "makeProteinDB.R" script to load your new
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# annotation when you recreate the database. Open the script in the
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# RStudio editor, 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 and close the file.
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#
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# Then SKIP the next section.
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#
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#
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# === 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment
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#
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#
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# You DO already have a file called "<MYSPE>-Annotations.json" in the
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# ./data/ directory:
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#
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# - Open the file in the RStudio editor.
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#
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# - Make as many copies of the "APSES fold" line as you have found
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# features in SMART.
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#
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# - Add a comma after every line except for the last one
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#
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# - Edit the annotations but include only features that are in the
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# myDB$feature table. Check which features are in the databse by executing
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#
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# myDB$feature$name
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#
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# - Update the "start" and "end" coordinates for each feature to the
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# values you found.
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#
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# - Save your file.
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#
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# - Validate your file online at https://jsonlint.com/
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#
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#
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# == 1.2 Execute and Validate ==============================================
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#
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# - source() your database creation script:
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#
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# source("makeProteinDB.R")
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#
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# This should run without errors or warnings. If it doesn't work and you
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# can't figure out quickly what's happening, ask on the mailing list for
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# help.
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#
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# - Confirm
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# The following commands should retrieve all of the features that have been
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# annotated for MBP1_MYSPE
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sel <- myDB$protein$name == paste("MBP1_", biCode(MYSPE), sep = "")
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(proID <- myDB$protein$ID[sel])
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(fanIDs <- myDB$annotation$ID[myDB$annotation$proteinID == proID])
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(ftrIDs <- unique(myDB$annotation$featureID[fanIDs]))
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myDB$feature$name[ftrIDs] # This should list ALL of your annotated features
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# (once). If not, consider what could have gone wrong
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# and ask on the list if you have difficulties fixing
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# it.
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# = 2 Plot Annotations ====================================================
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# In this section we will plot domain annotations as colored rectangles on a
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# sequence, as an example for using the R plotting system for generic, data
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# driven images.
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# We need a small utility function that draws the annotation boxes on a
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# representation of sequence. It will accept the left and right boundaries, the
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# height and the color of the box and plot it using R's rect() function.
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# representation of sequence. It should accept the start and end coordinates,
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# the y value where it should be plotted and the color of the box, and plot a
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# rectangle using R's rect() function.
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drawBox <- function(xLeft, xRight, y, colour) {
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# Draw a box from xLeft to xRight at y, filled with colour
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rect(xLeft, (y - 0.1), xRight, (y + 0.1),
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border = "black", col = colour)
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drawBox <- function(xStart, xEnd, y, myCol) {
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# Draw a box from xStart to xEnd at y, filled with colour myCol
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delta <- 0.1
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rect(xStart, (y - delta), xEnd, (y + delta),
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border = "black", col = myCol)
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}
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# test this:
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@ -71,10 +166,10 @@ drawBox(-1, 1, 0.0, "peachpuff")
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# Next, we define a function to plot annotations for one protein: the name of
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# the protein, a horizontal grey line for its length, and all of its features.
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plotProtein <- function(DB, ID, y) {
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# DB: protein database, probably you want myDB
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# ID: the ID of the protein to plot.
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# y: where to draw the plot
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plotProtein <- function(DB, name, y) {
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# DB: protein database
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# name: the name of the protein in the database.
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# y: height where to draw the plot
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#
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# Define colors: we create a vector of color values, one for
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# each feature, and we give it names of the feature ID. Then we
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@ -89,58 +184,79 @@ plotProtein <- function(DB, ID, y) {
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space="Lab",
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interpolate="linear")(nrow(DB$feature))
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# B: Features may overlap, so we make the colors transparent by setting
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# their "alpha channel" to 1/2 (hex: 7F)
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ftrCol <- paste(ftrCol, "7F", sep = "")
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# their "alpha channel" to 1/3 (hex: 55)
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ftrCol <- paste0(ftrCol, "55")
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# C: we asssign names
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names(ftrCol) <- DB$feature$ID
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# E.g. color for the third feature: ftrCol[ DB$feature$ID[3] ]
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# find the row-index of the protein ID in the protein table of DB
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iProtein <- which(DB$protein$ID == ID)
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iProtein <- which(DB$protein$name == name)
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# write the name of the protein
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text(-30, y, adj=1, labels=DB$protein$name[iProtein], cex=0.75 )
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text(-30, y, adj=1, labels=name, cex=0.75 )
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#draw a line from 0 to nchar(sequence-of-the-protein)
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lines(c(0, nchar(DB$protein$sequence[iProtein])), c(y, y),
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lwd=3, col="#999999")
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# get the rows of feature annotations for the protein
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iFtr <- which(DB$proteinAnnotation$protein.ID == ID)
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iFtr <- which(DB$annotation$proteinID == DB$protein$ID[iProtein])
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# draw a colored box for each feature
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for (i in iFtr) {
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drawBox(DB$proteinAnnotation$start[i],
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DB$proteinAnnotation$end[i],
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drawBox(DB$annotation$start[i],
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DB$annotation$end[i],
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y,
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ftrCol[ DB$proteinAnnotation$feature.ID[i] ])
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ftrCol[ DB$annotation$featureID[i] ])
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}
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}
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# Plot each annotated protein:
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# Get the rows of all unique annotated protein IDs in the protein table
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iRows <- which(myDB$protein$ID %in% unique(myDB$proteinAnnotation$protein.ID))
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# Get the rows of all unique annotated Mbp1 proteins in myDB
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iRows <- grep("^MBP1_", myDB$protein$name)
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# define the size of the plot-frame to accomodate all proteins
|
||||
yMax <- length(iRows) * 1.1
|
||||
xMax <- max(nchar(myDB$protein$sequence[iRows])) * 1.1 # longest sequence
|
||||
|
||||
# plot an empty frame
|
||||
plot(1,1, xlim=c(-200, xMax), ylim=c(0, yMax),
|
||||
type="n", axes=FALSE, bty="n", xlab="sequence position", ylab="")
|
||||
plot(1, 1,
|
||||
xlim = c(-200, xMax + 100),
|
||||
ylim = c(0, yMax),
|
||||
type = "n",
|
||||
axes = FALSE,
|
||||
bty = "n",
|
||||
main = "Mbp1 orthologue domain annotations",
|
||||
xlab = "sequence position",
|
||||
ylab="")
|
||||
axis(1, at = seq(0, xMax, by = 100))
|
||||
myCol <- colorRampPalette(c("#f2003c", "#F0A200",
|
||||
"#f0ea00", "#62C923",
|
||||
"#0A9A9B", "#1958C3",
|
||||
"#8000D3", "#D0007F"),
|
||||
space="Lab",
|
||||
interpolate="linear")(nrow(myDB$feature))
|
||||
myCol <- paste0(myCol, "55")
|
||||
legend(xMax - 150, 6,
|
||||
legend = myDB$feature$name,
|
||||
cex = 0.7,
|
||||
fill = myCol)
|
||||
|
||||
|
||||
# Finally, iterate over all proteins and call plotProtein()
|
||||
for (i in 1:length(iRows)) {
|
||||
plotProtein(myDB, myDB$protein$ID[iRows[i]], i)
|
||||
for (i in seq_along(iRows)) {
|
||||
plotProtein(myDB, myDB$protein$name[iRows[i]], i)
|
||||
}
|
||||
|
||||
# The plot shows clearly what is variable and what is constant about the
|
||||
# annotations in a group of related proteins. Print the plot and bring it to
|
||||
# class for the next quiz.
|
||||
#
|
||||
|
||||
# = 1 Tasks
|
||||
# The plot shows what is variable and what is constant about the annotations in
|
||||
# a group of related proteins. Your MBP1_MYSPE annotations should appear at the
|
||||
# top.
|
||||
|
||||
# Task:
|
||||
# Put a copy of the plot into your journal and interpret it with respect
|
||||
# to MBP1_MYSPE, i.e. and note what you learn about MBP1_MYSPE from the plot.
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user