Hej Hervé!
---------------------------------------------------------------
Nicolas Delhomme
Genome Biology Computational Support
European Molecular Biology Laboratory
Tel: +49 6221 387 8310
Email: nicolas.delho...@embl.de
Meyerhofstrasse 1 - Postfach 10.2209
69102 Heidelberg, Germany
---------------------------------------------------------------
On Jul 10, 2013, at 8:54 PM, Hervé Pagès wrote:
Hi Nico,
On 07/09/2013 08:07 AM, Nicolas Delhomme wrote:
Hej Bioc Core!
There was some discussion last year about implementing a BamStreamer (à la
FastqStreamer), but I haven't seen anything like it in the current devel. I've
implemented the following function that should do the job for me - I have many
very large files, and I need to use a cluster with relatively few RAM per node
and a restrictive time allocation , so I want to parallelize the reading of the
BAM file to manage both. The example below is obviously not affecting the RAM
issue but I streamlined it to point out my issue.
".stream" <- function(bamFile,yieldSize=100000,verbose=FALSE){
## create a stream
stopifnot(is(bamFile,"BamFile"))
## set the yieldSize if it is not set already
if(is.na(yieldSize(bamFile))){
yieldSize(bamFile) <- yieldSize
}
## open it
open(bamFile)
## verb
if(verbose){
message(paste("Streaming",basename(path(bamFile))))
}
## create the output
out <- GappedAlignments()
## process it
while(length(chunk <- readBamGappedAlignments(bamFile))){
if(verbose){
message(paste("Processed",length(chunk),"reads"))
}
out <- c(out,chunk)
}
Note that regardless the speed of c() on GappedAlignments objects,
growing an object in a loop is fundamentally inefficient (see Circle 2
of The R Inferno).
Also keeping the chunks in memory kind of defeats the purpose of reading
the file one chunk at a time.
Sure. What this function normally really does is a data reduction - basically
getting a named vector back. I just came across the appending issue when
preparing the code example above.
## close
close(bamFile)
## return
return(out)
}
In the method above, the first iteration of combining the GappedAlignments:
out <- c(out,chunk) takes:
system.time(append(out,chunk))
user system elapsed
123.704 0.060 124.011
2 minutes! Whaoo, that's really slow. I can't reproduce this on my
machine though:
OK, sounds more like a system issue then.
library(Rsamtools)
library(RNAseqData.HNRNPC.bam.chr14)
bamfile <- BamFile(RNAseqData.HNRNPC.bam.chr14_BAMFILES[1L])
yieldSize(bamfile) <- 100000L
open(bamfile)
out <- GappedAlignments()
Then:
chunk <- readBamGappedAlignments(bamfile)
system.time(out <- append(out, chunk))
user system elapsed
0.284 0.000 0.286
I wonder what's going on on your system. Are you sure it was not running
out of memory when you did this?
Yes, that's a fat node with 0.2TB RAM and I was the only one on it at the time.
Try to check the load with uptime or
top in another terminal (e.g. start top right before you call append()).
If the system starts swapping, then your R process will become hundreds
or thousands times slower!
and there was no memory intensive job running. Could still have been some NFS
related issue. I will retry with a fresh session and monitor the I/O as well.
whereas the second iteration (faked here) takes only (still long):
system.time(append(chunk,chunk))
user system elapsed
2.708 0.044 2.758
2nd, 3rd and 4th iterations for me:
chunk <- readBamGappedAlignments(bamfile)
system.time(out <- append(out, chunk))
user system elapsed
0.516 0.004 0.521
chunk <- readBamGappedAlignments(bamfile)
system.time(out <- append(out, chunk))
user system elapsed
0.656 0.008 0.663
chunk <- readBamGappedAlignments(bamfile)
system.time(out <- append(out, chunk))
user system elapsed
0.796 0.004 0.801
As expected, the time is growing (this is why the process
of growing an object in a loop is considered to be quadratic
in time).
Quadratic! Wow, I knew it was slower but still... Good to know.
I suppose this has to do with the way
GenomicRanges:::unlist_list_of_GappedAlignments deals with combining the
objects and all the related sanity checks. For the first iteration, the
seqlengths are different so I suppose that is what explains the 60X lag
compared to the second iteration.
The seqinfo of the 2 objects to combine need to be merged together
and set back on each object before the 2 objects can actually
be combined. This operation is cheap and I wouldn't expect this
to slow down the first iteration significantly.
Yes, that was very surprising.
Due to the implementation of GappedAlignments, I can't set the seqlengths
programmatically in GappedAlignments() which I imagine would have reduced the
first iteration lag; see the trials below:
out <- GappedAlignments(seqlengths=seqlengths(chunk))
Error in GappedAlignments(seqlengths = seqlengths(chunk)) :
'names(seqlengths)' incompatible with 'levels(seqnames)'
out <- GappedAlignments(seqlengths=seqlengths(chunk),seqnames=seqnames(chunk))
Error in GappedAlignments(seqlengths = seqlengths(chunk), seqnames =
seqnames(chunk)) :
'strand' must be specified when 'seqnames' is not empty
out <-
GappedAlignments(seqlengths=seqlengths(chunk),seqnames=seqnames(chunk),strand="+")
Error in validObject(.Object) :
invalid class “GappedAlignments” object: 1: invalid object for slot "strand" in class
"GappedAlignments": got class "character", should be or extend class "Rle"
invalid class “GappedAlignments” object: 2: number of rows in DataTable
'mcols(x)' must match length of 'x'
The trick is to create an empty GappedAlignments objects
with non-empty seqlevels so you can put seqlengths on the
seqlevels.
Here are 2 ways to create an empty GappedAlignments objects with
non-empty seqlevels:
(1) Pass an empty factor with non-empty levels to the seqnames
arg:
out <- GappedAlignments(seqnames=factor(levels=seqlevels(chunk)))
(2) The recommended way:
out <- GappedAlignments()
seqinfo(out) <- seqinfo(chunk)
Note that with (2), 'out' gets all the seqinfo from 'chunk' (including
its seqlengths), not only its seqlevels.
(1) could be adapted to also set the seqlengths:
out <- GappedAlignments(seqnames=factor(levels=seqlevels(chunk)),
seqlengths=seqlengths(chunk))
but (2) is really the preferred way.
Thanks for the pointers!
I completely approve of such sanity checks; it seems that I'm just trying to do
something that it was not designed for :-) All I'm really interested in is a
way to stream my BAM file and I'm looking forward to any suggestion. I
especially don't want to re-invent the wheel if you have already planned
something. If you haven't I'd be glad to get some insight how I can walk around
that problem.
My sessionInfo:
R version 3.0.1 (2013-05-16)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=C LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] BiocInstaller_1.11.3 Rsamtools_1.13.22 Biostrings_2.29.12
[4] GenomicRanges_1.13.26 XVector_0.1.0 IRanges_1.19.15
[7] BiocGenerics_0.7.2
loaded via a namespace (and not attached):
[1] bitops_1.0-5 stats4_3.0.1 zlibbioc_1.7.0
Looks like you are using Bioc-devel. Did you get all the
warnings about GappedAlignments, readBamGappedAlignments(),
and GappedAlignments() being deprecated?
I though I did, but indeed I didn't get the warnings then. This is very strange.
I thought you were using the release so that's what I used:
sessionInfo()
R version 3.0.0 (2013-04-03)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=C LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] RNAseqData.HNRNPC.bam.chr14_0.1.3 Rsamtools_1.12.3
[3] Biostrings_2.28.0 GenomicRanges_1.12.4
[5] IRanges_1.18.1 BiocGenerics_0.6.0
loaded via a namespace (and not attached):
[1] bitops_1.0-5 stats4_3.0.0 zlibbioc_1.6.0
The timings I get with Bioc-devel are pretty much the same though.
Something doesn't seem to be quite right with your cluster.
I agree, I'll check that out.
What happens
if you try to rbind() 2 data.frames of 100000 rows each in a fresh
session?
df <- data.frame(aa=1:100000, bb=100000:1, cc="cc", dd="dd")
system.time(df2 <- rbind(df, df))
user system elapsed
0.204 0.000 0.206
Good point. I'll try that out and let you know.
Thanks for the very detailed answer!
Cheers,
Nico
Thanks,
H.
Cheers,
Nico
---------------------------------------------------------------
Nicolas Delhomme
Genome Biology Computational Support
European Molecular Biology Laboratory
Tel: +49 6221 387 8310
Email: nicolas.delho...@embl.de
Meyerhofstrasse 1 - Postfach 10.2209
69102 Heidelberg, Germany
_______________________________________________
Bioc-devel@r-project.org mailing list
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--
Hervé Pagès
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N, M1-B514
P.O. Box 19024
Seattle, WA 98109-1024
E-mail: hpa...@fhcrc.org
Phone: (206) 667-5791
Fax: (206) 667-1319
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