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2020 updates - deactivate for maintenance

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hyginn 2020-09-18 21:56:30 +10:00
parent 89bdd14d1c
commit 37ef655d47
42 changed files with 447 additions and 243 deletions
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42
.init.R
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@ -1,42 +0,0 @@
# .init.R
# Functions to initialize this collection of learning units
# Boris Steipe
# ====================================================================
# Create a local copy of myScript.R if required, and not been done yet.
if (! file.exists("myScript.R") && file.exists(".tmp.R")) {
file.copy(".tmp.R", "myScript.R")
}
# If it doesn't exist yet, set up a profile:
if (! file.exists(".myProfile.R")) {
# setup profile data
cat("\nPlease enter the requested values correctly, no spaces, and\n")
cat("press <enter>.\n")
e <- readline("Please enter your UofT eMail address: ")
n <- readline("Please enter your Student Number: ")
conn <- file(".myProfile.R")
writeLines(c(sprintf("myEMail <- \"%s\"", e),
sprintf("myStudentNumber <- %d", as.numeric(n))),
conn)
close(conn)
rm(e, n, conn)
}
# Patch YFO -> MYSPE if necessary:
tmp <- readLines(".myProfile.R")
if (length(grep("^YFO", tmp)) > 0) {
idx <- grep("^YFO", tmp)
tmp[idx] <- gsub("^YFO", "MYSPE", tmp[idx])
writeLines(tmp, ".myProfile.R")
}
rm(tmp)
source(".myProfile.R")
source(".utilities.R")
file.edit("ABC-units.R")
# [End]

20
.utilities.R
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@ -181,19 +181,29 @@ fetchMSAmotif <- function(ali, mot) {
# ====== PDB ID selection ======================================================
selectPDBrep <- function(n) {
selectPDBrep <- function(n, seed = as.numeric(Sys.time())) {
# Select n PDB IDs from a list of high-resolution, non-homologous, single
# domain, single chain structure files that represent a CATH topology
# group.
# Parameters n num number of IDs to return.
# Parameters:
# n num number of IDs to return
# seed num a seed for the RNG
#
# Value: char PDB IDs
# Note: the list is loaded from an RData file in the data directory
#
# Note: the list is loaded from an RData file in the "./data" directory.
# If you use this function for a course submissio, it MUST be invoked as:
#
# selectPDBrep(n, seed = myStudentNumber)
#
# ... and myStudentNumber MUST be correctly initialized
load("./data/pdbRep.RData") # loads pdbRep
if (n > length(pdbRep)) {
stop(sprintf("You can select no more than %d IDs.", length(pdbRep)))
stop(sprintf("There are only %d PDB IDs in the table to choose from.",
length(pdbRep)))
}
set.seed(as.numeric(Sys.time()))
set.seed(seed)
return(sample(pdbRep, n))
}

42
ABC-units.R
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@ -2,11 +2,16 @@
#
# Purpose: A Bioinformatics Course: R code for learning units
#
# Version: 0.1
# Version: 4.0
#
# Date: 2017 08 18
# Date: 2020 09 16
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# V 4.0 2020 version
# V 3.0 2019 version
# V 2.0 2018 version
# V 1.0 2017 version
# V 0.1 First code
#
# TODO:
@ -14,23 +19,19 @@
#
# == HOW TO WORK WITH LEARNING UNIT FILES ======================================
#
# Expect that the learning unit files will be continuously updated.
#
# The R-scripts and datasets in this project will be continuously updated,
# and updates will be posted on GitHub. To bring your version into the latest
# state use the Git-pane (top left) and "pull" (blue downward arrow) from the
# repository. However, this will overwrite locally edited version of files.
# If you wish to edit any of the code, for example to add your own comments and
# examples, save any edited version under a different name. Otherwise you will
# have problems with git when you update the project to a new version.
# To edit code and experiment with it, for example to add your own comments and
# examples, save your edited version into the "myScripts" folder. Otherwise you
# may have problems with git when you update the project to a new version. It's
# good practice to change the filename, for example by prepending your initials.
# This helps distinguish the files you are working with e.g. in a list of
# recent files. For example if your name is Honjo Tasuku, your edited
# BIN-Sequence.R might be named HT-BIN-Sequence.R
# DO NOT SIMPLY source() THESE FILES!
# 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 ...
#
# While this file itself should not be edited by you this is YOUR project
# directory, and files that you create (notes etc.) will not be harmed when you
# pull updated version of the master, or other new files, from github.
#
# If you pull from github and get the following type of error ...
# ---------------
# error: Your local changes to the following files would be
@ -41,8 +42,11 @@
# ... then, you need to bring the offending file into its original state.
# Open the Commit window, select the file, and click on the Revert button.
#
# Of course, you can save a local copy under a different name before you revert,
# in case you want to keep your changes.
# When working with these script DO NOT SIMPLY source() THESE FILES!
# 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 ...
#
#
# ==============================================================================

12
BIN-ALI-BLAST.R
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@ -1,4 +1,10 @@
# BIN-ALI-BLAST.R
# tocID <- "BIN-ALI-BLAST.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-ALI-BLAST unit.
@ -29,13 +35,13 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ---------------------------------------------------
#TOC> 1 Defining the APSES domain 42
#TOC> 2 Executing the BLAST search 64
#TOC> 3 Analysing results 86
#TOC>
#TOC>
#TOC> ==========================================================================

12
BIN-ALI-Dotplot.R
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@ -1,4 +1,10 @@
# BIN-ALI-Dotplot.R
# tocID <- "BIN-ALI-Dotplot.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-ALI-Dotplot unit.
@ -27,12 +33,12 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------
#TOC> 1 ___Section___ 39
#TOC> 2 Tasks 187
#TOC>
#TOC>
#TOC> ==========================================================================

14
BIN-ALI-MSA.R
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@ -1,4 +1,10 @@
# BIN-ALI-MSA.R
# tocID <- "BIN-ALI-MSA.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-ALI-MSA unit.
@ -29,7 +35,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ------------------------------------------------------------------
#TOC> 1 Preparations 54
@ -47,7 +53,7 @@
#TOC> 6 Sequence Logos 546
#TOC> 6.1 Subsetting an alignment by motif 555
#TOC> 6.2 Plot a Sequence Logo 604
#TOC>
#TOC>
#TOC> ==========================================================================
@ -239,7 +245,7 @@ for (i in seq_along(highScoringRanges$lengths)) {
# - adjust the sequence names
# - convert to msaAAMultipleAlignment object
# === 4.1.1 importing an .aln file
# === 4.1.1 importing an .aln file
# The seqinr package has a function to read CLUSTAL W formatted .aln files ...
if (! requireNamespace("seqinr", quietly=TRUE)) {

8
BIN-ALI-Optimal_sequence_alignment.R
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@ -1,4 +1,10 @@
# BIN-ALI-Optimal_sequence_alignment.R
# tocID <- "BIN-ALI-Optimal_sequence_alignment.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-ALI-Optimal_sequence_alignment unit.

12
BIN-ALI-Similarity.R
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@ -1,4 +1,10 @@
# BIN-ALI-Similarity.R
# tocID <- "BIN-ALI-Similarity.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-ALI-Similarity unit.
@ -28,13 +34,13 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------
#TOC> 1 Amino Acid Properties 41
#TOC> 2 Mutation Data matrix 158
#TOC> 3 Background score 199
#TOC>
#TOC>
#TOC> ==========================================================================

12
BIN-Data_integration.R
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@ -1,4 +1,10 @@
# BIN-Data_integration.R
# tocID <- "BIN-Data_integration.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-Data_integration unit.
@ -30,12 +36,12 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -------------------------------------------------
#TOC> 1 Identifier mapping 42
#TOC> 2 Cross-referencing tables 165
#TOC>
#TOC>
#TOC> ==========================================================================

14
BIN-FUNC-Domain_annotation.R
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@ -1,4 +1,10 @@
# BIN-FUNC-Domain_annotation.R
# tocID <- "BIN-FUNC-Domain_annotation.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-FUNC-Domain_annotation unit.
@ -25,7 +31,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------------------------------
#TOC> 1 Update your database script 41
@ -34,7 +40,7 @@
#TOC> 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment 93
#TOC> 1.2 Execute and Validate 119
#TOC> 2 Plot Annotations 144
#TOC>
#TOC>
#TOC> ==========================================================================
@ -90,7 +96,7 @@
# Then SKIP the next section.
#
#
# === 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment
# === 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment
#
#
# You DO already have a file called "<MYSPE>-Annotations.json" in the

34
BIN-FUNC-Semantic_similarity.R
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@ -1,4 +1,10 @@
# BIN-FUNC_Semantic_similarity.R
# tocID <- "BIN-FUNC_Semantic_similarity.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-FUNC_Semantic_similarity unit.
@ -28,14 +34,14 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------------------------------------
#TOC> 1 Preparations: Packages, AnnotationDB, Setup 42
#TOC> 2 Fetch GO Annotations 98
#TOC> 3 Semantic Similarities 107
#TOC> 4 GO Term Enrichment in Gene Sets 125
#TOC>
#TOC>
#TOC> ==========================================================================
@ -158,27 +164,9 @@ myEnr <- GOenrichment(mySet, allGenes)
sort(myEnr$p.values) # Any significantly enriched terms? All of these are ...
#Yes: most significantly enriched is GO:0071931. What is this?
getGOTerm("GO:0071931") # ... makes sense.
#Most significantly enriched is GO:0071931. What is this?
annotate::getGOTerm("GO:0071931") # ... makes sense.
(fullSet <- myEnr$genes$`GO:0071931`) # What genes are annotated to this term?
intersect(mySet, fullSet) # These are in both sets
setdiff(mySet, fullSet) # These mySet members are not annotated to that term
setdiff(fullSet, mySet) # These are annotated to that term but not in mySet.
# ... that's the most interesting set. From a set of
# genes we have identified a function that they
# share, and that shared function has allowed us
# to identify
# What are these genes?
# Select annotations from the annotation database:
AnnotationDbi::select(org.Sc.sgd.db,
keys = setdiff(fullSet, mySet),
columns = c("COMMON", "DESCRIPTION"))
# Note that these annotations are partially redundant to several different
# aliases of the same three genes.

8
BIN-MYSPE.R
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@ -1,4 +1,10 @@
# BIN-MYSPE.R
# tocID <- "BIN-MYSPE.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-MYSPE unit

12
BIN-PHYLO-Data_preparation.R
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@ -1,4 +1,10 @@
# BIN-PHYLO-Data_preparation.R
# tocID <- "BIN-PHYLO-Data_preparation.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-PHYLO-Data_preparation unit.
@ -29,7 +35,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ---------------------------------------------------------
#TOC> 1 Preparations 44
@ -37,7 +43,7 @@
#TOC> 3 Multiple Sequence Alignment 117
#TOC> 4 Reviewing and Editing Alignments 136
#TOC> 4.1 Masking workflow 152
#TOC>
#TOC>
#TOC> ==========================================================================

12
BIN-PHYLO-Tree_analysis.R
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@ -1,4 +1,10 @@
# BIN-PHYLO-Tree_analysis.R
# tocID <- "BIN-PHYLO-Tree_analysis.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-PHYLO-Tree_analysis unit.
@ -31,7 +37,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------------------
#TOC> 1 Preparation and Tree Plot 46
@ -39,7 +45,7 @@
#TOC> 2.1 Rooting Trees 145
#TOC> 2.2 Rotating Clades 190
#TOC> 2.3 Computing tree distances 241
#TOC>
#TOC>
#TOC> ==========================================================================

20
BIN-PHYLO-Tree_building.R
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@ -1,4 +1,10 @@
# BIN-PHYLO-Tree_building.R
# tocID <- "BIN-PHYLO-Tree_building.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-PHYLO-Tree_building unit.
@ -29,7 +35,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------
#TOC> 1 Calculating Trees 46
@ -39,7 +45,7 @@
#TOC> 1.1.3 ... on Linux 96
#TOC> 1.1.4 Confirming PROMLPATH 101
#TOC> 1.2 Building a maximum likelihood tree 110
#TOC>
#TOC>
#TOC> ==========================================================================
@ -68,7 +74,7 @@ if (! requireNamespace("Rphylip", quietly = TRUE)) {
# on your computer Phylip has been installed and define the path
# to the proml program that calculates a maximum-likelihood tree.
# === 1.1.1 ... on the Mac
# === 1.1.1 ... on the Mac
# On the Mac, the standard installation places a phylip folder
# in the /Applications directory. That folder contains all the
# individual phylip programs as <name>.app files. These are not
@ -79,7 +85,7 @@ if (! requireNamespace("Rphylip", quietly = TRUE)) {
# directly to that subdirectory to find the program it needs:
# PROMLPATH <- "/Applications/phylip-3.695/exe/proml.app/Contents/MacOS"
# === 1.1.2 ... on Windows
# === 1.1.2 ... on Windows
# On Windows you need to know where the programs have been installed, and you
# need to specify a path that is correct for the Windows OS. Find the folder
# that is named "exe", and right-click to inspect its properties. The path
@ -93,12 +99,12 @@ if (! requireNamespace("Rphylip", quietly = TRUE)) {
# I have heard that your path must not contain spaces, and it is prudent to
# avoid other special characters as well.
# === 1.1.3 ... on Linux
# === 1.1.3 ... on Linux
# If you are running Linux I trust you know what to do. It's probably
# something like
# PROMLPATH <- "/usr/local/phylip-3.695/bin"
# === 1.1.4 Confirming PROMLPATH
# === 1.1.4 Confirming PROMLPATH
# Confirm that the settings are right.
PROMLPATH # returns the path
list.dirs(PROMLPATH) # returns the directories in that path

12
BIN-PPI-Analysis.R
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@ -1,4 +1,10 @@
# BIN-PPI-Analysis.R
# tocID <- "BIN-PPI-Analysis.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-PPI-Analysis unit.
@ -29,7 +35,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ---------------------------------------------------------------
#TOC> 1 Setup and data 46
@ -39,7 +45,7 @@
#TOC> 2.3 Betweenness Centrality 180
#TOC> 3 biomaRt 226
#TOC> 4 Task for submission 296
#TOC>
#TOC>
#TOC> ==========================================================================

12
BIN-SEQA-Composition.R
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@ -1,4 +1,10 @@
# BIN-SEQA-Composition.R
# tocID <- "BIN-SEQA-Composition.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-SEQA-Comparison unit
@ -29,7 +35,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------------------
#TOC> 1 Preparation 47
@ -40,7 +46,7 @@
#TOC> 3.3 Plotting log ratios 185
#TOC> 3.4 Sort by frequency 200
#TOC> 3.5 Color by amino acid type 215
#TOC>
#TOC>
#TOC> ==========================================================================

24
BIN-Sequence.R
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@ -1,4 +1,10 @@
# BIN-Sequence.R
# tocID <- "BIN-Sequence.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-Sequence unit.
@ -30,7 +36,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------------
#TOC> 1 Prepare 63
@ -50,7 +56,7 @@
#TOC> 7.2 Sampling 306
#TOC> 7.2.1 Equiprobable characters 308
#TOC> 7.2.2 Defined probability vector 350
#TOC>
#TOC>
#TOC> ==========================================================================
@ -171,16 +177,16 @@ cat(sprintf("\n%s fish", c("one", "two", "red", "blue")))
# = 6 Changing strings ====================================================
# === 6.1.1 Changing case
# === 6.1.1 Changing case
tolower(s)
toupper(tolower(s))
# === 6.1.2 Reverse
# === 6.1.2 Reverse
reverse(s)
# === 6.1.3 Change characters
# === 6.1.3 Change characters
# chartr(old, new, x) maps all characters in x that appear in "old" to the
# correpsonding character in "new."
@ -208,7 +214,7 @@ chartr(myCypher, lett, x)
# (Nb. substitution cyphers are easy to crack!)
# === 6.1.4 Substitute characters
# === 6.1.4 Substitute characters
(s <- gsub("IV", "i-v", s)) # gsub can change length, first argument is
# a "regular expression"!
@ -305,7 +311,7 @@ sum(d <= 2.5) # 276. 276 of our 10000 samples are just as bunched near the
# == 7.2 Sampling ==========================================================
# === 7.2.1 Equiprobable characters
# === 7.2.1 Equiprobable characters
# Assume you need a large random-nucleotide string for some statistical model.
# How to create such a string? sample() can easily create it:
@ -347,7 +353,7 @@ length(unlist(x))
# of the smaller number of Cs and Gs - before biology even comes into play. How
# do we account for that?
# === 7.2.2 Defined probability vector
# === 7.2.2 Defined probability vector
# This is where we need to know how to create samples with specific probability
# distributions. A crude hack would be to create a sampling source vector with

22
BIN-Storing_data.R
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@ -1,4 +1,10 @@
# BIN-Storing_data.R
# tocID <- "BIN-Storing_data.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-Storing_data unit
@ -27,7 +33,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------------------
#TOC> 1 A Relational Datamodel in R: review 57
@ -50,7 +56,7 @@
#TOC> 3.3 Create an R script to create your own database 535
#TOC> 3.3.1 Check and validate 555
#TOC> 3.4 Task: submit for credit (part 2/2) 596
#TOC>
#TOC>
#TOC> ==========================================================================
@ -205,7 +211,7 @@ str(philDB)
# go back, re-read, play with it, and ask for help. This is essential.
# === 1.1.1 completing the database
# === 1.1.1 completing the database
# Next I'll add one more person, and create the other two tables:
@ -369,7 +375,7 @@ dbSanitizeSequence(x)
# == 2.3 Create a protein table for our data model =========================
# === 2.3.1 Initialize the database
# === 2.3.1 Initialize the database
# The function dbInit contains all the code to return a list of empty
@ -381,7 +387,7 @@ myDB <- dbInit()
str(myDB)
# === 2.3.2 Add data
# === 2.3.2 Add data
# fromJSON() returns a dataframe that we can readily process to add data
@ -428,7 +434,7 @@ source("./scripts/ABC-createRefDB.R")
str(myDB)
# === 2.4.1 Examples of navigating the database
# === 2.4.1 Examples of navigating the database
# You can look at the contents of the tables in the usual way we access
@ -552,7 +558,7 @@ myDB$taxonomy$species[sel]
# in any of the JSON files. Later you will add more information ...
# === 3.3.1 Check and validate
# === 3.3.1 Check and validate
# Is your protein named according to the pattern "MBP1_MYSPE"? It should be.

12
FND-Genetic_code.R
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@ -1,4 +1,10 @@
# FND-Genetic_code.R
# tocID <- "FND-Genetic_code.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the FND-Genetic_code unit.
@ -28,7 +34,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------------------------
#TOC> 1 Storing the genetic code 45
@ -38,7 +44,7 @@
#TOC> 3 An alternative representation: 3D array 212
#TOC> 3.1 Print a Genetic code table 246
#TOC> 4 Tasks 272
#TOC>
#TOC>
#TOC> ==========================================================================

14
FND-MAT-Graphs_and_networks.R
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@ -1,4 +1,10 @@
# FND-MAT-Graphs_and_networks.R
# tocID <- "FND-MAT-Graphs_and_networks.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the FND-MAT-Graphs_and_networks unit.
@ -29,7 +35,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ------------------------------------------------------------
#TOC> 1 Review 50
@ -43,7 +49,7 @@
#TOC> 4 RANDOM GRAPHS AND GRAPH METRICS 539
#TOC> 4.1 Diameter 576
#TOC> 5 GRAPH CLUSTERING 645
#TOC>
#TOC>
#TOC> ==========================================================================
@ -280,7 +286,7 @@ plot(GBA,
vertex.color=heat.colors(max(igraph::degree(GBA)+1))[igraph::degree(GBA)+1],
vertex.size = 200 + (30 * igraph::degree(GBA)),
vertex.label = NA)
par(oPar) # restore grphics state
par(oPar) # restore graphics state
# This is a very obviously different graph! Some biological networks have
# features that look like that - but in my experience the hub nodes are usually

41
FND-STA-Information_theory.R
View File

@ -1,14 +1,21 @@
# FND-STA-Information_theory.R
# tocID <- "FND-STA-Information_theory.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the FND-STA-Information_theory unit.
#
# Version: 0.2
# Version: 0.2.1
#
# Date: 2017 MM DD
# Date: 2017 - 2019
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 0.2.1 Maintenance
# 0.2 Under development
# 0.1 First code copied from 2016 material.
#
@ -58,11 +65,33 @@ AAref["Y"] <- 0.0294
sum(AAref)
# Function to calculate Shannon entropy
H <- function(v) {
# Shannon entropy (bits)
return(-sum(v * (log(v) / log(2))))
H <- function(pmf) {
# Calculate Shannon entropy
# Parameters:
# pmf (numeric) probability mass function: a vector of states and
# associated probabilities. Each element of
# pmf must be in (0, 1] and sum(pmf) must be 1.
# Value:
# Shannon entropy in bits.
# Examples:
# H(c(A=0.25, C=0.25, G=0.25, T=0.25)) # 2 bits entropy in a random
# # nucleotide sequence
# H(1) # If all elements are the same, entropy is zero
#
if (any(pmf <= 0 | pmf > 1) || isFALSE(all.equal(1.0, sum(pmf)))) {
stop("Input is not a discrete probability distribution.")
}
H <- -sum(pmf * (log(pmf) / log(2)))
return(H)
}
# Why use all.equal()? Exact comparisons with floating point numbers are
# brittle. Consider for example:
1/6 + 1/6 + 1/6 + 1/6 + 1/6 + 1/6 == 1
print(1/6 + 1/6 + 1/6 + 1/6 + 1/6 + 1/6, digits = 22) # 0.9999999999999998889777
# all.equal() tests for _near_ equality with tolerance of ~ 1.5e-8
# Entropy of the database frequencies (in bits):
(Href <- H(AAref))

16
FND-STA-Probability_distribution.R
View File

@ -1,4 +1,10 @@
# FND-STA-Probability_distribution.R
# tocID <- "FND-STA-Probability_distribution.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the FND-STA-Probability_distribution unit.
@ -28,7 +34,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------------------------
#TOC> 1 Introduction 52
@ -45,7 +51,7 @@
#TOC> 4.2.1 An example from tossing dice 463
#TOC> 4.2.2 An example from lognormal distributions 586
#TOC> 4.3 Kolmogorov-Smirnov test for continuous distributions 629
#TOC>
#TOC>
#TOC> ==========================================================================
@ -460,7 +466,7 @@ chisq.test(countsL1, countsG1.9, simulate.p.value = TRUE, B = 10000)
# be applied to discrete distributions. But we need to talk a bit about
# converting counts to p.m.f.'s.
# === 4.2.1 An example from tossing dice
# === 4.2.1 An example from tossing dice
# The p.m.f of an honest die is (1:1/6, 2:1/6, 3:1/6, 4:1/6, 5:1/6, 6:1/6). But
# there is an issue when we convert sampled counts to frequencies, and estimate
@ -583,7 +589,7 @@ abline(v = KLdiv(rep(1/6, 6), pmfPC(counts, 1:6)), col="firebrick")
# somewhat but not drastically atypical.
# === 4.2.2 An example from lognormal distributions
# === 4.2.2 An example from lognormal distributions
# We had compared a set of lognormal and gamma distributions above, now we
# can use KL-divergence to quantify their similarity:

14
FND-STA-Significance.R
View File

@ -1,4 +1,10 @@
# FND-STA-Significance.R
# tocID <- "FND-STA-Significance.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the FND-STA-Significance unit.
@ -25,7 +31,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ------------------------------------------------------------------
#TOC> 1 Significance and p-value 43
@ -36,7 +42,7 @@
#TOC> 3 Significance by integration 198
#TOC> 4 Significance by simulation or permutation 204
#TOC> 5 Final tasks 312
#TOC>
#TOC>
#TOC> ==========================================================================
@ -100,7 +106,7 @@ print(x, digits = 22)
# curve, as a fraction of the whole.
# === 1.2.1 p-value illustrated
# === 1.2.1 p-value illustrated
# Let's illustrate. First we draw a million random values from our
# standard, normal distribution:

2
README.md
View File

@ -1,2 +1,4 @@
# ABC-units
A Bioinformatics Course: R modules for learning units
Follow the instructions in the learning unit to install your local copy of this R-project.

8
RPR-Biostrings.R
View File

@ -1,4 +1,10 @@
# RPR-Biostrings.R
# tocID <- "RPR-Biostrings.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Biostrings unit.

8
RPR-FASTA.R
View File

@ -1,4 +1,10 @@
# RPR-FASTA.R
# tocID <- "RPR-FASTA.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-FASTA unit.

12
RPR-GEO2R.R
View File

@ -1,4 +1,10 @@
# RPR_GEO2R.R
# tocID <- "RPR_GEO2R.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR_GEO2R unit.
@ -34,7 +40,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------------------------------------------
#TOC> 1 Preparations 56
@ -49,7 +55,7 @@
#TOC> 5.1 Final task: Gene descriptions 504
#TOC> 6 Improving on Discovery by Differential Expression 510
#TOC> 7 Annotation data 594
#TOC>
#TOC>
#TOC> ==========================================================================

20
RPR-Genetic_code_optimality.R
View File

@ -1,4 +1,10 @@
# RPR-Genetic_code_optimality.R
# tocID <- "RPR-Genetic_code_optimality.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Genetic_code_optimality unit.
@ -30,7 +36,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------------------------------
#TOC> 1 Designing a computational experiment 57
@ -43,7 +49,7 @@
#TOC> 2.2.4 measure effect 213
#TOC> 3 Run the experiment 260
#TOC> 4 Task solutions 356
#TOC>
#TOC>
#TOC> ==========================================================================
@ -142,7 +148,7 @@ swappedGC <- function(GC) {
# - we count the number of mutations and evaluate their severity.
# === 2.2.1 reverse-translate
# === 2.2.1 reverse-translate
# To reverse-translate an amino acid vector, we randomly pick one of its
# codons from a genetic code, and assemble all codons to a sequence.
@ -167,7 +173,7 @@ traRev <- function(s, GC) {
}
# === 2.2.2 Randomly mutate
# === 2.2.2 Randomly mutate
# To mutate, we split a codon into it's three nucleotides, then randomly replace
# one of the three with another nucleotide.
@ -192,7 +198,7 @@ randMut <- function(vC) {
# === 2.2.3 Forward- translate
# === 2.2.3 Forward- translate
traFor <- function(vC, GC) {
# Parameters:
@ -210,7 +216,7 @@ traFor <- function(vC, GC) {
}
# === 2.2.4 measure effect
# === 2.2.4 measure effect
# How do we evaluate the effect of the mutation? We'll take a simple ad hoc
# approach: we divide amino acids into hydrophobic, hydrophilic, and neutral

8
RPR-Introduction.R
View File

@ -1,4 +1,10 @@
# RPR-Introduction.R
# tocID <- "RPR-Introduction.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Introduction unit

12
RPR-PROSITE_POST.R
View File

@ -1,4 +1,10 @@
# RPR-PROSITE_POST.R
# tocID <- "RPR-PROSITE_POST.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Scripting_data_downloads unit.
@ -29,13 +35,13 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ---------------------------------------------------------------------
#TOC> 1 Constructing a POST command from a Web query 42
#TOC> 1.1 Task - fetchPrositeFeatures() function 142
#TOC> 2 Task solutions 150
#TOC>
#TOC>
#TOC> ==========================================================================

8
RPR-RegEx.R
View File

@ -1,4 +1,10 @@
# RPR-RegEx.R
# tocID <- "RPR-RegEx.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-RegEx unit

8
RPR-SX-PDB.R
View File

@ -1,4 +1,10 @@
# RPR-SX-PDB.R
# tocID <- "RPR-SX-PDB.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-SX-PDB unit.

12
RPR-UniProt_GET.R
View File

@ -1,4 +1,10 @@
# RPR-UniProt_GET.R
# tocID <- "RPR-UniProt_GET.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Scripting_data_downloads unit.
@ -28,13 +34,13 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------------------
#TOC> 1 UniProt files via GET 41
#TOC> 1.1 Task - fetchUniProtSeq() function 103
#TOC> 2 Task solutions 110
#TOC>
#TOC>
#TOC> ==========================================================================

16
RPR-Unit_testing.R
View File

@ -1,4 +1,10 @@
# RPR-Unit_testing.R
# tocID <- "RPR-Unit_testing.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Unit_testing unit.
@ -29,10 +35,10 @@
#TOC>
#TOC> Section Title Line
#TOC> -------------------------------------------------
#TOC> 1 Unit Tests with testthat 40
#TOC> 2 Organizing your tests 159
#TOC> 2.1 Testing scripts 183
#TOC> 3 Task solutions 198
#TOC> 1 Unit Tests with testthat 46
#TOC> 2 Organizing your tests 165
#TOC> 2.1 Testing scripts 189
#TOC> 3 Task solutions 204
#TOC>
#TOC> ==========================================================================

12
RPR-eUtils_XML.R
View File

@ -1,4 +1,10 @@
# RPR-eUtils_and_XML.R
# tocID <- "RPR-eUtils_and_XML.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Scripting_data_downloads unit.
@ -28,13 +34,13 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------
#TOC> 1 Working with NCBI eUtils 41
#TOC> 1.1 Task - fetchNCBItaxData() function 144
#TOC> 2 Task solutions 151
#TOC>
#TOC>
#TOC> ==========================================================================

2
scripts/ABC-createRefDB.R
View File

@ -8,7 +8,7 @@
# http://steipe.biochemistry.utoronto.ca/abc/index.php/Reference_species_for_fungi
#
# For the data model, see
# https://docs.google.com/drawings/d/1uupNvz18_FYFwyyVPebTM0CUxcJCPDQuxuIJGpjWQWg
# https://docs.google.com/presentation/d/13vWaVcFpWEOGeSNhwmqugj2qTQuH1eZROgxWdHGEMr0
# For the schema, see dbInit() in ./scripts/ABC-dbUtilities.R
#
# ==============================================================================

82
scripts/ABC-dbUtilities.R
View File

@ -1,12 +1,35 @@
# ABC-dbUtilities.R
# tocID <- "scripts/ABC-dbUtilities.R"
#
# database utilities for ABC learning units
#
# ==============================================================================
#
# ====== PACKAGES ==============================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> -------------------------------------------------
#TOC> 1 PACKAGES 32
#TOC> 2 FUNCTIONS 50
#TOC> 2.01 dbSanitizeSequence() 53
#TOC> 2.02 dbConfirmUnique() 88
#TOC> 2.03 dbInit() 106
#TOC> 2.04 dbAutoincrement() 147
#TOC> 2.05 dbAddProtein() 160
#TOC> 2.06 dbAddFeature() 180
#TOC> 2.07 dbAddTaxonomy() 199
#TOC> 2.08 dbAddAnnotation() 215
#TOC> 2.09 dbFetchUniProtSeq() 243
#TOC> 2.10 dbFetchPrositeFeatures() 267
#TOC> 2.11 node2text() 311
#TOC> 2.12 dbFetchNCBItaxData() 323
#TOC> 2.13 UniProtIDmap() 362
#TOC> 3 TESTS 399
#TOC>
#TOC> ==========================================================================
# = 1 PACKAGES ============================================================
if (! requireNamespace("jsonlite", quietly = TRUE)) {
@ -24,9 +47,10 @@ if (! requireNamespace("xml2", quietly = TRUE)) {
}
# ====== FUNCTIONS =============================================================
# = 2 FUNCTIONS ===========================================================
# == 2.01 dbSanitizeSequence() =============================================
dbSanitizeSequence <- function(s, unambiguous = TRUE) {
# Remove FASTA header lines, if any,
# flatten any structure that s has,
@ -61,6 +85,7 @@ dbSanitizeSequence <- function(s, unambiguous = TRUE) {
}
# == 2.02 dbConfirmUnique() ================================================
dbConfirmUnique <- function(x) {
# x is a vector of logicals.
# returns x if x has exactly one TRUE element.
@ -78,24 +103,27 @@ dbConfirmUnique <- function(x) {
}
# == 2.03 dbInit() =========================================================
dbInit <- function() {
# Return an empty instance of the protein database
# Open the link and study the schema:
# https://docs.google.com/presentation/d/13vWaVcFpWEOGeSNhwmqugj2qTQuH1eZROgxWdHGEMr0
db <- list()
db$version <- "1.0"
db$protein <- data.frame(
ID = numeric(),
name = character(),
RefSeqID = character(),
UniProtID = character(),
taxonomyID = numeric(),
sequence = character(),
stringsAsFactors = FALSE)
sequence = character())
db$taxonomy <- data.frame(
ID = numeric(),
species = character(),
stringsAsFactors = FALSE)
species = character())
db$annotation <- data.frame(
@ -103,21 +131,20 @@ dbInit <- function() {
proteinID = numeric(),
featureID = numeric(),
start = numeric(),
end = numeric(),
stringsAsFactors = FALSE)
end = numeric())
db$feature <- data.frame(
ID = numeric(),
name = character(),
description = character(),
sourceDB = character(),
accession = character(),
stringsAsFactors = FALSE)
accession = character())
return(db)
}
# == 2.04 dbAutoincrement() ================================================
dbAutoincrement <- function(tb) {
# Return a unique integer that can be used as a primary key
# Value:
@ -130,6 +157,7 @@ dbAutoincrement <- function(tb) {
}
# == 2.05 dbAddProtein() ===================================================
dbAddProtein <- function(db, jsonDF) {
# Add one or more protein entries to the database db.
# Parameters:
@ -142,14 +170,14 @@ dbAddProtein <- function(db, jsonDF) {
RefSeqID = jsonDF$RefSeqID[i],
UniProtID = jsonDF$UniProtID[i],
taxonomyID = jsonDF$taxonomyID[i],
sequence = dbSanitizeSequence(jsonDF$sequence[i]),
stringsAsFactors = FALSE)
sequence = dbSanitizeSequence(jsonDF$sequence[i]))
db$protein <- rbind(db$protein, x)
}
return(db)
}
# == 2.06 dbAddFeature() ===================================================
dbAddFeature <- function(db, jsonDF) {
# Add one or more feature entries to the database db.
# Parameters:
@ -161,14 +189,14 @@ dbAddFeature <- function(db, jsonDF) {
name = jsonDF$name[i],
description = jsonDF$description[i],
sourceDB = jsonDF$sourceDB[i],
accession = jsonDF$accession[i],
stringsAsFactors = FALSE)
accession = jsonDF$accession[i])
db$feature <- rbind(db$feature, x)
}
return(db)
}
# == 2.07 dbAddTaxonomy() ==================================================
dbAddTaxonomy <- function(db, jsonDF) {
# Add one or more taxonomy entries to the database db.
# Parameters:
@ -178,13 +206,13 @@ dbAddTaxonomy <- function(db, jsonDF) {
for (i in seq_len(nrow(jsonDF))) {
x <- data.frame(
ID = jsonDF$ID[i],
species = jsonDF$species[i],
stringsAsFactors = FALSE)
species = jsonDF$species[i])
db$taxonomy <- rbind(db$taxonomy, x)
}
return(db)
}
# == 2.08 dbAddAnnotation() ================================================
dbAddAnnotation <- function(db, jsonDF) {
# Add one or more annotation entries to the database db.
# Parameters:
@ -205,14 +233,14 @@ dbAddAnnotation <- function(db, jsonDF) {
proteinID = pID,
featureID = fID,
start = as.integer(jsonDF$start[i]),
end = as.integer(jsonDF$end[i]),
stringsAsFactors = FALSE)
end = as.integer(jsonDF$end[i]))
db$annotation <- rbind(db$annotation, x)
}
return(db)
}
# == 2.09 dbFetchUniProtSeq() ==============================================
dbFetchUniProtSeq <- function(ID) {
# Fetch a protein sequence from UniProt.
# Parameters:
@ -236,6 +264,7 @@ dbFetchUniProtSeq <- function(ID) {
}
# == 2.10 dbFetchPrositeFeatures() =========================================
dbFetchPrositeFeatures <- function(ID) {
# Fetch feature annotations from ScanProsite.
# Parameters:
@ -272,14 +301,14 @@ dbFetchPrositeFeatures <- function(ID) {
start = as.numeric(tokens[4]),
end = as.numeric(tokens[5]),
psID = tokens[6],
psName = tokens[7],
stringsAsFactors = FALSE))
psName = tokens[7]))
}
}
return(myFeatures)
}
# == 2.11 node2text() ======================================================
node2text <- function(doc, tag) {
# an extractor function for the contents of elements
# between given tags in an XML response.
@ -291,6 +320,7 @@ node2text <- function(doc, tag) {
}
# == 2.12 dbFetchNCBItaxData() =============================================
dbFetchNCBItaxData <- function(ID) {
# Fetch feature taxID and Organism from the NCBI.
# Parameters:
@ -329,6 +359,7 @@ dbFetchNCBItaxData <- function(ID) {
# == 2.13 UniProtIDmap() ===================================================
UniProtIDmap <- function (s, mapFrom = "P_REFSEQ_AC", mapTo = "ACC") {
# Use UniProt ID mapping service to map one or more IDs
# Parameters:
@ -351,8 +382,7 @@ UniProtIDmap <- function (s, mapFrom = "P_REFSEQ_AC", mapTo = "ACC") {
if (httr::status_code(response) == 200) { # 200: oK
myMap <- read.delim(file = textConnection(httr::content(response)),
sep = "\t",
stringsAsFactors = FALSE)
sep = "\t")
myMap <- myMap[ , c(1,3)]
colnames(myMap) <- c("From", "To")
} else {
@ -366,7 +396,7 @@ UniProtIDmap <- function (s, mapFrom = "P_REFSEQ_AC", mapTo = "ACC") {
}
# ====== TESTS =================================================================
# = 3 TESTS ===============================================================
if (FALSE) {
if (! requireNamespace("testthat", quietly = TRUE)) {

2
scripts/ABC-makeScCCnet.R
View File

@ -1,4 +1,4 @@
# ABC-makeScCCnet.R
# tocID <- "scripts/ABC-makeScCCnet.R"
#
# Create a subnetwork of high-confidence yeast genes with a "mitotic cell cycle"
# GOSlim annotation.

2
scripts/ABC-writeALN.R
View File

@ -1,4 +1,4 @@
# ABC-writeALN.R
# tocID <- "scripts/ABC-writeALN.R"
#
# ToDo: calculate consensus line
# append sequence numbers

4
scripts/ABC-writeMFA.R
View File

@ -40,7 +40,7 @@ writeMFA <- function(ali,
if (is.na(blockWidth)) {
stop("PANIC: parameter \"blockWidth\" must be numeric.")
}
if (blockWidth < 1){
if (! blockWidth > 0){
stop("PANIC: parameter \"blockWidth\" must be greater than zero.")
}
@ -105,7 +105,7 @@ writeMFA <- function(ali,
txt <- c(txt, "") # append an empty line for readability
}
writeLines(txt, con= myCon)
writeLines(txt, con = myCon)
}

31
scripts/BLAST.R
View File

@ -357,20 +357,23 @@ parseBLASTalignment <- function(hit) {
# ==== TESTS ===================================================================
# define query:
# q <- paste("IYSARYSGVDVYEFIHSTGSIMKRKKDDWVNATHI", # Mbp1 APSES domain
# "LKAANFAKAKRTRILEKEVLKETHEKVQGGFGKYQ",
# "GTWVPLNIAKQLAEKFSVYDQLKPLFDFTQTDGSASP",
# sep="")
# or ...
# q <- "NP_010227" # refseq ID
#
# test <- BLAST(q,
# nHits = 100,
# E = 0.001,
# rid = "",
# limits = "txid4751[ORGN]")
# length(test$hits)
if (FALSE) {
# define query:
q <- paste("IYSARYSGVDVYEFIHSTGSIMKRKKDDWVNATHI", # Mbp1 APSES domain
"LKAANFAKAKRTRILEKEVLKETHEKVQGGFGKYQ",
"GTWVPLNIAKQLAEKFSVYDQLKPLFDFTQTDGSASP",
sep="")
# or ...
q <- "NP_010227" # refseq ID
test <- BLAST(q,
nHits = 100,
E = 0.001,
rid = "",
limits = "txid4751[ORGN]")
str(test)
length(test$hits)
}
# [END]