tool to merge pileup

[sbaheti]

hi

I need to merge pileup from multiple samples, do we have an open source tool to merge multiple pileups.

Thanks

Saurabh

[mgolo]

Hi!

I have the same question Saurabh. I have 3 biological replicates and i would like to normalize them before i merge their pileup files. Does anyone have a clue for this?

Thanks in advance!

Maria

[gringer]

For a basic merge, 'samtools mpileup ' will do this:

http://samtools.sourceforge.net/mpileup.shtml

It displays different columns for each sample; I'm not sure if that is what you want.

[mgolo]

For a basic merge, 'samtools mpileup ' will do this:

http://samtools.sourceforge.net/mpileup.shtml

It displays different columns for each sample; I'm not sure if that is what you want.

Hi! Thanks for your fast reply

I don´t think this is what i need. I would like to merge 3 biological replicates' results into one single file. Basically for each count calculate the mean of the 3 runs. But before doing that i need to normalize somehow the 3 files, as they have different coverages. Maybe i have to do this before creating the pileup files... Any idea?

[gringer]

I don't think this is what i need. I would like to merge 3 biological replicates' results into one single file. Basically for each count calculate the mean of the 3 runs. But before doing that i need to normalize somehow the 3 files, as they have different coverages. Maybe i have to do this before creating the pileup files... Any idea?

mpileup is the only hammer I know (I'm very new to this), so take this with a grain of salt....

mpileup gives raw count data for each run, so you can extract those columns as your raw coverages per sample. I would then normalise by dividing by some statistic from the counts per column (e.g. 75th percentile). Here's a quick R script that does this:

# read in pileup data
data.df <- read.delim("mpileup_lane1-6.csv", sep = "\t", header = FALSE,
                      col.names = c("isoform","pos","flag",paste(c("count","seq","qual"),rep(1:6,each=3),sep="_")));
pos.counts <- cbind(isoform = data.df$isoform,pos = data.df$pos,data.df[,paste("count",1:6,sep="_")]);
# rough normalisation of count data
quantile.counts <- apply(pos.counts[,3:8],2,quantile, p=0.75);
quantile.counts <- quantile.counts / min(quantile.counts);
pos.counts[,3:8] <- t(t(pos.counts[,3:8]) / quantile.counts);
pos.counts$mean <- apply(pos.counts[,3:8],1,mean);

A more complex *PKM-style normalisation would need to take into account the transcript sizes for each hit, so you'd use something like cufflinks/DEseq/whatever to get *PKM values for each transcript, then divide by that value. I'm not sure if going that far would be necessary, given that they should be hitting transcripts with a similar relative frequency.

[mgolo]

Thank you gringer!

I think you are right about the normalization, no need to take into account transcript sizes