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Archive for August, 2013

Software update

August 18, 2013 Leave a comment

Picards tools

The classical one ūüėȬ† Note that some changes were done for java 7 compatibility. So, after GATK, turning to java 7 as default may be on its way.

Beagle 4

Another update (r1128), not documented so far.

Minimac

The new version now use all variants in the reference panel (snp, indels, SV)

Open MP

The openmp specification 4.0 are out ! Now support Fortran2003 and prepare the support for accelerator. Note the next intel compiler version already support a large number of the new specifications.

Magma

This library now in its version 1.4 . Support for new GPUs were added as well as additional subroutines. I wish more Fortran interface were added…maybe next time !

Cuda 5.5

As previously mentioned, the last version of cuda is now available as rpm/deb package (allowing a much easier install).

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Categories: emacs, Linux, NGS

More on variant distribution with dbsnp and Vep

August 12, 2013 Leave a comment

Just a follow-up post, there are so many questions one can wonder about genome, that I thought it would be nice to elaborate a bit on the dbSNP data. So to move forward, we’ll see how to obtain sift score for the dbnsp ressources with Vep.

Install Vep

#Download VEP
wget www.ensembl.org/info/docs/variation/vep/vep_script.html -O Out && wget `gawk '/variant_effect_predictor.tar.gz/ && /latest/{split($0,T,"\"");print T[2]}' Out ` -O vep.tar.gz
#Extract it
gunzip vep.tar.gz ; tar -xvf vep.tar
#Go into the directory, create a cache folder
cd variant_effect_predictor ; mkdir .vep
#Run install, answers should be yes to ask for the use of cache file, and the number <=> bos Taurus
perl INSTALL.pl -c .vep

Note :

  • We first download Vep page
  • Then the html code is parsed, and the link to the latest Vep verions is extracted and donwloaded
  • During install, you’ll have to indicate a local vep directory (here .vep)
  • Prefer¬† a local cache file

Running Vep on vcf

#Download dbSNP vcf
wget ftp://ftp.ncbi.nih.gov/snp/organisms/cow_9913/VCF/vcf_*vcf.gz
for BTA in `seq 1 29 ` X MT
do
#Decompress
zcat vcf_chr_${BTA}.vcf.gz >$BTA.vcf
#Run Vep
perl variant_effect_predictor.pl --offline --species bos_taurus -i ${BTA}.vcf --vcf --html --sift b --dir .vep --output_file Vep${BTA}
done

Note :

  • We use the Vep in local mode, so you’ll have to declare the .vep directory explicitly “–dir .vep”
  • Output will be in vcf format (to avoid handling too many different file format)¬† “–vcf”
  • sift score are available for cow since ensembl 71 nevertheless you must ask for them in Vep “–sift b”

Location of “deleterious” variant

The vcf now have some annotations appended. We just go back to last post’s R code, but¬† wonder this time where the variations supposed to be deleterious are ?

#Open an output file
png("DeleteriousDistribution.png",1920,1080)
#split the screen in 30
par(mfrow=c(3,10))
#Create a vector of chromosomes
chr=c(seq(1:29),"X")
#run a loop on chromosomes
for( i in chr){
#Read file
Pos=read.table(paste("Vep",i,sep=""),skip=15,fill=TRUE)
#plot the snp density
plot(density(Pos$V2[grep("snp",Pos$V8,perl=TRUE)]),col="grey",main=paste("BTA : ",i,sep=""))
polygon(density(Pos$V2[grep("snp",Pos$V8,perl=TRUE)]),col=rgb(0,0,0.3,0.04))
#Add in-del line
dense=density(Pos$V2[grep("deleterious",Pos$V8,perl=TRUE)])
lines(dense$x,0.5*dense$y,col="red")
}
dev.off()

Note: A shortcut would be to download the (not so up-to-date) vcf available at ensembl ftp site. The code is essentially the same as the one used in the previous post.

And you should obtain something like the following plot. DeleteriousDistribution

Once again this is still a quick and very dirty result. I wonder if there are any good story in these graphs (I mean one story that would not instantly vanished due to assembly problem or obvious bias !).

Hourglass distribution

August 6, 2013 1 comment

Some days ago, I went through an article on mutation rate mentioning the “hourglass distribution”. The illustration of this distribution is pretty obvious from the plot in the article, with for some chromosome a pretty clear reduction in number of SNP around centromeres. I just wondered if we could also observe this phenomenon on bos taurus. So i just had a look on dbSNP138 data to have some clues.

First version


#Retrieve dbSNP
 wget ftp://ftp.ncbi.nih.gov/snp/organisms/cow_9913/VCF/vcf_*vcf.gz

And then just run the following R code.

#Open an output file
png("hourglass.png",1920,1080)
#split the screen in 30
par(mfrow=c(3,10))
#Create a vector of chromosomes
 chr=c(seq(1:29),"X")
 #run a loop on chromosomes
 for( i in chr){
 #Read file
 Pos=read.table(paste("vcf_chr_",i,".vcf.gz",sep=""),skip=15,fill=TRUE)
 #plot the snp density
 plot(density(Pos$V2),col="grey",main=paste("BTA : ",i,sep=""))
 polygon(density(Pos$V2),col=rgb(0,0,0.3,0.04))
 }
 dev.off()

Notes on code :

  • read.table is used directly on a gzipped file (very handy trick)
  • dbsnp file have a 15 lines long header, so I use the skip =15 option
  • I had some glitches while reading some files, (a problem with # of fields) option fill=TRUE, just fix it
  • Plot are nice….but polygon are even better, so I first plot the density and then add a polygon on it
  • rgb function is a simple way to obtain transparency, so after the three values for red, green and blue, I add the alpha setting to a value of 0.04

And after a while….you ‘ll obtain something like this

hourglass

I must say I was a bit disappointed. At least, there are no clear pattern as can be seen in human. All the bovine chromosomes are acrocentric  this may explain why generally no clear decrease in SNP density can be seen. The pattern observed on chromosome 12, 18 and 10 were even more surprising. I am wondering if there could be some sampling bias. Concerning the pattern on BTA23, the latter could be due to MHC, known to exhibit a great diversity. Density computation may also blur a bit things.

Second version

The basic work being done, we can try to investigate others hypotheses. As instance, are SNP and Indel distributed the same way along the genome ? With some slight changes, the code become :

#Open an output file
png("SNPandindelDistribution.png",1920,1080)
#split the screen in 30
par(mfrow=c(3,10))
#Create a vector of chromosomes
 chr=c(seq(1:29),"X")
 #run a loop on chromosomes
 for( i in chr){
 #Read file
 Pos=read.table(paste("vcf_chr_",i,".vcf.gz",sep=""),skip=15,fill=TRUE)
 #plot the snp density
 plot(density(Pos$V2[grep("snp",Pos$V8,perl=TRUE)]),col="grey",main=paste("BTA : ",i,sep=""))
 polygon(density(Pos$V2[grep("snp",Pos$V8,perl=TRUE)]),col=rgb(0,0,0.3,0.04))
 #Add in-del line
 dense=density(Pos$V2[grep("in-del",Pos$V8,perl=TRUE)])
 lines(dense$x,0.5*dense$y,col="red")
 }
dev.off()

Notes on code :

  • in dbsnp variant are coded either as snp or in-del, we extract line with the grep function accordingly
  • I tweaked a bit the indel line in order to avoid scale problems.

SNPandindelDistributionWe observe roughly the same pattern between snp and indel, albeit indel distribution may be smoother. I was expecting some discrepancies (relying on the article by Montgomery et al. but here again, we are only dealing with 1 base indel, which is not really representative of short indel  in general). I may try to check this results with my own results.

Recent articles

August 1, 2013 Leave a comment

Just a small review of the articles I went through recently :

  • Why proper definition of any statistic matter. An article on the numerous Fst definitions, and their respective behavior with varying assumptions : MAF spectrum, samples size etc.¬† The article is very comprehensive and give a nice review of main concept around Fst.
  • Still around population genetics an article of my PhD supervisor on selective sweeps identification. The supporting information are also pretty worthy.
  • A nice review on measure of dependence¬†it’s a¬† real relief to see alternative to the correlation coefficient do exist ! I must admit that pointless statements like “X and Y are correlated at 5%” just kill me.
  • “Encode returns” a reply to Graur’s article by Mattick and Dinger.
  • While at first glance its title may sound rather tautological, this article on long intergenic non coding RNA nicely shows that similar behavior (Ribosome occupancy) does not imply similar results (Protein coding). I wondered if the absence of stop codon in lincRNA (and thus the absence of ribosome release) impact the cell, but apparently lincRNA are not numerous enough to have any substantial effect.