Add code and utility functions for database export of protein annotations to JSON - towards sharing annotations on the Student Wiki Public page
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# Purpose: A Bioinformatics Course:
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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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# R code accompanying the BIN-FUNC-Domain_annotation unit.
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#
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#
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# Version: 1.2
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# Version: 1.3
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#
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#
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# Date: 2017-11 - 2020-10
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# Date: 2017-11 - 2020-10
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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#
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# Versions:
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# Versions:
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# 1.3 Add code for database export to JSON and instructions
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# for uploading annotations to the Public Student Wiki page
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# 1.2 Consistently: data in ./myScripts/ ;
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# 1.2 Consistently: data in ./myScripts/ ;
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# begin SHARING DATA section
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# begin SHARING DATA section
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# 1.1 2020 Updates
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# 1.1 2020 Updates
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@ -28,16 +30,18 @@
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#TOC> ==========================================================================
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#TOC> ==========================================================================
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#TOC>
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#TOC>
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#TOC> Section Title Line
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#TOC> Section Title Line
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#TOC> ---------------------------------------------------------------------
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#TOC> ---------------------------------------------------------------------
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#TOC> 1 Update your database script 42
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#TOC> 1 Update your database script 48
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#TOC> 1.1 Preparing an annotation file ... 49
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#TOC> 1.1 Preparing an annotation file ... 55
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#TOC> 1.1.1 BEFORE "BIN-ALI-Optimal_sequence_alignment" 52
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#TOC> 1.1.1 BEFORE "BIN-ALI-Optimal_sequence_alignment" 58
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#TOC> 1.1.2 AFTER "BIN-ALI-Optimal_sequence_alignment" 97
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#TOC> 1.1.2 AFTER "BIN-ALI-Optimal_sequence_alignment" 106
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#TOC> 1.2 Execute and Validate 124
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#TOC> 1.2 Execute and Validate 133
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#TOC> 2 Plot Annotations 149
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#TOC> 2 Plot Annotations 158
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#TOC>
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#TOC> 3 SHARING DATA 283
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#TOC> 3.1 Post MBP1_MYSPE as JSON data 298
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#TOC>
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#TOC> ==========================================================================
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#TOC> ==========================================================================
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@ -99,7 +103,7 @@
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# Then SKIP the next section.
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# Then SKIP the next section.
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#
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#
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#
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#
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# === 1.1.2 AFTER "BIN-ALI-Optimal_sequence_alignment"
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# === 1.1.2 AFTER "BIN-ALI-Optimal_sequence_alignment"
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#
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#
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# IF YOU HAVE ALREADY COMPLETED THE BIN-ALI-OPTIMAL_SEQUENCE_ALIGNMENT UNIT:
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# IF YOU HAVE ALREADY COMPLETED THE BIN-ALI-OPTIMAL_SEQUENCE_ALIGNMENT UNIT:
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#
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#
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@ -276,16 +280,47 @@ par(oPar) # reset the plot parameters
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# It would be better to align the motif borders, at least approximately (not
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# It would be better to align the motif borders, at least approximately (not
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# all proteins have all motifs). How would you go about doing that?
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# all proteins have all motifs). How would you go about doing that?
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# = 1 SHARING DATA ======
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# = 3 SHARING DATA ========================================================
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# It's particularly interesting to compare such annotations across many
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# It's particularly interesting to compare such annotations across many
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# homologous proteins. I have created a file on the Student Wiki that you can
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# homologous proteins. I have created a page on the Student Wiki () that you can
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# edit, and then download the data from the entire class directly to your
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# edit, and then download the data from the entire class directly to your
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# RStudio project.
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# RStudio project.
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#
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#
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# I have provided a function that extracts all information that refers to a
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# single protein from the database, and prints it out as well-formatted JSON,
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# suitable to be pasted into our shareable Wiki-page. There is a fair amount of
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# bookkeeping involved, but the code is not otherwise very enlightening so I
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# will spare you the details - it's in "./scripts/ABC-dbUtilities.R" if you
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# would want to have a look.
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# == 3.1 Post MBP1_MYSPE as JSON data ======================================
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# Task:
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# Task:
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# =====
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# =====
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# TBC ...
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# 1: Run the following code:
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cat("{{Vspace}}",
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"<!-- ==== BEGIN PROTEIN ==== -->",
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"<pre>",
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dbProt2JSON(sprintf("MBP1_%s", biCode(MYSPE))),
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"</pre>",
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"<!-- ===== END PROTEIN ====== -->",
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"", sep = "\n"
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)
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# 2: Copy the entire output,
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# 3: Navigate to
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# http://steipe.biochemistry.utoronto.ca/abc/students/index.php/Public
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# ... edit the page, and paste your output at the top.
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# 4: Save your edits.
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# Next, once we have collected a number of protein annotations, we can access
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# the page and import the data into our database.
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#
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# Code to come soon ...
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# [END]
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# [END]
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# tocID <- "scripts/ABC-dbUtilities.R"
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# tocID <- "scripts/ABC-dbUtilities.R"
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#
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#
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# database utilities for ABC learning units
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# Purpose: Database utilities for ABC learning units.
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#
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# Version 2.1
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#
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# Date: 2017-11 - 2020-10
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 2.1 Add JSON export functions
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# 2.0 Test all JSON import and prevent addition of duplicates. This
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# is necessary for import of data from the public page
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# 1.1 2020 Updates
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# 1.0 Live version 2017
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#
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# Notes:
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# There are no functions to modify or delete entries. To do either,
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# recreate the database with correct data in the creation script. This is the
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# preferred way that ensures the entire database can be reproduced by
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# source()'ing its generating script.
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#
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# Inserting data goes only through the very most minimal validation steps. For
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# production applications, more validation would need to be added, as well
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# as an overall validation of database integrity
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#
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# ToDo:
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#
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#
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# ==============================================================================
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# ==============================================================================
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#TOC> ==========================================================================
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#TOC> ==========================================================================
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#TOC>
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#TOC>
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#TOC> Section Title Line
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#TOC> Section Title Line
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#TOC> -------------------------------------------------
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#TOC> -------------------------------------------------------
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#TOC> 1 PACKAGES 32
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#TOC> 1 INITIALISATIONS AND PARAMETERS 60
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#TOC> 2 FUNCTIONS 50
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#TOC> 2 PACKAGES 65
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#TOC> 2.01 dbSanitizeSequence() 53
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#TOC> 3 FUNCTIONS 81
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#TOC> 2.02 dbConfirmUnique() 88
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#TOC> 3.01 dbSanitizeSequence() 84
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#TOC> 2.03 dbInit() 106
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#TOC> 3.02 dbConfirmUnique() 119
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#TOC> 2.04 dbAutoincrement() 147
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#TOC> 3.03 dbInit() 137
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#TOC> 2.05 dbAddProtein() 160
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#TOC> 3.04 dbAutoincrement() 177
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#TOC> 2.06 dbAddFeature() 180
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#TOC> 3.05 dbAddProtein() 190
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#TOC> 2.07 dbAddTaxonomy() 199
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#TOC> 3.06 dbAddFeature() 222
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#TOC> 2.08 dbAddAnnotation() 215
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#TOC> 3.07 dbAddTaxonomy() 253
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#TOC> 2.09 dbFetchUniProtSeq() 243
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#TOC> 3.08 dbAddAnnotation() 288
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#TOC> 2.10 dbFetchPrositeFeatures() 289
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#TOC> 3.09 dbFetchUniProtSeq() 335
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#TOC> 2.11 node2text() 339
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#TOC> 3.10 dbFetchPrositeFeatures() 381
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#TOC> 2.12 dbFetchNCBItaxData() 351
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#TOC> 3.11 node2text() 431
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#TOC> 2.13 UniProtIDmap() 390
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#TOC> 3.12 dbFetchNCBItaxData() 443
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#TOC> 3 TESTS 429
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#TOC> 3.13 UniProtIDmap() 482
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#TOC> 3.14 dbProt2JSON() 521
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#TOC> 3.15 dbSeq2JSON() 606
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#TOC> 3.16 dbRow2JSON() 636
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#TOC> 4 TESTS 656
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#TOC>
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#TOC>
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#TOC> ==========================================================================
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#TOC> ==========================================================================
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# = 1 PACKAGES ============================================================
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# = 1 INITIALISATIONS AND PARAMETERS ======================================
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doTESTS <- FALSE # run tests if TRUE
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# = 2 PACKAGES ============================================================
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if (! requireNamespace("jsonlite", quietly = TRUE)) {
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if (! requireNamespace("jsonlite", quietly = TRUE)) {
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install.packages("jsonlite")
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install.packages("jsonlite")
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}
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}
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if (! requireNamespace("httr", quietly = TRUE)) {
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if (! requireNamespace("httr", quietly = TRUE)) {
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install.packages("httr")
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install.packages("httr")
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}
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}
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if (! requireNamespace("xml2", quietly = TRUE)) {
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if (! requireNamespace("xml2", quietly = TRUE)) {
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install.packages("xml2")
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install.packages("xml2")
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}
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}
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# = 2 FUNCTIONS ===========================================================
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# = 3 FUNCTIONS ===========================================================
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# == 2.01 dbSanitizeSequence() =============================================
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# == 3.01 dbSanitizeSequence() =============================================
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dbSanitizeSequence <- function(s, unambiguous = TRUE) {
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dbSanitizeSequence <- function(s, unambiguous = TRUE) {
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# Remove FASTA header lines, if any,
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# Remove FASTA header lines, if any,
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# flatten any structure that s has,
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# flatten any structure that s has,
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}
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}
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# == 2.02 dbConfirmUnique() ================================================
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# == 3.02 dbConfirmUnique() ================================================
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dbConfirmUnique <- function(x) {
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dbConfirmUnique <- function(x) {
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# x is a vector of logicals.
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# x is a vector of logicals.
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# returns x if x has exactly one TRUE element.
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# returns x if x has exactly one TRUE element.
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}
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}
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# == 2.03 dbInit() =========================================================
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# == 3.03 dbInit() =========================================================
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dbInit <- function() {
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dbInit <- function() {
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# Return an empty instance of the protein database
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# Return an empty instance of the protein database
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# Open the link and study the schema:
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# The schema is here:
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# https://docs.google.com/presentation/d/13vWaVcFpWEOGeSNhwmqugj2qTQuH1eZROgxWdHGEMr0
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# https://docs.google.com/presentation/d/13vWaVcFpWEOGeSNhwmqugj2qTQuH1eZROgxWdHGEMr0
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db <- list()
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db <- list()
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ID = numeric(),
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ID = numeric(),
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species = character())
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species = character())
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db$annotation <- data.frame(
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db$annotation <- data.frame(
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ID = numeric(),
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ID = numeric(),
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proteinID = numeric(),
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proteinID = numeric(),
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}
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}
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# == 2.04 dbAutoincrement() ================================================
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# == 3.04 dbAutoincrement() ================================================
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dbAutoincrement <- function(tb) {
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dbAutoincrement <- function(tb) {
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# Return a unique integer that can be used as a primary key
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# Return a unique integer that can be used as a primary key
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# Value:
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# Value:
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}
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}
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# == 2.05 dbAddProtein() ===================================================
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# == 3.05 dbAddProtein() ===================================================
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dbAddProtein <- function(db, jsonDF) {
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dbAddProtein <- function(db, jsonDF) {
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# Add one or more protein entries to the database db.
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# Add one or more protein entries to the database db if a protein with the
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# same name does not yet exist. This enforces that protein names are unique.
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# Parameters:
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# Parameters:
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# db list a database created with dbInit()
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# db list a database created with dbInit()
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# jsonDF data frame protein data imported into a data frame with
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# jsonDF data frame protein data imported into a data frame with
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# fromJSON()
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# fromJSON()
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for (i in seq_len(nrow(jsonDF))) {
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for (i in seq_len(nrow(jsonDF))) {
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x <- data.frame(ID = dbAutoincrement(db$protein),
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if (jsonDF$name[i] %in% db$protein$name) {
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name = jsonDF$name[i],
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cat(sprintf("Note: Protein No. %d in the input is \"%s\", but %s.\n",
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RefSeqID = jsonDF$RefSeqID[i],
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i, jsonDF$name[i],
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UniProtID = jsonDF$UniProtID[i],
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"a protein with this name already exists in the database. ",
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taxonomyID = jsonDF$taxonomyID[i],
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"Skipping this input."))
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sequence = dbSanitizeSequence(jsonDF$sequence[i]))
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isValid <- FALSE
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db$protein <- rbind(db$protein, x)
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}
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if (isValid) {
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x <- data.frame(ID = dbAutoincrement(db$protein),
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name = jsonDF$name[i],
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RefSeqID = jsonDF$RefSeqID[i],
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UniProtID = jsonDF$UniProtID[i],
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taxonomyID = jsonDF$taxonomyID[i],
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sequence = dbSanitizeSequence(jsonDF$sequence[i]))
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db$protein <- rbind(db$protein, x)
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}
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}
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}
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return(db)
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return(db)
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}
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}
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# == 2.06 dbAddFeature() ===================================================
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# == 3.06 dbAddFeature() ===================================================
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dbAddFeature <- function(db, jsonDF) {
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dbAddFeature <- function(db, jsonDF) {
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# Add one or more feature entries to the database db.
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# Add one or more feature entries to the database db. Skip if a feature with
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# the same name already exists.
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# Parameters:
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# Parameters:
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# db list a database created with dbInit()
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# db list a database created with dbInit()
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# jsonDF data frame feature data imported into a data frame with
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# jsonDF data frame feature data imported into a data frame with
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# fromJSON()
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# fromJSON()
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for (i in seq_len(nrow(jsonDF))) {
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for (i in seq_len(nrow(jsonDF))) {
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x <- data.frame(ID = dbAutoincrement(db$feature),
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isValid <- TRUE
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name = jsonDF$name[i],
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if (jsonDF$name[i] %in% db$feature$name) {
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description = jsonDF$description[i],
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cat(sprintf("Note: Feature No. %d in the input is \"%s\", but %s.\n",
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sourceDB = jsonDF$sourceDB[i],
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i, jsonDF$name[i],
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accession = jsonDF$accession[i])
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"a feature with this name already exists in the database. ",
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db$feature <- rbind(db$feature, x)
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"Skipping this input."))
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isValid <- FALSE
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}
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if (isVALID) {
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x <- data.frame(ID = dbAutoincrement(db$feature),
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name = jsonDF$name[i],
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description = jsonDF$description[i],
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sourceDB = jsonDF$sourceDB[i],
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accession = jsonDF$accession[i])
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db$feature <- rbind(db$feature, x)
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}
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}
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}
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return(db)
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return(db)
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}
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}
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# == 2.07 dbAddTaxonomy() ==================================================
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# == 3.07 dbAddTaxonomy() ==================================================
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dbAddTaxonomy <- function(db, jsonDF) {
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dbAddTaxonomy <- function(db, jsonDF) {
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# Add one or more taxonomy entries to the database db.
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# Add one or more taxonomy entries to the database db. Skip if species name
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# or taxonomy ID already exist in the database.
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# Parameters:
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# Parameters:
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# db list A database created with dbInit()
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# db list A database created with dbInit()
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# jsonDF data frame Taxonomy data imported into a data frame with
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# jsonDF data frame Taxonomy data imported into a data frame with
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# fromJSON()
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# fromJSON()
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for (i in seq_len(nrow(jsonDF))) {
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for (i in seq_len(nrow(jsonDF))) {
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x <- data.frame(
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isValid <- TRUE
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ID = jsonDF$ID[i],
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species = jsonDF$species[i])
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if (jsonDF$species[i] %in% db$taxonomy$species) {
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db$taxonomy <- rbind(db$taxonomy, x)
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cat(sprintf("Note: Species No. %d in the input is \"%s\", but %s%s\n",
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i, jsonDF$name[i],
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"a species with this name already exists in the database. ",
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"Skipping this input."))
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isValid <- FALSE
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} else if (jsonDF$ID[i] %in% db$taxonomy$ID) {
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cat(sprintf("Note: Taxonomy ID No. %d in the input is \"%d\", but %s%s\n",
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i, jsonDF$ID[i],
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"this taxonomy ID already exists in the database. ",
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"Skipping this input."))
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isValid <- FALSE
|
||||||
|
}
|
||||||
|
if (isValid) {
|
||||||
|
x <- data.frame(
|
||||||
|
ID = as.integer(jsonDF$ID[i]),
|
||||||
|
species = jsonDF$species[i])
|
||||||
|
db$taxonomy <- rbind(db$taxonomy, x)
|
||||||
|
}
|
||||||
}
|
}
|
||||||
return(db)
|
return(db)
|
||||||
}
|
}
|
||||||
|
|
||||||
# == 2.08 dbAddAnnotation() ================================================
|
|
||||||
|
# == 3.08 dbAddAnnotation() ================================================
|
||||||
dbAddAnnotation <- function(db, jsonDF) {
|
dbAddAnnotation <- function(db, jsonDF) {
|
||||||
# Add one or more annotation entries to the database db.
|
# Add one or more annotation entries to the database db. Skip the entry if
|
||||||
|
# it already exists in the database.
|
||||||
# Parameters:
|
# Parameters:
|
||||||
# db list a database created with dbInit()
|
# db list a database created with dbInit()
|
||||||
# jsonDF data frame annotation data imported into a data frame with
|
# jsonDF data frame annotation data imported into a data frame with
|
||||||
# fromJSON()
|
# fromJSON()
|
||||||
for (i in seq_len(nrow(jsonDF))) {
|
for (i in seq_len(nrow(jsonDF))) {
|
||||||
|
isValid <- TRUE
|
||||||
|
|
||||||
sel <- jsonDF$pName[i] == db$protein$name
|
sel <- jsonDF$pName[i] == db$protein$name
|
||||||
sel <- dbConfirmUnique(sel)
|
sel <- dbConfirmUnique(sel) # Confirm that this protein ID exists
|
||||||
pID <- db$protein$ID[sel]
|
pID <- db$protein$ID[sel]
|
||||||
|
|
||||||
sel <- jsonDF$fName[i] == db$feature$name
|
sel <- jsonDF$fName[i] == db$feature$name
|
||||||
sel <- dbConfirmUnique(sel)
|
sel <- dbConfirmUnique(sel) # Confirm that this feature ID exists
|
||||||
fID <- db$feature$ID[sel]
|
fID <- db$feature$ID[sel]
|
||||||
|
|
||||||
x <- data.frame(ID = dbAutoincrement(db$annotation),
|
sel <- db$annotation$proteinID == pID &
|
||||||
proteinID = pID,
|
db$annotation$featureID == fID &
|
||||||
featureID = fID,
|
db$annotation$start == as.integer(jsonDF$start[idx]) &
|
||||||
start = as.integer(jsonDF$start[i]),
|
db$annotation$end == as.integer(jsonDF$end[idx])
|
||||||
end = as.integer(jsonDF$end[i]))
|
|
||||||
db$annotation <- rbind(db$annotation, x)
|
if (any(sel)) {
|
||||||
|
cat(sprintf("Note: annotation No. %d in the input has %s%s%\n",
|
||||||
|
i,
|
||||||
|
"the same protein name, feature name, start, and end ",
|
||||||
|
"as one that already exists in the database. ",
|
||||||
|
"Skipping this input."))
|
||||||
|
|
||||||
|
isValid <- FALSE
|
||||||
|
}
|
||||||
|
|
||||||
|
if (isValid) {
|
||||||
|
x <- data.frame(ID = dbAutoincrement(db$annotation),
|
||||||
|
proteinID = pID,
|
||||||
|
featureID = fID,
|
||||||
|
start = as.integer(jsonDF$start[i]),
|
||||||
|
end = as.integer(jsonDF$end[i]))
|
||||||
|
db$annotation <- rbind(db$annotation, x)
|
||||||
|
}
|
||||||
}
|
}
|
||||||
return(db)
|
return(db)
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
# == 2.09 dbFetchUniProtSeq() ==============================================
|
# == 3.09 dbFetchUniProtSeq() ==============================================
|
||||||
dbFetchUniProtSeq <- function(IDs) {
|
dbFetchUniProtSeq <- function(IDs) {
|
||||||
# Fetch a protein sequence from UniProt.
|
# Fetch a protein sequence from UniProt.
|
||||||
# Parameters:
|
# Parameters:
|
||||||
@ -286,7 +378,7 @@ if (FALSE) {
|
|||||||
|
|
||||||
|
|
||||||
|
|
||||||
# == 2.10 dbFetchPrositeFeatures() =========================================
|
# == 3.10 dbFetchPrositeFeatures() =========================================
|
||||||
dbFetchPrositeFeatures <- function(ID) {
|
dbFetchPrositeFeatures <- function(ID) {
|
||||||
# Fetch feature annotations from ScanProsite.
|
# Fetch feature annotations from ScanProsite.
|
||||||
# Parameters:
|
# Parameters:
|
||||||
@ -336,7 +428,7 @@ if (FALSE) {
|
|||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
# == 2.11 node2text() ======================================================
|
# == 3.11 node2text() ======================================================
|
||||||
node2text <- function(doc, tag) {
|
node2text <- function(doc, tag) {
|
||||||
# an extractor function for the contents of elements
|
# an extractor function for the contents of elements
|
||||||
# between given tags in an XML response.
|
# between given tags in an XML response.
|
||||||
@ -348,7 +440,7 @@ node2text <- function(doc, tag) {
|
|||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
# == 2.12 dbFetchNCBItaxData() =============================================
|
# == 3.12 dbFetchNCBItaxData() =============================================
|
||||||
dbFetchNCBItaxData <- function(ID) {
|
dbFetchNCBItaxData <- function(ID) {
|
||||||
# Fetch feature taxID and Organism from the NCBI.
|
# Fetch feature taxID and Organism from the NCBI.
|
||||||
# Parameters:
|
# Parameters:
|
||||||
@ -387,7 +479,7 @@ dbFetchNCBItaxData <- function(ID) {
|
|||||||
|
|
||||||
|
|
||||||
|
|
||||||
# == 2.13 UniProtIDmap() ===================================================
|
# == 3.13 UniProtIDmap() ===================================================
|
||||||
UniProtIDmap <- function (s, mapFrom = "P_REFSEQ_AC", mapTo = "ACC") {
|
UniProtIDmap <- function (s, mapFrom = "P_REFSEQ_AC", mapTo = "ACC") {
|
||||||
# Use UniProt ID mapping service to map one or more IDs
|
# Use UniProt ID mapping service to map one or more IDs
|
||||||
# Parameters:
|
# Parameters:
|
||||||
@ -426,9 +518,144 @@ UniProtIDmap <- function (s, mapFrom = "P_REFSEQ_AC", mapTo = "ACC") {
|
|||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
# = 3 TESTS ===============================================================
|
# == 3.14 dbProt2JSON() ====================================================
|
||||||
|
dbProt2JSON <- function(thisProt) {
|
||||||
|
# Extract all protein related data from myDB and return in JSON format.
|
||||||
|
|
||||||
if (FALSE) {
|
thisData <- list()
|
||||||
|
|
||||||
|
# add a protein table
|
||||||
|
sel <- which(myDB$protein$name == thisProt)
|
||||||
|
thisData$protein <- myDB$protein[sel, ]
|
||||||
|
|
||||||
|
# add a taxonomy table
|
||||||
|
sel <- which(myDB$taxonomy$ID == thisData$protein$taxonomyID)
|
||||||
|
thisData$taxonomy <- myDB$taxonomy[sel, ]
|
||||||
|
|
||||||
|
# add the entries for this protein from the annotation table
|
||||||
|
sel <- which(myDB$annotation$proteinID == thisData$protein$ID)
|
||||||
|
thisData$annotation <- myDB$annotation[sel, ]
|
||||||
|
# our .json convention uses pName and fName as keys, not the db-internal IDs
|
||||||
|
# add empty columns for pName and fName
|
||||||
|
l <- nrow(thisData$annotation)
|
||||||
|
thisData$annotation$pName <- character(l)
|
||||||
|
thisData$annotation$fName <- character(l)
|
||||||
|
# get the appropriate protein and feature names
|
||||||
|
for (i in seq_len(l)) {
|
||||||
|
pID <- thisData$annotation$proteinID[i]
|
||||||
|
sel <- which(myDB$protein$ID == pID)
|
||||||
|
thisData$annotation$pName[i] <- myDB$protein$name[sel] # store pName
|
||||||
|
fID <- thisData$annotation$featureID[i]
|
||||||
|
sel <- which(myDB$feature$ID == fID)
|
||||||
|
thisData$annotation$fName[i] <- myDB$feature$name[sel] # store fName
|
||||||
|
}
|
||||||
|
|
||||||
|
# add the corresponding feature table
|
||||||
|
sel <- which(myDB$feature$ID %in% thisData$annotation$featureID)
|
||||||
|
thisData$feature <- myDB$feature[sel, ]
|
||||||
|
|
||||||
|
# remove columns that are not going into JSON output
|
||||||
|
thisData$protein$ID <- NULL
|
||||||
|
thisData$annotation$ID <- NULL
|
||||||
|
thisData$annotation$proteinID <- NULL
|
||||||
|
thisData$annotation$featureID <- NULL
|
||||||
|
thisData$feature$ID <- NULL
|
||||||
|
|
||||||
|
# create JSON-formatted output
|
||||||
|
# ( jsonlite::prettify() is too wordy for a compact Wikipage )
|
||||||
|
|
||||||
|
out <- character()
|
||||||
|
out <- c(out, '{')
|
||||||
|
|
||||||
|
out <- c(out, ' "protein": {')
|
||||||
|
sel <- colnames(thisData$protein) != "sequence"
|
||||||
|
out <- c(out, sprintf(" %s,", dbRow2JSON(thisData$protein[1, sel],
|
||||||
|
coll = ",\n ")))
|
||||||
|
out <- c(out, dbSeq2JSON(thisData$protein$sequence[1]))
|
||||||
|
out <- c(out, ' },')
|
||||||
|
|
||||||
|
out <- c(out, ' "taxonomy": {')
|
||||||
|
out <- c(out, sprintf(" %s", dbRow2JSON(thisData$taxonomy)))
|
||||||
|
out <- c(out, ' },')
|
||||||
|
|
||||||
|
out <- c(out, ' "annotation": [')
|
||||||
|
for (i in seq_len(nrow(thisData$annotation))) {
|
||||||
|
out <- c(out, sprintf(" {%s},", dbRow2JSON(thisData$annotation[i, ])))
|
||||||
|
}
|
||||||
|
out[length(out)] <- gsub(",$", "", out[length(out)]) # remove last ","
|
||||||
|
out <- c(out, ' ],')
|
||||||
|
|
||||||
|
out <- c(out, ' "feature": [')
|
||||||
|
sel <- colnames(thisData$feature) != "description"
|
||||||
|
for (i in seq_len(nrow(thisData$feature))) {
|
||||||
|
out <- c(out, sprintf(" {%s,",
|
||||||
|
dbRow2JSON(thisData$feature[i, sel])))
|
||||||
|
out <- c(out, sprintf(" %s},",
|
||||||
|
dbRow2JSON(thisData$feature[i, "description",
|
||||||
|
drop = FALSE])))
|
||||||
|
}
|
||||||
|
out[length(out)] <- gsub(",$", "", out[length(out)]) # remove last ","
|
||||||
|
out <- c(out, ' ]')
|
||||||
|
|
||||||
|
out <- c(out, '}')
|
||||||
|
|
||||||
|
return(paste0(out, collapse = "\n"))
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# == 3.15 dbSeq2JSON() =====================================================
|
||||||
|
|
||||||
|
dbSeq2JSON <- function(s, nIndents = 4, width = 70) {
|
||||||
|
# Turn a sequence into a JSON key-value pair, with the value being a JSON
|
||||||
|
# array of elements not exceeding a width of "width", and an indent of
|
||||||
|
# "indents" spaces.
|
||||||
|
ind <- paste0(rep(" ", nIndents), collapse = "")
|
||||||
|
|
||||||
|
out <- character()
|
||||||
|
out <- c(out, sprintf("%s\"sequence\" : [", ind))
|
||||||
|
|
||||||
|
for (i in seq_along(s)) {
|
||||||
|
l <- nchar(s[i])
|
||||||
|
if (l <= width) {
|
||||||
|
out <- c(out, s[i])
|
||||||
|
} else {
|
||||||
|
starts <- seq(1, l, by = width)
|
||||||
|
ends <- seq(width, l, by = width)
|
||||||
|
if (length(ends) < length(starts)) { ends <- c(ends, l) }
|
||||||
|
out <- c(out, sprintf("%s \"%s\",", ind, substring(s[i], starts, ends)))
|
||||||
|
}
|
||||||
|
}
|
||||||
|
out[length(out)] <- gsub(",$", "", out[length(out)]) # remove last ","
|
||||||
|
|
||||||
|
out <- c(out, sprintf("%s]", ind))
|
||||||
|
return(paste0(out, collapse = "\n"))
|
||||||
|
}
|
||||||
|
cat(dbSeq2JSON(myDB$protein$sequence[1]))
|
||||||
|
|
||||||
|
|
||||||
|
# == 3.16 dbRow2JSON() =====================================================
|
||||||
|
|
||||||
|
dbRow2JSON <- function(df, coll = ", ") {
|
||||||
|
# Turn a single dataframe row into JSON key value pairs, where the keys are the
|
||||||
|
# column names. Respects character / numeric mode.
|
||||||
|
out <- character()
|
||||||
|
for (i in 1:ncol(df)) {
|
||||||
|
if (class(df[1, i]) == "integer") {
|
||||||
|
val <- sprintf("%d", df[1, i])
|
||||||
|
} else if (class(df[1, i]) == "numeric") {
|
||||||
|
val <- sprintf("%f", df[1, i])
|
||||||
|
} else {
|
||||||
|
val <- sprintf("\"%s\"", as.character(df[1, i]))
|
||||||
|
}
|
||||||
|
out <- c(out, sprintf("\"%s\": %s", colnames(df)[i], val))
|
||||||
|
}
|
||||||
|
return(paste0(out, collapse = coll))
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# = 4 TESTS ===============================================================
|
||||||
|
|
||||||
|
if (doTESTS) {
|
||||||
if (! requireNamespace("testthat", quietly = TRUE)) {
|
if (! requireNamespace("testthat", quietly = TRUE)) {
|
||||||
install.packages("testthat")
|
install.packages("testthat")
|
||||||
}
|
}
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user