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bioconductor_intro.R
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bioconductor_intro.R
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#_________________BASIC DATA TYPES in Bioconductor________________________#
#1# Experimental data
# e.g. gene1 has an expression level of 7.65
#2# Metadata (more info about the experiment)
# e.g. sample1 is a 60 years old woman
#3# Annotation (data we pick up most often from a big
# corporate database that gives context to the experiment).
# e.g., gene1 is highly conserved in yeast.
# A GRanges is a datastructure for storing genomic intervals.
# Every R-user dealing with genomic data needs to master this material.
# Many entities in genomics are intervals or sets of intervals (=integers):
# Promoters, Genes, SNPs, CpG Islands,...Sequencing reads; mapped and processed.
#GenomeInfoDb( Rank = 4 )
# packageDescription("Biostrings")$Version -> Get back the pack's version.
##################1) IRanges - BASIC USAGE (Rank= 5) ######################################
#Foundation of integer range manipulation in Bioconductor.
library(IRanges)
ir1<- IRanges(start = c(1,3,5), end = c(3, 5,7))
ir1
ir2<- IRanges(start = c(1,3,5), width = 3)
ir2
start(ir1)
width(ir2)<-2
ir2
names(ir1) <- paste("A", 1:3, sep = "" )
ir1
dim(ir1) # = NULL because they all are vectors
#even though they look a bit like matrices
length(ir1) # it works.
ir1[1]
ir1["A1"]
#concatenate two IRanges
c(ir1, ir2)
length(c(ir1, ir2))
#a specific type of IRanges: a normal IRange
plotRanges <- function(x, xlim= x, main = deparse(substitute(x)),
col = "black", sep = 0.5, ...) {
height <- 1
if(is(xlim, "Ranges"))
xlim <- c(min(start(xlim)), max(end(xlim)))
bins <- disjointBins(IRanges(start(x), end(x) + 1))
plot.new()
plot.window(xlim, c(0, max(bins)*(height + sep)))
ybottom <- bins * (sep + height) - height
rect(start(x)-0.5, ybottom, end(x)+0.5, ybottom + height, col = col, ...)
title(main)
axis(1)
}
par(mfrow = c(2,1))
ir <- IRanges(start = c(1,3,7,9), end = c(4,4,8,10))
plotRanges(ir) #plot the IRange
plotRanges(reduce(ir)) #get a normal IRanges = a minimal representation of the original IRanges as a set.
plotRanges(disjoin(ir)) #create a set of disjoint (non-overlapping) intervals
#Manipulating IRanges
#Take the original ranges and produce a single new range for each of the original ranges
resize(ir, width =1, fix = "start")
plotRanges(resize(ir, width =1, fix = "start"))
resize(ir, width =1, fix = "end")
resize(ir, width =1, fix = "center")#More useful: resize them from the center of the intervals
ir1<- IRanges( start = c(1, 3, 5), width = 1)
ir2<- IRanges( start = c(4, 5, 6), width = 1)
union(ir1, ir2)
plotRanges(union(ir1, ir2))
reduce(c(ir1, ir2))
intersect(ir1, ir2)
ir1<- IRanges( start = c(1, 4, 8), end = c(3, 7, 10))
ir2<- IRanges( start = c(3, 4), width = 3)
ir1
ir2
ov <- findOverlaps(query= ir1, subject=ir2)
ov
plotRanges(ir1)
plotRanges(ir2)
countOverlaps(ir1, ir2) #a quick function that summarizes the number of overlaps
nearest(ir1, ir2) #function ++++ in genomics (e.g. in case of a region of interest, find the nearest gene) # which of these IRanges in ir1 are closer to the ones in ir2
#########################2) GenomicRanges - GRanges (Rank = 12) #################################
#Representation and manipulation of genomic intervals.
#It's very similar to IRanges with some additional stuff having to do with chromosomes.
#Chromosomes in GRanges are called seqnames.
BiocManager::install("GenomicRanges")
library(GenomicRanges)
gr <- GRanges(
seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)),
ranges = IRanges(101:110, end = 111:120, names = head(letters, 10)),
strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)),
score = 1:10,
GC = seq(1, 0, length=10))
gr
gr<- GRanges(
seqnames = Rle(c("chr1", "chr2"), c(2, 1)),
ranges = IRanges(start= c(10000,11100,20000), end = c(10300, 11500,20030)),
strand = Rle(strand(c("+", "-", "+") ) ),
score= c(10, 20, 15)
)
gr
gr= GRanges(seqnames = c("chr1"),
strand = c("+", "-", "+"),
ranges = IRanges(start = c(1,3,5), width=3))
gr
#strand can be "+" (forward : 5'--> 3'), "-" (reverse : 3' --> 5') or "*" (unknown or there's an entity present on both strands))
#Splitting GRanges objects
sp <- split(gr, rep(1:2, each=5))
split(gr, names(gr))
split(gr, seqnames(gr))
split(gr, strand(gr))
#Combining GRanges objects
c(sp[[1]], sp[[2]]) # can use append()
#Subsetting GRanges objects
#GRanges objects act like vectors of ranges
gr[2:3]
gr[2:3, "GC"]
# 2nd row replaced with the 1st row
singles <- split(gr, names(gr))
grMod <- gr
grMod[2] <- singles[[1]]
head(grMod, n=3)
#repeat, reverse, or select specific portions of GRanges object.
rep(singles[[2]], times = 3)
rev(gr) # flip it downside-up
window(gr, 3,5)
gr[IRanges(start=c(2,7), end=c(3,9))] #(2-3 & 7-9)
g <- gr[1:3]
g
g<- append(g, singles[[10]]) # insert
start(g)
range(g) # IRanges by chromosome
# Recover regions flanking the set of ranges represented
#by the GRanges object
flank(gr,5)
#get a GRanges object containing the ranges
#that include the 5 bases upstream of the ranges.
flank(gr, 5 , start=FALSE)
#get a GRanges object containing the ranges
#that include the 5 bases downstream of the ranges.
shift(g, 5) #move the ranges by a specific number of base pairs
resize(g, 30) #extend the ranges by a specified width
reduce(g) #align the ranges and merge overlapping ranges to produce a simplified set
# same as range(g).
gaps(g) #the gaps between the ranges
disjoin(g) #represent the GRanges object as a collection of non-overlapping ranges
coverage(g) #quantify the degree of overlap for all the ranges
g
g2 <- head(gr, n=2)
union(g, g2)
intersect(g, g2)
setdiff(g, g2)
# A list of available methods is discovered with
methods(class="GRanges")
promoters(gr)
seqinfo(gr) # get info about your chromosome
seqlengths(gr) = c("chr1"= 10)
seqinfo(gr)
seqlevels(gr)# see the chromosome's name
#gaps() gives us all the stuff on the X that aren't covered by a range.
gaps(gr)
seqlevels(gr)= c("chr1", "chr2")
seqnames(gr) <- c("chr1", "chr2", "chr1")
gr
sort(gr)
seqlevels(gr)= c("chr2", "chr1")
sort(gr)
genome(gr)= "hg19"
gr
seqinfo(gr)
gr2= gr
genome(gr2)= "hg18"
findOverlaps(gr, gr2)
######################## GenomicRanges - BASIC GRanges Usage ########################
#Dataframe (works like classical R dataframe), it allows many types of objects of arbitrary type.
#It allows storing IRanges inside them.
ir= IRanges(start = 1:3, width = 2)
df <- DataFrame(ir=ir, score=rnorm(3))
df
df[1,1]
df[2,1]
df[3,1]
df[1,2]
df[2,2]
df[3,2]
df$ir
df$score
##Let's return to GRanges.
gr
# add 1 or more metadata columns to the GRanges object.
values(gr)= DataFrame(score=rnorm(3), expression= 1:3)
gr
# N.B: The metadata is like a dataframe.
# The info to the left of the | is not like a data.frame.
# e.g.: we cannot do something like gr$seqnames
# However we can do sth like gr$score
#Access the metadata columns (as a DataFrame object )
values(gr)
mcols(gr) # do the same.
values(gr)$score
mcols(gr)$expression
gr$score
gr$score[1]
gr$expression
#GRanges extracted without metadata
granges(gr)
#Extract IRanges
ranges(gr)
# add another metadata column
gr$score2 = gr$score/2
gr
# add again another metadata column
gr$promoter = c("a","b", "c")
##The main work horse of the GRanges class and the IRanges ecosystem: is the findOverlaps().
gr2<- GRanges(seqnames = c("chr1", "chr2", "chr1"), ranges = IRanges(start=c(1,3,5), width=3), strand = "*")
findOverlaps(gr, gr2)
# see one-to-one overlaps between peaks and CpG islands.
#It returns a matrix showing which peak overlaps which CpG island.
countOverlaps(gr, gr2)
# tabulates the number of overlaps for each element in the query.
#ignore.strand = TRUE allows elements on the + strand to overlap elements on the - strand.
findOverlaps(gr, gr2, ignore.strand = TRUE)
# ignore.strand When set to TRUE, the strand information is ignored in the overlap calculations.
# A BIT MORE OF EXPLANATIONS :
#For set operations: If set to TRUE, then the strand of x and y is set to "*" prior to any computation.
#For parallel set operations: If set to TRUE, the strand information is ignored in the computation and the result has the strand information of x.
#select only ranges from the GRanges that overlap some other elements.
subsetByOverlaps(gr, gr2)
subsetByOverlaps(gr2, gr)
# returns a subsetted query containing only those
#elements overlapped by at least one element in subject
#Convert a classic-R dataframe into GRanges:
df <- data.frame(chr = "chr1", start= 1:3, end= 4:6, score = rnorm(3))
df
makeGRangesFromDataFrame(df) # 'score' column will be missed here
makeGRangesFromDataFrame(df, keep.extra.columns = T) #SOLUTION
###seqinfo
library("GenomicRanges")
gr<-GRanges(seqnames = c("chr1", "chr2"),
ranges = IRanges(start = 1:2, end = 4:5))
gr
# remove a X
dropSeqlevels(gr, "chr2", pruning.mode = "coarse")
# keep a X
keepSeqlevels(gr, "chr2",pruning.mode = "coarse")
#keep only Standard X
gr<-GRanges(seqnames = c("chr1", "chrU2"),
ranges = IRanges(start = 1:2, end = 4:5))
keepStandardChromosomes(gr, pruning.mode = "coarse")
#Change the X' names
gr<-GRanges(seqnames = c("chr1", "chr2"),
ranges = IRanges(start = 1:2, end = 4:5))
newStyle <- mapSeqlevels(seqlevels(gr), "NCBI")
newStyle
gr <- renameSeqlevels(gr, newStyle)
gr
######################3) AnnotationHub (Rank=55) ##################################
# AnnotationHub is a wonderful resource for accessing genomic data or
#querying a large collection of whole genome resources, including ENSEMBL,
#UCSC, ENCODE, Broad Institute, KEGG, NIH Pathway Interaction Database, etc.
#This package is an interface to a lot of different online resources.
#The idea is to create a hub which is a local database of a lot of different
# online data.
#You take this local database, you query it, and you figure out which data
# do you want and then you go online and you retrieve them.
#This is a scalable way to access tons of different data.
# If working offline, add argument localHub=TRUE.
# to work with a local, non-updated hub.
# You gotta Only resources available that have previously been downloaded.
BiocManager::install(c("AnnotationHub"))
library(AnnotationHub)
Sys.setenv(TZ='GMT')
ah= AnnotationHub()
ah
unique(ah$dataprovider)
head(unique(ah$species))
ah[1]
unique(ah$rdataclass)
unique(ah$genome)
length(ah)
#To search this database you can use the subset() for example.
# If you work on human data:
ah<- subset(ah, species== "Homo sapiens")
ah
#query(): search a term in all the different components of the database
#e.g. if you wanna query the AnnotationHub to find a specific histonmodification.
query(ah, "H3K4me3")
#Find it within a specific cell line.
query(ah, c("H3K4me3", "Gm12878"))
ah2 <- display(ah)
# get an idea of the different files available
table(ah$sourcetype)
############# Usercase: AnnotationHub and GRanges ##############
library("AnnotationHub")
ahub = AnnotationHub()
ahub = subset(ahub, species == "Homo sapiens")
qhs = query(ahub, c("H3K4me3", "Gm12878"))
qhs
# H3K4me3 (H34K trimethylation) is a histone mark found inside
# promoters of genes and associated with gene expression activation.
BiocManager::install("rtracklayer")
library("rtracklayer")
gr1 = qhs[[2]]
gr1 # BroadPeaks
gr2=qhs[[4]]
gr2 # NarrowPeak
#In narrowpeaks (5-20 million reads) the regions bound are pretty much limited.
#In broadpeaks (20-60 million reads) the regions can be much wider.
summary(width(gr1))
summary(width(gr2))
which(width(gr1)== 10)
gr1[6980]
table(width(gr2))
peaks<- gr2 # narrowPeak
# ChIP-seq peaks are the sites where DNA-binding proteins interact with DNA.
# These proteins could be:
# 1) Transcription factors,
# 2) Chromatin-modifying enzymes,
# 3) Modified histones interacting with genomic DNA,
# 4) Components of the basal transcriptional machinery (e.g., RNA polymerase II).
#[beyzal]
#We're gonna answer if these peaks are enriched in promoters.
#we need to get the promoters.
#Promoter = is a small interval of ~ 2KB
# around the trscrpt° start side (TSS) of genes.
#We're gonna get the TSS of genes.
#The way to do that is to use a transcript database object (TSDB).
# But instead we're gonna get gene annotation using an AnnotationHub.
#We're gonna go for a specific type of gene annotation called RefSeq.
#RefSeq gives a highly curated (organized, selected) set of validated genes.
qhs = query(ahub, "RefSeq")
qhs
qhs$genome
cache(qhs[1]) <- NULL #remove the cached file to download that sh*t below.
genes= qhs[[1]]
genes
#RefSeq is very conservative.
#how many transcripts do we have per gene.
#how often I see a single transcript, 2 and so on and so forth.
#there's about 1k genes that has multiple transcripts.
table(table(genes$name))
# 2 transcripts from the same gene are gonna have the same name.
length(genes$name) # 50066 transcripts of different genes
length(unique(genes$name)) # only 46340 genes
# looking for genes with several transcripts
tail(table(genes$name), 100)
# e.g.: how many transcripts do NR_110886 & NR_110999 have?
which(genes$name== "NR_110886") # 3 transcripts = 37341 37350 37355
which(genes$name== "NR_110999") # 2 transcripts = 21384 21414
prom <- promoters(genes)
#how wide are these promoters ?
table(width(prom)) # promoters' size = 2.2 kb
args(promoters)
peaks
seqnames(peaks)
# we're gonna ask if these histone modification peaks
#are enriched in promoters.
# To do that, we need to know how often they overlap.
ov=findOverlaps(prom, peaks)
ov
# number of promoters that have a peak in them.
length(unique(queryHits(ov)))#9820
# number of peaks that have a promoter in them.
length(unique(subjectHits(ov))) #9850
# out of my peaks, how many overlap a promoter
length(subsetByOverlaps(peaks, prom, ignore.strand = TRUE))
#9850
length(subsetByOverlaps(prom, peaks, ignore.strand = TRUE))
#9820
# Percentage of peaks that overlap a promoter
length(subsetByOverlaps(peaks, prom, ignore.strand = TRUE))/ length(peaks)
#0.132268 ----> 13.2268 %
# H3K4me3 is a histone mark that doesn't just mark actual promoters.
#It's also sometimes associated with enhancers and other regulatory elements.
# How many promoters have a peak in them.
length(subsetByOverlaps(prom,peaks, ignore.strand = TRUE))/ length(prom)
#0.1708539 ----> 17.08539 %
# Human genome = 3.2 billion base pairs = 3.10^9 base pairs
# How many Megabases do the peaks call ?
sum(width(reduce(peaks, ignore.strand = TRUE))) #11190440
sum(width(reduce(peaks, ignore.strand = TRUE)))/ 10^6
# 11.19044 Mb
# How many Megabases do the promoters call ?
sum(width(reduce(prom, ignore.strand = TRUE)))/ 10^6
# 108.3146 Mb
# How big is the overlap ?
sum(width(intersect(peaks, prom, ignore.strand = TRUE)))/ 10^6
#1.356499 Mb
# N.B: a base can be either in a peak and in a promoter or can be in a peak and not
#in a promoter and vice versa.
# We're gonna quantify the relationship between the things.
#look for is there some kinda significant enrichment here.
inOut <- matrix(0, ncol= 2, nrow = 2)
colnames(inOut) <- c("in", "out")
rownames(inOut) <- c("in", "out")
inOut[1,1] <- sum(width(intersect(peaks, prom, ignore.strand = TRUE)))
inOut[1,2] <- sum(width(setdiff(peaks, prom, ignore.strand = TRUE)))
inOut[2,1] <- sum(width(setdiff(prom, peaks , ignore.strand = TRUE)))
inOut
?setdiff
colSums(inOut)
rowSums(inOut)
inOut[2,2] = 3*10^9 - sum(inOut)
inOut
#setdiff(): is used to find the elements which are in the first Object
# but not in the second ( asymmetric difference ).
fisher.test(inOut)$statistic
oddsRatio = inOut[1,1] * inOut[2,2] / (inOut[2,1] * inOut[1,2] )
oddsRatio #3.716635
#oddsRatio is a number between 0 & Inf.
# if > 1 ----> enrichment
#In this case that means that the overlap between the peaks and the promoters is like
# 3 fold more enriched than we would expect.
################################4) Biostrings (Rank= 10) ############################
#This is a package that contains functionality for representing and
# manipulating biological strings (DNA strings, RNA strings, or amino acids)
# and biodata.
library("Biostrings")
dna1<- DNAString("ACGT-T")
dna1
dna2<- DNAStringSet(c("ACG", "ACGT", "ACGTT"))
dna2
nchar(dna1 ) # count the number of nucltd. in a string.
nchar(dna2 )
IUPAC_CODE_MAP
dna1[2:4]
dna2[1:2]
dna2[[3]]
(dna2[[1]])[3] # Access inside the sequence
unlist(dna2) # paste all in 1 ( convert to a DNAString object )
letter(dna1, 3) # extract a letter by its position
matchPattern(DNAString("T"),dna1)
subseq(dna1, end=3)
subseq(dna1, start=3)
subseq(dna1, 5,5 ) # extract the substring located in 5th position
names(dna2) <- paste0("sequence", 1:3)
width(dna2)
sort(dna2, decreasing=T) #reverse the sequences' order
rev(dna2) #do the same thing
rev(dna1) #Reverse clean the sequence: Biological reversion !!!
reverse(dna1) # same.
reverse(dna2) # Biological reversion for DNAStringSet !
complement(dna1)
reverseComplement(dna1)
complement(dna2)
reverseComplement(dna2)
translate(dna2)
translate(dna1)
AMINO_ACID_CODE
# The frequency of occurrence of each nucleotide
alphabetFrequency(dna1)
alphabetFrequency(dna2)
# Get the GC content for ex.
letterFrequency(dna2, letters = "GC")
dinucleotideFrequency(dna2)
trinucleotideFrequency(dna2)
oligonucleotideFrequency(dna2, 4)
consensusMatrix(dna2) # tells how many strings have a specific nucleotide
# of that persistance.
# This is useful when you build up precision weight matrcies or
# motifs for transcription factor binding sites.
#__Biostrings__Matching functionality for finding subsequences & other sequences:
library(BSgenome.Scerevisiae.UCSC.sacCer2)
dnaseq<- DNAString("ACGTACGT")
dnaseq
#If you have millions of short reads it's recommended
# to use a dedicated short read aligner such as bowtie and MAQ.
#However you gotta be able to match a small set of sequences
# to a given genome.
# Matching a single string to a single string.
matchPattern(dnaseq, Scerevisiae$chrV)
# Counting how many matches we have.
countPattern(dnaseq, Scerevisiae$chrV)
#Most of the time,
# We're not interested in matching up against a single chromosome.
# But rather in matching up against a set of chromosomes.
# For this we use:
vmatchPattern(dnaseq, Scerevisiae)
# N.B: Contrary to matchPattern(), vmatchPattern() do search both
#the + and - strands.
vcountPattern(dnaseq, Scerevisiae) # searching both strands across all chromosomes
sum(vcountPattern(dnaseq, Scerevisiae)["count"]) # 170 counts= 170 sequences
strand(vmatchPattern(dnaseq,Scerevisiae))
#The DNA sequence we are searching for
# turns out to be its own reverse complement.
dnaseq== reverseComplement(dnaseq)
complement(dnaseq)
reverseComplement(dnaseq)
# matchPDict() takes a set of sequences such as
# short reads of the same length and it builds a dictionary
# on them, and then it matches them against the full genome.
# matchPDict(), matchpattern() and vmatchPattern() all
# allow a small set of mismatches and indels,
# and is a very fast and efficient way of searching the
# genome for a small set of sequences.
# matchPWM() (precision weight matrix or sequence logo)
# allows us to search the genome for example
# for binding site for a given transcription factor.
# pairwiseAlignment() implements a classic pair-wise Alignment algorithm,
# either a global pair-wise Alignment or a local pair-wise Alignment
# like a Smith Waterman or Needleman Bush, a type algorithm.
# And it allows to map millions of reads against a short sequence
# such as a gene.
# It turns out that these local and global alignment route genes using dynamic
# programming are impossible to use when you map up against the entire genome.
# But they are still very useful even if you have a million of reads,
# As long as you align them up to a very small section of the genome (e.g.,gene).
# bowtie2 and bwa : The most commonly used programs for mapping in bioinformatics.
# Mapping: is the process of comparing each one of the reads with
#the reference genome.
# A READ: is a sequence fragment produced by a sequencing machine,
#they can be long, short, paired, single, etc
# OR MORE FORMALLY:
# A READ is a raw sequence that comes off a sequencing machine. A read may
#consist of multiple segments.
#For sequencing data,reads are indexed by the order in which they are sequenced.
# trimLRPatterns()
# trimming off specific patterns on the left and the right of a DNA string set.
# The use case here is trimming off sequence adapters.
# trimLRPatterns has a very rich set of functionality allowing indels
# and mismatches in the sequence adapters.
# Sequence Alignment Softwares (ELAND, MAQ, and Bowtie) perform the bulk
#of short read mappings to a target genome.
##--------------------------#5) BSgenome (Rank=51/2140) #----------------------------
# the full genome sequences for living organisms..
BiocManager::install(c("BSgenome"))
library("BSgenome")
available.genomes() # list all the genomes you can download directly from
# the Bioconductor website
# load a genome:
BiocManager::install("BSgenome.Scerevisiae.UCSC.sacCer2")
library(BSgenome.Scerevisiae.UCSC.sacCer2)
Scerevisiae
BSgenome.Scerevisiae.UCSC.sacCer2 # Same thing.
isCircular(Scerevisiae)
seqnames(Scerevisiae)
seqlengths(Scerevisiae)
# 2micron: is a plasmid of Saccharomyces cerevisiae that is a relatively small
#multi-copy selfish DNA element that resides in the yeast nucleus at a
#copy number of 40-60 per haploid cell.
Scerevisiae$chrI
# Compute the GC content
letterFrequency(Scerevisiae$chrI, "GC")
# GC content as percentage : 90411 / 230208 = 0.39 * 100
letterFrequency(Scerevisiae$chrI, "GC", as.prob= TRUE)*100 # ~ 40%
# GC content as percentage
letterFrequency(Scerevisiae$chrI, "AT", as.prob= TRUE)*100 # ~ 60%
dinucleotideFrequency(Scerevisiae$chrI)
# % of each dinucltd.
sum(dinucleotideFrequency(Scerevisiae$chrI, as.prob= TRUE )*100 )
alphabetFrequency(Scerevisiae$chrI)
# %A %T %C %G :
alphabetFrequency(Scerevisiae$chrI, as.prob= TRUE)*100
#Computing the GC content for the entire genome.
# we can use lapply()
# But there's a new type of apply called bsapply().
# To run bsapply you need to set up BSParams.
param <- new("BSParams", X = Scerevisiae, FUN = letterFrequency)
bsapply(param, "GC")
unlist(bsapply(param, "GC"))
#Gettting the GC content across the entire genome
sum(unlist(bsapply(param, "GC")) / sum(seqlengths(Scerevisiae)))
# ~ 0.38 = 38 %
#Checking the GC % for each single X
unlist(bsapply(param, "GC", as.prob = TRUE))
#______________________BSgenome - Views_____________________________________
library(BSgenome.Scerevisiae.UCSC.sacCer2)
dnaseq<- DNAString("ACGTACGT")
vi<- matchPattern(dnaseq, Scerevisiae$chrI)
vi
#underneath the hood, a views object is the same as an IRanges object.
ranges(vi)
#Get out the entire seq
Scerevisiae$chrI[57932:57939]
alphabetFrequency(vi)
# We can Shift the view by 10 paces for ex.
shift(vi, 10)
shift(vi, -10)
#Getting the coordinate of each IRanges object
# associated with its DNAstring.
# That allows us to easily represent many
# subsequences, such as promoters or exons.
gr <- vmatchPattern(dnaseq, Scerevisiae)
gr
vi2 <- Views(Scerevisiae, gr)
vi2
#Computing the GC content of the
#promoters in the yeast genome,
#in order to get the promoters,
# let's load up AnnotationHub:
library(AnnotationHub)
ahub <- AnnotationHub()
ahub
# Let's do a query on our AnnotationHub for
#this specific version of the genome.
query(ahub, c("Homo sapiens"))
qh <- query(ahub, c("sacCer2", "genes"))
qh
genes<- ahub[["AH7048"]]
genes
prom <- promoters(genes)
#Lots of error & warnings 'cause G ranges
# complain when it gets genome
# indices that are less than zero.
# There are some of these sequences that are
# right at the boundary of the genome.
prom
#PROMOTER= 2.2 kB
# 2k paces upstream and 200 paces downstream
# of the transcription stop site.
#Trim off the promoters by cutting off
# anything outside the sequence length
# of the genome.
prom <- trim(promoters(genes))
#Some promoters are gonna be less than 2.2 kb long.
prom
promViews <- Views(Scerevisiae, prom)
promViews
gcProm<- letterFrequency(promViews, "GC",as.prob = T)
plot(density(gcProm)) # a density plot of the GC content in the promoters
abline(v = 0.38)
#The promoters don't seem to have different GC content
# than the rest of genome.
#That may be surprising, but
#remember that the yeast genome is quite small.
#It's filled up with coding sequences and
#regulatory regions unlike the human genome.
##-------- * Run-length encoding (RLE) - GRanges -------------------
#Rle is a way of representing very long vectors (compression)
library(GenomicRanges)
rl <- Rle(c(1, 1, 1,1,1,1,2,2,2,2,2,4,4,2))
rl
runLength(rl)
runValue(rl)
#This is a way of compressing a vector. 'Cause
# We have taken 14 numbers and compressed them
# into 8 numbers.
# Convert Rle to a normal vector.
as.numeric(rl)
#Coverage is defined as the number of sample
# nucleotide bases sequence
# aligned to a specific
# locus in a reference genome.
#E.G. computing average signal
# across a set of pre-specified
# genomic regions.
# Coverage of signal across inside genome
rl
# a set of genomic regions
ir <- IRanges(start = c(2,8), width = 4)
# we wanna for ex. compute the average
# signal across a set of pre-specified genomic
# regions of a coverage vector.
aggregate(rl, ir, FUN = mean)
#[1] 1 2
vec<- as.numeric(rl)
mean(vec[2:5]) #[1] 1
mean(vec[8:11]) #[1] 2
#Construct a coverage vector out of IRanges.
ir <- IRanges(start = 1:5, width = 3)
ir
rl
#
# If you wanna visualize that sh*t, run this f**king code !
plotRanges <- function(x, xlim= x, main = deparse(substitute(x)),
col = "black", sep = 0.5, ...) {
height <- 1
if(is(xlim, "Ranges"))
xlim <- c(min(start(xlim)), max(end(xlim)))
bins <- disjointBins(IRanges(start(x), end(x) + 1))
plot.new()
plot.window(xlim, c(0, max(bins)*(height + sep)))
ybottom <- bins * (sep + height) - height
rect(start(x)-0.5, ybottom, end(x)+0.5, ybottom + height, col = col, ...)