How to efficiently read in large bedgraph files in R

This post is a bit R programming-technical, but I have spent quite some time finding a solution, so I thought I would share it anyway.


p>First some background. One of the factors

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that might affect the potency of an antisense oligonucleotide is whether or not there are proteins binding to the same region of the transcript that the oligo binds to. Should such potential protein binding increase or decrease potency? Well, it could go both ways, I guess. A large protein complex might hinder the oligo in getting near its target region. On the other hand, since the oligo is expected to bind much more strongly than the protein, in a thermal and stochastic microworld, the presumed binding/release events between protein and transcript might serve to actually make the target region more accessible to the oligo. In the end, it might simply depend on the type of protein, whether the potency goes up or down.

In a recent paper by Baltz et al. (2012) from the Landthaler lab, the mRNA-bound proteome and its global occupancy profile on protein-coding transcripts is identified by UV crosslinking, proteomics, and sequencing. The data for this paper includes bedgraph files of (genome-wide) protein occupancy (GSE38355). These bedgraph files are basically tab-separated text files with 4 columns (chromosome, start position, end position, occupancy value).

With such data, we may be able to evaluate whether protein binding affect oligo potency. For a given oligo binding region, a simple approach would be to extract the occupancy values for that region, and associate that with the observed oligo potency.

However, in my case, I really like to do these analyses in the R language, and the bedgraph files are >1GB in size. A simple read.table command takes longer to complete than I had patience to wait (probably hours). Googling around, this size/speed-limitation of read.table is a known problem. One solution, I found, is to use the scan command instead. Here is a simple function based on that

read.bedgraph <- function(file) { dat <- scan(file=file,

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what=list(character(),integer(),integer(),numeric()), sep="\t", skip=1) dat <- data.frame(chr=dat[[1]], start=dat[[2]], end=dat[[3]], val=dat[[4]]) return(dat) }

With the read.bedgraph command, it takes 189s to read in a 1.28GB bedgraph file. Inspection of the data frame reveals that there are over 45 million rows. Reading in that many rows in a bit more than 3 minutes is OK I think. An additional improvement, however, comes when using the save command to store the bedgraph data frame as an R object in binary format (.rdata). This reduces the size of the saved file to 266MB, and when using the load command to retrieve the data frame, it now takes only 6.1s.

So, using these tricks, getting a very big data file into R can be done in a few seconds.

Of course, this solution is dependent on holding the entire table in memory. What if the bedgraph file was, say 50GB? On my 8GB laptop, the in-memory solution will not work then. In this case, I found a solution where the data is first

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read into a temporary SQL database. From this database, the relevant data are selected and returned in a data frame. As an example, to extract occupancy data from between position 60K and 100K on chromosome 10 from the above mentioned bedgraph files, the R code (which depends on the package sqldf) looks like this

require(sqldf) f <- file(filename) d <- sqldf("select * from f where V1='chr10' and V2>60000 and V3<100000", dbname = tempfile(), file.format = list(header = F, row.names = F, sep="\t", skip=1)))

I have tested this on a 1.35GB bedgraph file, and here it took 250s (4.2 minutes). This is OK. If it actually works, in reasonably time, on a >10GB (larger than in-memory) file, would be interesting to know. Anyone know of oligo-relevant bedgraph files of that size?

Posted in Education, Oligoinformatics, Sequencing

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