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Added package information code after every library() call.

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hyginn 2017-10-28 23:05:53 -04:00
parent e57db9e9a0
commit 4479fa2d4d
21 changed files with 362 additions and 245 deletions
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19
BIN-ALI-BLAST.R
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@ -23,28 +23,31 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ---------------------------------------------
#TOC> 1 Packages 41
#TOC> 2 Defining the APSES domain 50
#TOC> 3 Executing the BLAST search 72
#TOC> 4 Analysing results 94
#TOC> 1 Preparations 41
#TOC> 2 Defining the APSES domain 54
#TOC> 3 Executing the BLAST search 76
#TOC> 4 Analysing results 98
#TOC>
#TOC> ==========================================================================
# = 1 Packages ============================================================
# = 1 Preparations ========================================================
if (!require(Biostrings, quietly=TRUE)) {
source("https://bioconductor.org/biocLite.R")
biocLite("Biostrings")
library(Biostrings)
}
# Package information:
# library(help = Biostrings) # basic information
# browseVignettes("Biostrings") # available vignettes
# data(package = "Biostrings") # available datasets
# = 2 Defining the APSES domain ===========================================

15
BIN-ALI-Dotplot.R
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@ -10,27 +10,34 @@
#
# Versions:
# 0.1 First code copied from 2016 material.
#
#
# TODO:
#
#
# == DO NOT SIMPLY source() THIS FILE! =======================================
#
# If there are portions you don't understand, use R's help system, Google for an
# answer, or ask your instructor. Don't continue if you don't understand what's
# going on. That's not how it works ...
#
# ==============================================================================
# = 1 ___Section___
# First, we install and load the Biostrings package.
if (!require(Biostrings, quietly=TRUE)) {
source("https://bioconductor.org/biocLite.R")
if (! exists("biocLite")) {
source("https://bioconductor.org/biocLite.R")
}
biocLite("Biostrings")
library(Biostrings)
}
# library(help = Biostrings) # basic information
# browseVignettes("Biostrings") # available vignettes
# data(package = "Biostrings") # available datasets
# Let's load BLOSUM62
data(BLOSUM62)

27
BIN-ALI-Optimal_sequence_alignment.R
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@ -22,22 +22,21 @@
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> -------------------------------------------------------
#TOC> 1 Prepare 41
#TOC> 2 Biostrings Pairwise Alignment 49
#TOC> 2.1 Optimal global alignment 60
#TOC> 2.2 Optimal local alignment 123
#TOC> 3 APSES Domain annotation by alignment 147
#TOC> 4 Update your database script 228
#TOC> 1 Prepare 45
#TOC> 2 Biostrings Pairwise Alignment 53
#TOC> 2.1 Optimal global alignment 70
#TOC> 2.2 Optimal local alignment 133
#TOC> 3 APSES Domain annotation by alignment 157
#TOC> 4 Update your database script 238
#TOC>
#TOC> ==========================================================================
# = 1 Prepare =============================================================
# You need to recreate the protein database that you have constructed in the
@ -49,13 +48,19 @@ source("makeProteinDB.R")
# = 2 Biostrings Pairwise Alignment =======================================
if (!require(Biostrings, quietly=TRUE)) {
source("https://bioconductor.org/biocLite.R")
if (! exists("biocLite")) {
source("https://bioconductor.org/biocLite.R")
}
biocLite("Biostrings")
library(Biostrings)
}
# library(help = Biostrings) # basic information
# browseVignettes("Biostrings") # available vignettes
# data(package = "Biostrings") # available datasets
# Biostrings stores sequences in "XString" objects. Once we have onverted our
# traget sequences to AAString objects, the alignment itself is straightforward.
# Biostrings stores sequences in "XString" objects. Once we have converted our
# target sequences to AAString objects, the alignment itself is straightforward.
# == 2.1 Optimal global alignment ==========================================

31
BIN-ALI-Similarity.R
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@ -23,18 +23,17 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------
#TOC> 1 Amino Acid Properties 40
#TOC> 2 Mutation Data matrix 150
#TOC> 3 Background score 188
#TOC> 1 Amino Acid Properties 43
#TOC> 2 Mutation Data matrix 163
#TOC> 3 Background score 205
#TOC>
#TOC> ==========================================================================
# = 1 Amino Acid Properties ===============================================
@ -46,6 +45,10 @@ if (!require(seqinr)) {
install.packages("seqinr")
library(seqinr)
}
# Package information:
# library(help = seqinr) # basic information
# browseVignettes("seqinr") # available vignettes
# data(package = "seqinr") # available datasets
# A true Labor of Love has gone into the compilation of the seqinr "aaindex"
# data:
@ -128,6 +131,12 @@ if (!require(ggtern)) {
install.packages("ggtern")
library(ggtern)
}
# Package information:
# library(help = ggtern) # basic information
# browseVignettes("ggtern") # available vignettes
# data(package = "ggtern") # available datasets
# collect into data frame, normalize to (0.05, 0.95)
myDat <- data.frame("phi" = 0.9*(((Y$I-min(Y$I))/(max(Y$I)-min(Y$I))))+0.05,
@ -154,12 +163,16 @@ ggtern(data = myDat,
# The Biostrings package contains the most common mutation data matrices.
if (!require(Biostrings, quietly=TRUE)) {
source("https://bioconductor.org/biocLite.R")
if (! exists("biocLite")) {
source("https://bioconductor.org/biocLite.R")
}
biocLite("Biostrings")
library(Biostrings)
}
data(package = "Biostrings")
# Package information:
# library(help=Biostrings) # basic information
# browseVignettes("Biostrings") # available vignettes
# data(package = "Biostrings") # available datasets
# Let's load the BLOSUM62 mutation data matrix from the package
data(BLOSUM62)

17
BIN-Data_integration.R
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@ -24,18 +24,16 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> -------------------------------------------
#TOC> 1 Identifier mapping 41
#TOC> 2 Cross-referencing tables 142
#TOC> 1 Identifier mapping 45
#TOC> 2 Cross-referencing tables 151
#TOC>
#TOC> ==========================================================================
# = 1 Identifier mapping ==================================================
@ -59,6 +57,11 @@ if (!require(httr, quietly=TRUE)) {
install.packages("httr")
library(httr)
}
# Package information:
# library(help = httr) # basic information
# browseVignettes("httr") # available vignettes
# data(package = "httr") # available datasets
# We will walk through the process with the refSeqID
# of yeast Mbp1 and Swi4, and we will also enter a dummy ID to check what
@ -68,7 +71,7 @@ myQueryIDs <- "NP_010227 NP_00000 NP_011036"
# The UniProt ID mapping service API is very straightforward to use: just define
# the URL of the server and send a list of items labelled as "query" in the body
# of the request.
# of the request. GET() and POST() are functions from httr.
URL <- "http://www.uniprot.org/mapping/"
response <- POST(URL,

6
BIN-PHYLO-Tree_building.R
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@ -39,9 +39,15 @@ if (!require(Rphylip, quietly=TRUE)) {
install.packages("Rphylip")
library(Rphylip)
}
# Package information:
# library(help = Rphylip) # basic information
# browseVignettes("Rphylip") # available vignettes
# data(package = "Rphylip") # available datasets
# This will install RPhylip, as well as its dependency, the package "ape".
# The next part may be tricky. You will need to figure out where
# on your computer Phylip has been installed and define the path
# to the proml program that calculates a maximum-likelihood tree.

12
BIN-PPI-Analysis.R
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@ -154,11 +154,17 @@ ENSPsel
# day), simply a few lines of sample code to get you started on the specific use
# case of retrieving descriptions for ensembl protein IDs.
if (!require(biomaRt)) {
source("http://bioconductor.org/biocLite.R")
if (!require(biomaRt, quietly=TRUE)) {
if (! exists("biocLite")) {
source("https://bioconductor.org/biocLite.R")
}
biocLite("biomaRt")
library("biomaRt")
library(biomaRt)
}
# Package information:
# library(help = biomaRt) # basic information
# browseVignettes("biomaRt") # available vignettes
# data(package = "biomaRt") # available datasets
# define which dataset to use ...
myMart <- useMart("ensembl", dataset="hsapiens_gene_ensembl")

5
BIN-SEQA-Comparison.R
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@ -32,8 +32,11 @@ if (!require(seqinr, quietly=TRUE)) {
install.packages("seqinr")
library(seqinr)
}
# Package information:
# library(help = seqinr) # basic information
# browseVignettes("seqinr") # available vignettes
# data(package = "seqinr") # available datasets
help(package = seqinr) # shows the available functions
# Let's try a simple function
?computePI

52
BIN-Sequence.R
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@ -23,26 +23,29 @@
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------
#TOC> 1 Prepare 52
#TOC> 2 Storing Sequence 66
#TOC> 3 String properties 95
#TOC> 4 Substrings 102
#TOC> 5 Creating strings: sprintf() 108
#TOC> 6 Changing strings 139
#TOC> 6.1 stringi and stringr 191
#TOC> 6.2 dbSanitizeSequence() 201
#TOC> 7 Permuting and sampling 213
#TOC> 7.1 Permutations 220
#TOC> 7.2 Sampling 263
#TOC> 7.2.1 Equiprobable characters 265
#TOC> 7.2.2 Defined probability vector 300
#TOC> 8 Tasks 328
#TOC>
#TOC> 1 Prepare 56
#TOC> 2 Storing Sequence 74
#TOC> 3 String properties 103
#TOC> 4 Substrings 110
#TOC> 5 Creating strings: sprintf() 116
#TOC> 6 Changing strings 147
#TOC> 6.1 stringi and stringr 199
#TOC> 6.2 dbSanitizeSequence() 209
#TOC> 7 Permuting and sampling 221
#TOC> 7.1 Permutations 228
#TOC> 7.2 Sampling 271
#TOC> 7.2.1 Equiprobable characters 273
#TOC> 7.2.2 Defined probability vector 313
#TOC> 8 Tasks 341
#TOC>
#TOC> ==========================================================================
#
#
#
@ -54,13 +57,17 @@
# Much basic sequence handling is supported by the Bioconductor package
# Biostrings.
if (! require(Biostrings)) {
if (! require(Biostrings, quietly=TRUE)) {
if (! exists("biocLite")) {
source("https://bioconductor.org/biocLite.R")
}
biocLite("Biostrings")
library(Biostrings)
}
# Package information:
# library(help = Biostrings) # basic information
# browseVignettes("Biostrings") # available vignettes
# data(package = "Biostrings") # available datasets
# = 2 Storing Sequence ====================================================
@ -262,7 +269,7 @@ 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:
@ -280,10 +287,15 @@ sum(table(v)[c("G", "C")]) # 51 is close to expected
# What's the number of CpG motifs? Easy to check with the stringi
# stri_match_all() function
if (! require(stringi)) {
if (! require(stringi, quietly=TRUE)) {
install.packages("stringi")
library(stringi)
}
# Package information:
# library(help = stringi) # basic information
# browseVignettes("stringi") # available vignettes
# data(package = "stringi") # available datasets
(x <- stri_match_all(mySeq, regex = "CG"))
length(unlist(x))
@ -297,7 +309,7 @@ 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

76
BIN-Storing_data.R
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@ -24,36 +24,35 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ------------------------------------------------------------
#TOC> 1 A Relational Datamodel in R: review 58
#TOC> 1.1 Building a sample database structure 98
#TOC> 1.1.1 completing the database 209
#TOC> 1.2 Querying the database 244
#TOC> 1.3 Task: submit for credit (part 1/2) 273
#TOC> 2 Implementing the protein datamodel 285
#TOC> 2.1 JSON formatted source data 311
#TOC> 2.2 "Sanitizing" sequence data 346
#TOC> 2.3 Create a protein table for our data model 366
#TOC> 2.3.1 Initialize the database 368
#TOC> 2.3.2 Add data 380
#TOC> 2.4 Complete the database 400
#TOC> 2.4.1 Examples of navigating the database 427
#TOC> 2.5 Updating the database 459
#TOC> 3 Add your own data 471
#TOC> 3.1 Find a protein 479
#TOC> 3.2 Put the information into JSON files 508
#TOC> 3.3 Create an R script to create the database 531
#TOC> 3.3.1 Check and validate 551
#TOC> 3.4 Task: submit for credit (part 2/2) 592
#TOC>
#TOC> ==========================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------------
#TOC> 1 A Relational Datamodel in R: review 62
#TOC> 1.1 Building a sample database structure 102
#TOC> 1.1.1 completing the database 213
#TOC> 1.2 Querying the database 248
#TOC> 1.3 Task: submit for credit (part 1/2) 277
#TOC> 2 Implementing the protein datamodel 289
#TOC> 2.1 JSON formatted source data 315
#TOC> 2.2 "Sanitizing" sequence data 355
#TOC> 2.3 Create a protein table for our data model 375
#TOC> 2.3.1 Initialize the database 377
#TOC> 2.3.2 Add data 389
#TOC> 2.4 Complete the database 409
#TOC> 2.4.1 Examples of navigating the database 436
#TOC> 2.5 Updating the database 468
#TOC> 3 Add your own data 480
#TOC> 3.1 Find a protein 488
#TOC> 3.2 Put the information into JSON files 517
#TOC> 3.3 Create an R script to create your own database 540
#TOC> 3.3.1 Check and validate 560
#TOC> 3.4 Task: submit for credit (part 2/2) 601
#TOC>
#TOC> ==========================================================================
# = 1 A Relational Datamodel in R: review =================================
@ -206,7 +205,7 @@ 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:
@ -331,10 +330,15 @@ file.edit("./data/MBP1_SACCE.json")
# Let's load the "jsonlite" package and have a look at how it reads this data.
if (! require("jsonlite", quietly = TRUE)) {
if (! require(jsonlite, quietly=TRUE)) {
install.packages("jsonlite")
library(jsonlite)
}
# Package information:
# library(help = jsonlite) # basic information
# browseVignettes("jsonlite") # available vignettes
# data(package = "jsonlite") # available datasets
x <- fromJSON("./data/MBP1_SACCE.json")
str(x)
@ -365,7 +369,7 @@ 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
@ -377,7 +381,7 @@ 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
@ -424,7 +428,7 @@ 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
@ -528,10 +532,10 @@ myDB$taxonomy$species[sel]
# - Validate your two files online at https://jsonlint.com/
# == 3.3 Create an R script to create the database =========================
# == 3.3 Create an R script to create your own database ====================
# Next: to create the database.
# Next: to create your own database.
# - Make a new R script, call it "makeProteinDB.R"
# - enter the following expression as the first command:
# source("./scripts/ABC-createRefDB.R")
@ -548,7 +552,7 @@ 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.

26
FND-Genetic_code.R
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@ -22,22 +22,21 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------------------
#TOC> 1 Storing the genetic code 43
#TOC> 1.1 Genetic code in Biostrings 61
#TOC> 2 Working with the genetic code 88
#TOC> 2.1 Translate a sequence. 117
#TOC> 3 An alternative representation: 3D array 199
#TOC> 3.1 Print a Genetic code table 232
#TOC> 4 Tasks 258
#TOC> 1 Storing the genetic code 47
#TOC> 1.1 Genetic code in Biostrings 65
#TOC> 2 Working with the genetic code 97
#TOC> 2.1 Translate a sequence. 126
#TOC> 3 An alternative representation: 3D array 208
#TOC> 3.1 Print a Genetic code table 241
#TOC> 4 Tasks 267
#TOC>
#TOC> ==========================================================================
# = 1 Storing the genetic code ============================================
@ -64,13 +63,18 @@ x["TAA"]
# available in the Bioconductor "Biostrings" package:
if (! require(Biostrings)) {
if (! require(Biostrings, quietly=TRUE)) {
if (! exists("biocLite")) {
source("https://bioconductor.org/biocLite.R")
}
biocLite("Biostrings")
library(Biostrings)
}
# Package information:
# library(help = Biostrings) # basic information
# browseVignettes("Biostrings") # available vignettes
# data(package = "Biostrings") # available datasets
# The standard genetic code vector
GENETIC_CODE

34
FND-MAT-Graphs_and_networks.R
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@ -23,26 +23,25 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ------------------------------------------------------
#TOC> 1 Review 48
#TOC> 2 DEGREE DISTRIBUTIONS 192
#TOC> 2.1 Random graph 198
#TOC> 2.2 scale-free graph (Barabasi-Albert) 242
#TOC> 2.3 Random geometric graph 304
#TOC> 3 A CLOSER LOOK AT THE igraph PACKAGE 424
#TOC> 3.1 Basics 427
#TOC> 3.2 Components 499
#TOC> 4 RANDOM GRAPHS AND GRAPH METRICS 518
#TOC> 4.1 Diameter 553
#TOC> 5 GRAPH CLUSTERING 621
#TOC> 1 Review 52
#TOC> 2 DEGREE DISTRIBUTIONS 201
#TOC> 2.1 Random graph 207
#TOC> 2.2 scale-free graph (Barabasi-Albert) 251
#TOC> 2.3 Random geometric graph 313
#TOC> 3 A CLOSER LOOK AT THE igraph PACKAGE 433
#TOC> 3.1 Basics 436
#TOC> 3.2 Components 508
#TOC> 4 RANDOM GRAPHS AND GRAPH METRICS 527
#TOC> 4.1 Diameter 562
#TOC> 5 GRAPH CLUSTERING 630
#TOC>
#TOC> ==========================================================================
# = 1 Review ==============================================================
@ -121,10 +120,15 @@ set.seed(112358)
# standard package for work with graphs in r is "igraph". We'll go into more
# details of the igraph package a bit later, for now we just use it to plot:
if (!require(igraph)) {
if (! require(igraph, quietly=TRUE)) {
install.packages("igraph")
library(igraph)
}
# Package information:
# library(help = igraph) # basic information
# browseVignettes("igraph") # available vignettes
# data(package = "igraph") # available datasets
myG <- graph_from_adjacency_matrix(myRandAM, mode = "undirected")
set.seed(112358)

53
FND-STA-Probability_distribution.R
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@ -22,31 +22,29 @@
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------------------
#TOC> 1 Introduction 50
#TOC> 2 Three fundamental distributions 113
#TOC> 2.1 The Poisson Distribution 116
#TOC> 2.2 The uniform distribution 169
#TOC> 2.3 The Normal Distribution 189
#TOC> 3 quantile-quantile comparison 230
#TOC> 3.1 qqnorm() 240
#TOC> 3.2 qqplot() 300
#TOC> 4 Quantifying the difference 317
#TOC> 4.1 Chi2 test for discrete distributions 351
#TOC> 4.2 Kullback-Leibler divergence 435
#TOC> 4.2.1 An example from tossing dice 446
#TOC> 4.2.2 An example from lognormal distributions 568
#TOC> 4.3 Kolmogorov-Smirnov test for continuous distributions 609
#TOC>
#TOC> 1 Introduction 54
#TOC> 2 Three fundamental distributions 117
#TOC> 2.1 The Poisson Distribution 120
#TOC> 2.2 The uniform distribution 173
#TOC> 2.3 The Normal Distribution 193
#TOC> 3 quantile-quantile comparison 234
#TOC> 3.1 qqnorm() 244
#TOC> 3.2 qqplot() 304
#TOC> 4 Quantifying the difference 321
#TOC> 4.1 Chi2 test for discrete distributions 355
#TOC> 4.2 Kullback-Leibler divergence 446
#TOC> 4.2.1 An example from tossing dice 457
#TOC> 4.2.2 An example from lognormal distributions 579
#TOC> 4.3 Kolmogorov-Smirnov test for continuous distributions 620
#TOC>
#TOC> ==========================================================================
# = 1 Introduction ========================================================
# The space of possible outcomes of events is called a probability distribution
@ -372,12 +370,19 @@ myBreaks <- c(myBreaks, maxX) # ... and one that contains the outliers
hist(rG1.5, breaks = myBreaks, col = myCols[4])
# ... but basic R has no inbuilt function to stack histogram bars side-by-side.
# We use the multhist() function in the plotrix package:
# We use the multhist() function in the plotrix package: check out the
# package information - plotrix has _many_ useful utilities to enhance
# plots or produce informative visualizations.
if (!require(plotrix)) {
if (! require(plotrix, quietly=TRUE)) {
install.packages("plotrix")
library(plotrix)
}
# Package information:
# library(help = plotrix) # basic information
# browseVignettes("plotrix") # available vignettes
# data(package = "plotrix") # available datasets
h <- multhist(list(rL1, rL2, rG1.2, rG1.5, rG1.9 ),
breaks = myBreaks,
@ -436,14 +441,14 @@ chisq.test(countsL1, countsG1.9, simulate.p.value = TRUE, B = 10000)
# For discrete probability distributions, there is a much better statistic, the
# Kullback-Leibler divergence (or relative entropy). It is based in information
# theory, and evaluates how different each matching pair of outcomem categories
# theory, and evaluates how different the matched pairs of outcome categories
# are. Its inputs are the probability mass functions (p.m.f.) of the two
# functions to be compared. A probability mass function is the probability of
# every outcome the process can have. Kullback-Leibler divergence therefore can
# 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
@ -565,7 +570,7 @@ 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:

42
RPR-Biostrings.R
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@ -23,27 +23,26 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ---------------------------------------------------------
#TOC> 1 The Biostrings package 53
#TOC> 2 Getting Data into Biostrings Objects 82
#TOC> 3 Working with Biostrings Objects 102
#TOC> 3.1 Properties 105
#TOC> 3.2 Subsetting 142
#TOC> 3.3 Operators 154
#TOC> 3.4 Transformations 161
#TOC> 4 Getting Data out of Biostrings Objects 168
#TOC> 5 More 177
#TOC> 5.1 Views 179
#TOC> 5.2 Iranges 191
#TOC> 5.3 StringSets 197
#TOC> 1 The Biostrings package 57
#TOC> 2 Getting Data into Biostrings Objects 91
#TOC> 3 Working with Biostrings Objects 111
#TOC> 3.1 Properties 114
#TOC> 3.2 Subsetting 151
#TOC> 3.3 Operators 163
#TOC> 3.4 Transformations 170
#TOC> 4 Getting Data out of Biostrings Objects 177
#TOC> 5 More 186
#TOC> 5.1 Views 188
#TOC> 5.2 Iranges 200
#TOC> 5.3 StringSets 206
#TOC>
#TOC> ==========================================================================
# This is a very brief introduction to the biostrings package, other units will
@ -55,15 +54,20 @@
# First, we install and load the Biostrings package from bioconductor
if (!require(Biostrings, quietly=TRUE)) {
source("https://bioconductor.org/biocLite.R")
if (! require(Biostrings, quietly=TRUE)) {
if (! exists("biocLite")) {
source("https://bioconductor.org/biocLite.R")
}
biocLite("Biostrings")
library(Biostrings)
}
# Examine the ackage information:
library(help = Biostrings) # basic information
browseVignettes("Biostrings") # available vignettes
data(package = "Biostrings") # available datasets
# This is a large collection of tools ...
help(package = "Biostrings")
# At its core, Biostrings objects are "classes" of type XString (you can think
# of a "class" in R as a special kind of list), that can take on particular

34
RPR-Genetic_code_optimality.R
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@ -22,25 +22,24 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------------------------
#TOC> 1 Designing a computational experiment 53
#TOC> 2 Setting up the tools 69
#TOC> 2.1 Natural and alternative genetic codes 72
#TOC> 2.2 Effect of mutations 126
#TOC> 2.2.1 reverse-translate 137
#TOC> 2.2.2 Randomly mutate 162
#TOC> 2.2.3 Forward- translate 187
#TOC> 2.2.4 measure effect 205
#TOC> 3 Run the experiment 252
#TOC> 4 Task solutions 339
#TOC> 1 Designing a computational experiment 57
#TOC> 2 Setting up the tools 73
#TOC> 2.1 Natural and alternative genetic codes 76
#TOC> 2.2 Effect of mutations 135
#TOC> 2.2.1 reverse-translate 146
#TOC> 2.2.2 Randomly mutate 171
#TOC> 2.2.3 Forward- translate 196
#TOC> 2.2.4 measure effect 214
#TOC> 3 Run the experiment 261
#TOC> 4 Task solutions 348
#TOC>
#TOC> ==========================================================================
# This unit demonstrates R code to simulate alternate genetic codes and evaluate
@ -71,14 +70,19 @@
# == 2.1 Natural and alternative genetic codes =============================
# Load the code from the Biostrings package
if (! require(Biostrings)) {
# Load genetic code tables from the Biostrings package
if (! require(Biostrings, quietly=TRUE)) {
if (! exists("biocLite")) {
source("https://bioconductor.org/biocLite.R")
}
biocLite("Biostrings")
library(Biostrings)
}
# Package information:
# library(help = Biostrings) # basic information
# browseVignettes("Biostrings") # available vignettes
# data(package = "Biostrings") # available datasets
# There are many ways to generate alternative codes. The simplest way is to
# randomly assign amino acids to codons. A more sophisticated way is to keep the

20
RPR-PROSITE_POST.R
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@ -23,27 +23,33 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ---------------------------------------------------------------
#TOC> 1 Constructing a POST command from a Web query 40
#TOC> 1.1 Task - fetchPrositeFeatures() function 134
#TOC> 2 Task solutions 142
#TOC> 1 Constructing a POST command from a Web query 44
#TOC> 1.1 Task - fetchPrositeFeatures() function 145
#TOC> 2 Task solutions 153
#TOC>
#TOC> ==========================================================================
# = 1 Constructing a POST command from a Web query ========================
if (!require(httr)) {
if (! require(httr, quietly=TRUE)) {
install.packages("httr")
library(httr)
}
# Package information:
# library(help = httr) # basic information
# browseVignettes("httr") # available vignettes
# data(package = "httr") # available datasets
# We have reverse engineered the Web form for a ScanProsite request, and can now
# construct a POST request. The command is similar to GET(), but we need an

55
RPR-SX-PDB.R
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@ -24,27 +24,26 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------------
#TOC> 1 Introduction to the bio3D package 59
#TOC> 2 A Ramachandran plot 148
#TOC> 3 Density plots 224
#TOC> 3.1 Density-based colours 238
#TOC> 3.2 Plotting with smoothScatter() 257
#TOC> 3.3 Plotting hexbins 272
#TOC> 3.4 Plotting density contours 291
#TOC> 3.4.1 ... as overlay on a colored grid 321
#TOC> 3.4.2 ... as filled countour 338
#TOC> 3.4.3 ... as a perspective plot 369
#TOC> 4 cis-peptide bonds 387
#TOC> 5 H-bond lengths 402
#TOC> 1 Introduction to the bio3D package 63
#TOC> 2 A Ramachandran plot 151
#TOC> 3 Density plots 227
#TOC> 3.1 Density-based colours 241
#TOC> 3.2 Plotting with smoothScatter() 260
#TOC> 3.3 Plotting hexbins 275
#TOC> 3.4 Plotting density contours 299
#TOC> 3.4.1 ... as overlay on a colored grid 333
#TOC> 3.4.2 ... as filled countour 350
#TOC> 3.4.3 ... as a perspective plot 381
#TOC> 4 cis-peptide bonds 399
#TOC> 5 H-bond lengths 414
#TOC>
#TOC> ==========================================================================
# In this example of protein structure interpretation, we ...
@ -59,16 +58,15 @@
# = 1 Introduction to the bio3D package ===================================
if(!require(bio3d)) {
install.packages("bio3d", dependencies=TRUE)
if (! require(bio3d, quietly=TRUE)) {
install.packages("bio3d")
library(bio3d)
}
# Package information:
# library(help = bio3d) # basic information
# browseVignettes("bio3d") # available vignettes
# data(package = "bio3d") # available datasets
lbio3d() # ... lists the newly installed functions,
# they all have help files associated.
# More information is available in the so-called
# "vignettes" that are distributed with most R packages:
vignette("bio3d_vignettes")
# bio3d can load molecules directly from the PDB servers, you don't _have_ to
# store them locally, but you could.
@ -273,10 +271,15 @@ abline(v = 0, lwd = 0.5, col = "#00000044")
# If we wish to approximate values in a histogram-like fashion, we can use
# hexbin()
if (!require(hexbin)) {
if (! require(hexbin, quietly=TRUE)) {
install.packages("hexbin")
library(hexbin)
}
# Package information:
# library(help = hexbin) # basic information
# browseVignettes("hexbin") # available vignettes
# data(package = "hexbin") # available datasets
myColorRamp <- colorRampPalette(c("#EEEEEE",
"#3399CC",
@ -301,10 +304,14 @@ plot(hexbin(phi, psi, xbins = 10),
# distributions. But for 2D data like or phi-psi plots, we need a function from
# the MASS package: kde2d()
if (!require(MASS)) {
if (! require(MASS, quietly=TRUE)) {
install.packages("MASS")
library(MASS)
}
# Package information:
# library(help = MASS) # basic information
# browseVignettes("MASS") # available vignettes
# data(package = "MASS") # available datasets
?kde2d
dPhiPsi <-kde2d(phi, psi,

18
RPR-UniProt_GET.R
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@ -23,18 +23,17 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> ----------------------------------------------------
#TOC> 1 UniProt files via GET 40
#TOC> 1.1 Task - fetchUniProtSeq() function 98
#TOC> 2 Task solutions 105
#TOC> 1 UniProt files via GET 44
#TOC> 1.1 Task - fetchUniProtSeq() function 107
#TOC> 2 Task solutions 114
#TOC>
#TOC> ==========================================================================
# = 1 UniProt files via GET ===============================================
@ -49,10 +48,15 @@
# a Web browser. Since this is a short and simple request, the GET verb is the
# right tool:
if (!require(httr)) {
if (! require(httr, quietly=TRUE)) {
install.packages("httr")
library(httr)
}
# Package information:
# library(help = httr) # basic information
# browseVignettes("httr") # available vignettes
# data(package = "httr") # available datasets
# The UniProt ID for Mbp1 is ...

19
RPR-Unit_testing.R
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@ -23,27 +23,30 @@
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -------------------------------------------
#TOC> 1 Unit Tests with testthat 39
#TOC> 2 Organizing your tests 148
#TOC> 3 Task solutions 173
#TOC>
#TOC> 1 Unit Tests with testthat 43
#TOC> 2 Organizing your tests 156
#TOC> 3 Task solutions 181
#TOC>
#TOC> ==========================================================================
# = 1 Unit Tests with testthat ============================================
# The testthat package supports writing and executing unit tests in many ways.
if (!require(testthat)) {
if (! require(testthat, quietly=TRUE)) {
install.packages("testthat")
library(testthat)
}
# Package information:
# library(help = testthat) # basic information
# browseVignettes("testthat") # available vignettes
# data(package = "testthat") # available datasets
# An atomic test consists of an expectation about the bahaviour of a function or
# the existence of an object. testthat provides a number of useful expectations:

28
RPR-eUtils_XML.R
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@ -23,18 +23,17 @@
# going on. That's not how it works ...
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------
#TOC> 1 Working with NCBI eUtils 40
#TOC> 1.1 Task - fetchNCBItaxData() function 149
#TOC> 2 Task solutions 156
#TOC> 1 Working with NCBI eUtils 44
#TOC> 1.1 Task - fetchNCBItaxData() function 162
#TOC> 2 Task solutions 169
#TOC>
#TOC> ==========================================================================
# = 1 Working with NCBI eUtils ============================================
@ -44,19 +43,28 @@
# To begin, we load some libraries with functions
# we need...
# httr sends and receives information via the http
# ... the package httr, which sends and receives information via the http
# protocol, just like a Web browser.
if (!require(httr, quietly=TRUE)) {
if (! require(httr, quietly=TRUE)) {
install.packages("httr")
library(httr)
}
# Package information:
# library(help = httr) # basic information
# browseVignettes("httr") # available vignettes
# data(package = "httr") # available datasets
# NCBI's eUtils send information in XML format; we
# ...plus the package xml2: NCBI's eUtils send information in XML format so we
# need to be able to parse XML.
if (!require(xml2)) {
if (! require(xml2, quietly=TRUE)) {
install.packages("xml2")
library(xml2)
}
# Package information:
# library(help = xml2) # basic information
# browseVignettes("xml2") # available vignettes
# data(package = "xml2") # available datasets

18
scriptTemplate.R
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@ -11,7 +11,7 @@
#
# ToDo:
# Notes:
#
#
# ==============================================================================
setwd("<your/project/directory>")
@ -24,10 +24,16 @@ setwd("<your/project/directory>")
# ==== PACKAGES ==============================================================
# Load all required packages.
if (!require(RUnit, quietly=TRUE)) {
install.packages("RUnit")
library(RUnit)
if (! require(seqinr, quietly=TRUE)) {
install.packages("seqinr")
library(seqinr)
}
# Package information:
# library(help = seqinr) # basic information
# browseVignettes("seqinr") # available vignettes
# data(package = "seqinr") # available datasets
# ==== FUNCTIONS =============================================================
@ -43,9 +49,9 @@ myFunction <- function(a, b=1) {
# b: ...
# Value:
# result: ...
# code ...
return(result)
}