Objectives
In this exercise, you will use a recently developed R package called RIPSeeker to identify binding regions in a small CLIP-seq dataset.
Primary Data
Ameyar-zazoua M, Rachez C, Souidi M, et al. Argonaute proteins couple chromatin silencing to alternative splicing. Nat Struct Mol Biol. 2012;19(10):998-1004.
RIPSeeker
RIPSeeker is a Bioconductor R package, and we are going to run the job using the "Rscript" utility using the script and commands file that exist in the directory you just staged. Check out the "ripseeker_script.R" file.
source("http://bioconductor.org/biocLite.R")
biocLite("RIPSeeker")
library(RIPSeeker)
bamFiles = list.files(".", "\\.bam$", recursive=TRUE, full.names=TRUE)
ripGal=combineAlignGals(bamFiles[2], genomeBuild="hg19")
ctlGal=combineAlignGals(bamFiles[1], genomeBuild="hg19")
cNAME="input"
binSize=200
genomeBuild = "hg19"
outDir <- file.path(getwd())
reverseComplement = FALSE
uniqueHit = FALSE
assignMultihits = TRUE
rerunWithDisambiguatedMultihits = TRUE
seekOut.AGO2 <- ripSeek(bamPath = bamFiles, cNAME = cNAME, reverseComplement = FALSE, genomeBuild = "hg19", \
uniqueHit = TRUE, assignMultihits = TRUE, rerunWithDisambiguatedMultihits = FALSE, binSize=binSize, outDir=outDir)
Now, before running anything, let's go through this section by section. First, we need to get the RIPSeeker packages from Bioconductor and load them into R.
> source("http://bioconductor.org/biocLite.R")
> biocLite("RIPSeeker")
> library(RIPSeeker)
Then, we need to read in the BAM files we're going to use with their paths and use the RIPSeeker (or rather GenomicRanges) function "combineAlignGals" to generate a "gapped alignment" object in R using hg19 genomic and contig coordinates.
> bamFiles = list.files(".", "\\.bam$", recursive=TRUE, full.names=TRUE)
> ripGal=combineAlignGals(bamFiles[2], genomeBuild="hg19")
> ctlGal=combineAlignGals(bamFiles[1], genomeBuild="hg19")
Now, we're ready to execute the primary RIPSeeker function, appropriately called ripSeek(). Since it takes so many options, we're going to declare a bunch of variables before executing the command, and then have the command refer back to those already-declared variables. This is what it would look like:
> binSize=NULL > cNAME="input" #Which files are input > genomeBuild = "hg19" #Genome to use > outDir <- file.path(getwd()) #Output file prefix (here, the current directory) > reverseComplement = FALSE #Do not reverse complement BAM entries > uniqueHit = FALSE #If TRUE, use only unique hits to train initial model > assignMultihits = TRUE #If TRUE and uniqueHit is TRUE, assign multi-hits to regions based on initial model > rerunWithDisambiguatedMultihits = TRUE #If TRUE and prior two options are TRUE, re-train model after multi-hit assignment > seekOut.AGO2 <- ripSeek(bamPath = bamFiles, cNAME = cNAME, reverseComplement = FALSE, genomeBuild = "hg19", \ uniqueHit = TRUE, assignMultihits = TRUE, rerunWithDisambiguatedMultihits = FALSE, binSize=binSize, outDir=outDir)
The way we're going to run this at TACC is by having the actual R commands in a script called ripseeker_script.R, which is then called by a commands file using Rscript, which is a utility to run R scripts from the command line (or, in this case, a batch job) when it can't be interactive.
$BI/bin/launcher_creator.py -n ripseeker -t 01:00:00 -j ripseeker.cmds -q development -a CCBB qsub ripseeker.sge
The output should deposit two files in your current directory:
RIPregions.gff3 seekOutput.RData
The important file is RIPregions.gff3, which is a coordinate file of the regions predicted to be enriched in the IP. Because of the extremely slimmed-down dataset we used, there is only one peak, but this pipeline (with more reads that are actually real) would produce a file that is more interesting.
Alternatives
We could have added these lines to the ripseeker_script.R file:
biocLite("ChIPpeakAnno")
library(ChIPpeakAnno)
> biomart="ensembl" #Source of genome annotations
> biomaRt_dataset="hsapiens_gene_ensembl" #Specific dataset from biomaRt source
> goAnno="org.Hs.eg.db" #Annotation database
Which would automatically annotate the resulting peak coordinates with their gene names. We didn't do this because the package that is used, ChIPpeakAnno, has quite a few dependencies and takes a while to download (maybe ~15 minutes) and run.