[Bioc-sig-seq] understanding memory size of AlignedRead objects
Janet Young
jayoung at fhcrc.org
Sat May 14 01:09:41 CEST 2011
Hi Herve,
Thanks for this, as well as your long email of yesterday.
I'll try the code, but first a quick question: I did update.packages, and got the new IRanges (1.10.2) and ShortRead (1.10.2) versions as below, but it looks like Biostrings that's online is still 2.20.0 - maybe there was a glitch in uploading?
thanks,
Janet
On May 13, 2011, at 3:22 AM, Hervé Pagès wrote:
> Hi Janet,
>
> I've committed a few changes to IRanges/Biostrings/ShortRead that
> should address the problem you are experiencing with compact():
>
> library(ShortRead)
> dirPath <- "Data/C1-36Firecrest/Bustard/GERALD"
> bigaln <- readAligned(dirPath, "big_s_2_export.txt", "SolexaExport")
>
> length(bigaln) # 16384000
> object.size(bigaln) # 2457626736 bytes
>
> ## Size overhead for any AlignedRead object:
> emptyaln <- bigaln[FALSE]
> ALNovh <- object.size(emptyaln) # 20736 bytes
>
> ## Bytes per read:
> (object.size(bigaln) - ALNovh) / length(bigaln) # 150 bytes
>
> ## Take the first 10000 elts:
> first10000 <- bigaln[1:10000]
> first10000_compact <- compact(first10000)
>
> ## Bytes per read:
> (object.size(first10000_compact) - ALNovh) / 10000 # 150 bytes
>
> Make sure you have IRanges >= 1.10.2, Biostrings >= 2.20.1 and
> ShortRead >= 1.10.2 (should become available in release on
> Friday around 11am Seattle time).
>
> Cheers,
> H.
>
> > sessionInfo()
> R version 2.13.0 (2011-04-13)
> Platform: x86_64-unknown-linux-gnu (64-bit)
>
> locale:
> [1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
> [3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
> [5] LC_MONETARY=C LC_MESSAGES=en_CA.UTF-8
> [7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
> [9] LC_ADDRESS=C LC_TELEPHONE=C
> [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
>
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
>
> other attached packages:
> [1] ShortRead_1.10.2 Rsamtools_1.4.1 lattice_0.19-26
> [4] Biostrings_2.20.1 GenomicRanges_1.4.3 IRanges_1.10.3
>
> loaded via a namespace (and not attached):
> [1] Biobase_2.12.1 grid_2.13.0 hwriter_1.3
>
>
>
> On 11-05-10 05:16 PM, Janet Young wrote:
>> Hi again,
>>
>> I've been looking into this question a bit more, now using compact(). Things seemed to go well with the example dataset provided with ShortRead (as far as I can tell). But when I use our own much larger dataset the compaction factor is not very good (my small object is the first 100 reads, the full set is ~18 million reads, but the small object is 40% the size in memory of the full object). I think it looks like quality and sreads haven't really been compacted nearly as much as I would expect. The code is below.
>>
>> Martin - if you would like access to this dataset I can email you about that privately, but I'm guessing the same would happen with any big dataset?
>>
>> Janet
>>
>>
>>
>> library(ShortRead)
>>
>> ########## a big dataset (our data)
>>
>> read2Dir<- "solexa/110317_SN367_0148_A81NVUABXX/Data/Intensities/BaseCalls/GERALD_24-03-2011_solexa.2"
>>
>> my_reads<- readAligned(read2Dir, pattern="s_1_export.txt", type="SolexaExport")
>> my_reads_verysmall<- my_reads[1:100]
>> my_reads_verysmall_compact<- compact(my_reads[1:100])
>>
>> length(my_reads)
>> # [1] 17894091
>> length(my_reads_verysmall)
>> # [1] 100
>> length(my_reads_verysmall_compact)
>> # [1] 100
>>
>> object.size(my_reads)
>> # 3190125528 bytes
>> object.size(my_reads_verysmall)
>> # 1753653496 bytes
>> object.size(my_reads_verysmall_compact)
>> # 1253663384 bytes
>>
>> as.numeric(object.size(my_reads_verysmall)) / as.numeric(object.size(my_reads))
>> # [1] 0.549713
>>
>> as.numeric(object.size(my_reads_verysmall_compact)) / as.numeric(object.size(my_reads))
>> # [1] 0.3929825
>>
>> ##### where is most of that memory?
>> object.size(position(my_reads_verysmall_compact))
>> # 440 bytes
>> object.size(chromosome(my_reads_verysmall_compact))
>> # 3040 bytes
>> object.size(sread(my_reads_verysmall_compact))
>> # 626820976 bytes
>> object.size(quality(my_reads_verysmall_compact))
>> # 626821568 bytes
>>
>> myDNA2<- DNAStringSet(as.character(sread(my_reads_verysmall_compact)))
>> object.size(myDNA2)
>> # 9944 bytes
>>
>>
>> ###### and ShortRead's example dataset, 1000 reads
>>
>> exptPath<- system.file("extdata", package = "ShortRead")
>> sp<- SolexaPath(exptPath)
>> aln<- readAligned(sp, "s_2_export.txt")
>>
>> aln ## aln has 1000 reads
>> aln_small<- aln[1:2] ### aln 2 has 2 reads
>> aln_small_compact<- compact(aln[1:2])
>>
>> object.size(aln)
>> # 175968 bytes
>> object.size(aln_small)
>> # 91488 bytes
>> object.size(aln_small_compact)
>> # 21744 bytes
>>
>> as.numeric(object.size(aln_small)) / as.numeric(object.size(aln))
>> # [1] 0.5199127
>>
>> as.numeric(object.size(aln_small_compact)) / as.numeric(object.size(aln))
>> # [1] 0.1235679
>>
>> myDNA<- DNAStringSet( as.character(sread(aln_small_compact)) )
>> identical(myDNA,sread(aln_small_compact))
>> [1] TRUE
>>
>> aln_medium_compact<- compact(aln[1:300])
>> myDNA_medium<- DNAStringSet( as.character(sread(aln_medium_compact)) )
>> identical(myDNA,sread(aln_medium_compact))
>> # [1] FALSE
>>
>> aln_10_compact<- compact(aln[1:10])
>> myDNA_10<- DNAStringSet( as.character(sread(aln_10_compact)) )
>> identical(myDNA,sread(aln_10_compact))
>> # [1] FALSE
>>
>> as.numeric(object.size(aln_10_compact)) / as.numeric(object.size(aln))
>> # [1] 0.1317512
>>
>> #########
>>
>>
>> sessionInfo()
>>
>> R version 2.13.0 (2011-04-13)
>> 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=C LC_MESSAGES=en_US.UTF-8
>> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
>> [9] LC_ADDRESS=C LC_TELEPHONE=C
>> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>>
>> attached base packages:
>> [1] stats graphics grDevices utils datasets methods base
>>
>> other attached packages:
>> [1] ShortRead_1.10.0 Rsamtools_1.4.0 lattice_0.19-23
>> [4] Biostrings_2.20.0 GenomicRanges_1.4.0 IRanges_1.10.0
>>
>> loaded via a namespace (and not attached):
>> [1] Biobase_2.12.0 grid_2.13.0 hwriter_1.3
>>
>>
>>
>>
>> On May 10, 2011, at 4:25 PM, Martin Morgan wrote:
>>
>>> On 05/10/2011 02:47 PM, Janet Young wrote:
>>>> Hi, (probably hello to you, Martin)
>>>
>>> Hi (or is that hello?) Janet --
>>>
>>>> I'm looking at some Illumina seq data, and trying to be more rigorous
>>>> than I have been in the past about memory usage and tidying up unused
>>>> variables. I'm a little mystified by something - I wonder if you can
>>>> help me understand?
>>>>
>>>> I'm starting with a big AlignedRead object (one full lane of seq
>>>> data) and then I've been using [] on AlignedRead objects to take
>>>> various subsets of the data (and then looking at quality scores, map
>>>> positions, etc). I'm also taking some very small subsets (e.g. just
>>>> the first 100 reads) to test and optimize some functions I'm
>>>> writing.
>>>>
>>>> My confusion comes because even though I'm cutting down the number of
>>>> seq reads by a lot (e.g. from 18 million to just 100 reads), the new
>>>> AlignedRead object still takes up a lot of memory.
>>>
>>> XStringSet (including DNAStringSet and BStringSet, which are used to hold AlignedRead sequence and quality information) are actually 'views' (start and end coordinates) on an underlying XString; when you subset a DNAStringSet, you subset the view but not the underlying DNAString. This might sound bad, but actually you have just one instance of the DNAString, and two light-weight views (the full view, and the subset view). If you're done with the full DNAString, you can force it to be compacated with compact(), e.g., after your first example
>>>
>>>> object.size(s1)
>>> 50840 bytes
>>>> object.size(s1[1:10]) # mostly smaller 'view'
>>> 38984 bytes
>>>> object.size(s1[1]) # a little smaller 'view', same big DNAString
>>> 38864 bytes
>>>> object.size(compact(s1[1]))
>>> 3912 bytes
>>>
>>> or
>>>
>>>> object.size(aln)
>>> 175968 bytes
>>>> object.size(aln[1])
>>> 91488 bytes
>>>> object.size(compact(aln[1]))
>>> 21584 bytes
>>>
>>> (I think Herve is working on your weighted coverage question)
>>>
>>> Martin
>>>
>>>>
>>>> Two examples are given below - in both cases the small object takes
>>>> about half as much memory as the original, even though the number of
>>>> reads is now very much smaller.
>>>>
>>>> Do you have any suggestions as to how I might reduce the memory
>>>> footprint of the subsetted AlignedRead object? Is this an expected
>>>> behavior?
>>>>
>>>> thanks very much,
>>>>
>>>> Janet
>>>>
>>>>
>>>> library(ShortRead)
>>>>
>>>> exptPath<- system.file("extdata", package = "ShortRead") sp<-
>>>> SolexaPath(exptPath) aln<- readAligned(sp, "s_2_export.txt")
>>>>
>>>> aln ## aln has 1000 reads aln_small<- aln[1:2] ### aln 2 has 2
>>>> reads
>>>>
>>>> object.size(aln) # 165156 bytes object.size(aln_small) # 82220 bytes
>>>>
>>>> as.numeric(object.size(aln_small)) / as.numeric(object.size(aln))
>>>> #### [1] 0.4978324
>>>>
>>>> read2Dir<-
>>>> "data/solexa/110317_SN367_0148_A81NVUABXX/Data/Intensities/BaseCalls/GERALD_24-03-2011_solexa.2"
>>>>
>>>>
>>> my_reads<- readAligned(read2Dir, pattern="s_1_export.txt", type="SolexaExport")
>>>> my_reads_verysmall<- my_reads[1:100]
>>>>
>>>> length(my_reads) # [1] 17894091 length(my_reads_verysmall) # [1] 100
>>>>
>>>> object.size(my_reads) # 3190125528 bytes
>>>> object.size(my_reads_verysmall) # 1753653496 bytes
>>>>
>>>> as.numeric(object.size(my_reads_verysmall)) /
>>>> as.numeric(object.size(my_reads)) # [1] 0.549713
>>>>
>>>>
>>>>
>>>> sessionInfo()
>>>>
>>>> R version 2.13.0 (2011-04-13) Platform: i386-apple-darwin9.8.0/i386
>>>> (32-bit)
>>>>
>>>> locale: [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
>>>>
>>>> attached base packages: [1] stats graphics grDevices utils
>>>> datasets methods base
>>>>
>>>> other attached packages: [1] ShortRead_1.10.0 Rsamtools_1.4.1
>>>> lattice_0.19-26 Biostrings_2.20.0 [5] GenomicRanges_1.4.3
>>>> IRanges_1.10.0
>>>>
>>>> loaded via a namespace (and not attached): [1] Biobase_2.12.1
>>>> grid_2.13.0 hwriter_1.3
>>>>
>>>> _______________________________________________ Bioc-sig-sequencing
>>>> mailing list Bioc-sig-sequencing at r-project.org
>>>> https://stat.ethz.ch/mailman/listinfo/bioc-sig-sequencing
>>>
>>>
>>> --
>>> Computational Biology
>>> Fred Hutchinson Cancer Research Center
>>> 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109
>>>
>>> Location: M1-B861
>>> Telephone: 206 667-2793
>>
>> _______________________________________________
>> Bioc-sig-sequencing mailing list
>> Bioc-sig-sequencing at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/bioc-sig-sequencing
>
>
> --
> 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: hpages at fhcrc.org
> Phone: (206) 667-5791
> Fax: (206) 667-1319
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