Advice on reading big BED/BAM and ChIP-seq quality control
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Hari Easwaran ▴ 240
@hari-easwaran-3510
Last seen 9.6 years ago
United States
Dear Bioc gurus, I am a newbie with using R tools for ChIP-seq analyses and seek advice on the best way to go about a data set I have. Following are the file formats I have and what I would like to do with them: 1) Using Samtools, I created BED files (about 5 Gb) from the BAM files (3-4 Gb) 2) Want to read the BED files (or BAM files) into R. 3) Perform quality control plots (like the number of duplicated reads across the samples because the nature of ChIP-seq processing is different in some of the samples, and so I want to know what bias it introduces). 4) Be able to retrieve specific genomic regions for exploration and visualization of reads/peaks in the context of genomic annotations (I guess to have in a format so that I can play with GenomicRanges). I am doing all this in a cluster with fairly good memory capacities (about 18G; Or perhaps I think it is 'good' memory). I went through the mailing list and found some very useful discussion on reading BED/BAM files: https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-March/001900.html https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-September/002242.html I thought BED files will be easy to work with because it already has data in a format that I understand (chromosome, start, end, tags). I tried the 'import' function from rtracklayer, as suggested in the above link, to read the BED file. However, it didn't work as I run out of memory. >From the discussions, it seems an alternative is Rsamtools to read BAM files. Before I go about with trying Rsamtools, I would be happy to get some advice on whether I am on the right track by using Rsamtools, and if any other packages/tools might have in-built functions to achieve what I want with the data. Thanks for your time. Sincerely Hari Easwaran [[alternative HTML version deleted]]
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@martin-morgan-1513
Last seen 5 months ago
United States
On 05/13/2013 06:43 AM, Hari Easwaran wrote: > Dear Bioc gurus, > > I am a newbie with using R tools for ChIP-seq analyses and seek advice on > the best way to go about a data set I have. Following are the file formats > I have and what I would like to do with them: > > 1) Using Samtools, I created BED files (about 5 Gb) from the BAM files (3-4 > Gb) > > 2) Want to read the BED files (or BAM files) into R. > > 3) Perform quality control plots (like the number of duplicated reads > across the samples because the nature of ChIP-seq processing is different > in some of the samples, and so I want to know what bias it introduces). > > 4) Be able to retrieve specific genomic regions for exploration and > visualization of reads/peaks in the context of genomic annotations (I guess > to have in a format so that I can play with GenomicRanges). > > > I am doing all this in a cluster with fairly good memory capacities (about > 18G; Or perhaps I think it is 'good' memory). I went through the mailing > list and found some very useful discussion on reading BED/BAM files: > > https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-March/001900.html > > https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-September/002242.html > > I thought BED files will be easy to work with because it already has data > in a format that I understand (chromosome, start, end, tags). I tried the > 'import' function from rtracklayer, as suggested in the above link, to read > the BED file. However, it didn't work as I run out of memory. > >>From the discussions, it seems an alternative is Rsamtools to read BAM > files. Before I go about with trying Rsamtools, I would be happy to get > some advice on whether I am on the right track by using Rsamtools, and if > any other packages/tools might have in-built functions to achieve what I > want with the data. As the author of Rsamtools, I'd say yes, you are on the right track using this package and (indexed) bam files. A good place to start is the readBamGappedAlignments function (this is renamed to readGAlignmentsFromBam in the 'devel' version) which inputs essential alignment data (seqname, start, cigar) of aligned reads; additional information can also be input. You'll want to understand how to use ScanBamParam. Strategies for dealing with large data are to iterate through the bam file (using the 'BamFile' function with yieldSize=1000000 or similarly large but manageable number; see ?BamFile for examples) or indexing into the bam file to retrieve specific regions of interest (using the 'which' argument to ScanBamParam(), and using ScanBamParam as the 'param' argument to most functions that read BAM files). There are packages that provide QA, including the 'qrqc', 'QuasR', and 'ShortRead' packages among others. Be sure to read package vignettes from bioconductor.org or via, e.g., vignette(package="Rsamtools"). Don't get too bogged down on a cluster right out of the gate; instead get small data working, move to parallel::mclapply (typically processing separate bam files in separate processes) and then if needs be jobs submitted to clusters. Hope that helps, feel free to come back to the list with more specific questions as you encounter problems. Martin > > Thanks for your time. > > > Sincerely > > Hari Easwaran > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > -- Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M1 B861 Phone: (206) 667-2793
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I agree with everything Martin suggests. Definitely use the bam files directly, but one slight difference is that I'm usually a big fan of parallelizing over chromosomes instead of bams. To me it's much easier to get that workflow running on a single machine as the memory overhead would usually be less when doing things naively. HTH, -steve On Monday, May 13, 2013, Martin Morgan wrote: > On 05/13/2013 06:43 AM, Hari Easwaran wrote: > >> Dear Bioc gurus, >> >> I am a newbie with using R tools for ChIP-seq analyses and seek advice on >> the best way to go about a data set I have. Following are the file >> formats >> I have and what I would like to do with them: >> >> 1) Using Samtools, I created BED files (about 5 Gb) from the BAM files >> (3-4 >> Gb) >> >> 2) Want to read the BED files (or BAM files) into R. >> >> 3) Perform quality control plots (like the number of duplicated reads >> across the samples because the nature of ChIP-seq processing is different >> in some of the samples, and so I want to know what bias it introduces). >> >> 4) Be able to retrieve specific genomic regions for exploration and >> visualization of reads/peaks in the context of genomic annotations (I >> guess >> to have in a format so that I can play with GenomicRanges). >> >> >> I am doing all this in a cluster with fairly good memory capacities (about >> 18G; Or perhaps I think it is 'good' memory). I went through the mailing >> list and found some very useful discussion on reading BED/BAM files: >> >> https://stat.ethz.ch/**pipermail/bioc-sig-sequencing/** >> 2011-March/001900.html<https: stat.ethz.ch="" pipermail="" bioc-sig-="" sequencing="" 2011-march="" 001900.html=""> >> >> https://stat.ethz.ch/**pipermail/bioc-sig-sequencing/** >> 2011-September/002242.html<https: stat.ethz.ch="" pipermail="" bioc-sig-="" sequencing="" 2011-september="" 002242.html=""> >> >> I thought BED files will be easy to work with because it already has data >> in a format that I understand (chromosome, start, end, tags). I tried the >> 'import' function from rtracklayer, as suggested in the above link, to >> read >> the BED file. However, it didn't work as I run out of memory. >> >> From the discussions, it seems an alternative is Rsamtools to read BAM >>> >> files. Before I go about with trying Rsamtools, I would be happy to get >> some advice on whether I am on the right track by using Rsamtools, and if >> any other packages/tools might have in-built functions to achieve what I >> want with the data. >> > > As the author of Rsamtools, I'd say yes, you are on the right track using > this package and (indexed) bam files. > > A good place to start is the readBamGappedAlignments function (this is > renamed to readGAlignmentsFromBam in the 'devel' version) which inputs > essential alignment data (seqname, start, cigar) of aligned reads; > additional information can also be input. You'll want to understand how to > use ScanBamParam. > > Strategies for dealing with large data are to iterate through the bam file > (using the 'BamFile' function with yieldSize=1000000 or similarly large but > manageable number; see ?BamFile for examples) or indexing into the bam file > to retrieve specific regions of interest (using the 'which' argument to > ScanBamParam(), and using ScanBamParam as the 'param' argument to most > functions that read BAM files). > > There are packages that provide QA, including the 'qrqc', 'QuasR', and > 'ShortRead' packages among others. Be sure to read package vignettes from > bioconductor.org or via, e.g., vignette(package="Rsamtools"). > > Don't get too bogged down on a cluster right out of the gate; instead get > small data working, move to parallel::mclapply (typically processing > separate bam files in separate processes) and then if needs be jobs > submitted to clusters. > > Hope that helps, feel free to come back to the list with more specific > questions as you encounter problems. > > Martin > > >> Thanks for your time. >> >> >> Sincerely >> >> Hari Easwaran >> >> [[alternative HTML version deleted]] >> >> ______________________________**_________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.e="" thz.ch="" mailman="" listinfo="" bioconductor=""> >> Search the archives: http://news.gmane.org/gmane.** >> science.biology.informatics.**conductor<http: news.gmane.org="" gmane="" .science.biology.informatics.conductor=""> >> >> > > -- > Computational Biology / Fred Hutchinson Cancer Research Center > 1100 Fairview Ave. N. > PO Box 19024 Seattle, WA 98109 > > Location: Arnold Building M1 B861 > Phone: (206) 667-2793 > > ______________________________**_________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.et="" hz.ch="" mailman="" listinfo="" bioconductor=""> > Search the archives: http://news.gmane.org/gmane.** > science.biology.informatics.**conductor<http: news.gmane.org="" gmane.="" science.biology.informatics.conductor=""> > -- Steve Lianoglou Computational Biologist Department of Bioinformatics and Computational Biology Genentech [[alternative HTML version deleted]]
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@michael-stadler-5887
Last seen 4 weeks ago
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Dear Hari, Reading all alignments of a sample (either from a bed or a bam file) into memory may not be sustainable. One way to get around that would be to work on streams, i.e. only loading a chunk of the alignments at a time. The Rsamtools package provides such functionality for bam files (see ?BamFile and "yield" therein). Alternatively, I would like to point out the QuasR package to you (apologies for the advertisement). We have avoided the memory issue by traversing the bam files at the C level, and only extracting the information that is required. The function qQCrepoprt() may fulfill your requirement 3), and the functions qCount() and qProfile() may be what you can use to do 4). QuasR was designed to begin the analysis with reads and also create the bam files for you; you can however also start with pre-existing bam files and still use a good part of its functionality (see vignette section 5.1, "BAM" files). I hope this helps, Michael Reading alignments On 13.05.2013 15:43, Hari Easwaran wrote: > Dear Bioc gurus, > > I am a newbie with using R tools for ChIP-seq analyses and seek advice on > the best way to go about a data set I have. Following are the file formats > I have and what I would like to do with them: > > 1) Using Samtools, I created BED files (about 5 Gb) from the BAM files (3-4 > Gb) > > 2) Want to read the BED files (or BAM files) into R. > > 3) Perform quality control plots (like the number of duplicated reads > across the samples because the nature of ChIP-seq processing is different > in some of the samples, and so I want to know what bias it introduces). > > 4) Be able to retrieve specific genomic regions for exploration and > visualization of reads/peaks in the context of genomic annotations (I guess > to have in a format so that I can play with GenomicRanges). > > > I am doing all this in a cluster with fairly good memory capacities (about > 18G; Or perhaps I think it is 'good' memory). I went through the mailing > list and found some very useful discussion on reading BED/BAM files: > > https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-March/001900.html > > https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-September/002242.html > > I thought BED files will be easy to work with because it already has data > in a format that I understand (chromosome, start, end, tags). I tried the > 'import' function from rtracklayer, as suggested in the above link, to read > the BED file. However, it didn't work as I run out of memory. > >>From the discussions, it seems an alternative is Rsamtools to read BAM > files. Before I go about with trying Rsamtools, I would be happy to get > some advice on whether I am on the right track by using Rsamtools, and if > any other packages/tools might have in-built functions to achieve what I > want with the data. > > Thanks for your time. > > > Sincerely > > Hari Easwaran > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > -- -------------------------------------------- Michael Stadler, PhD Head of Computational Biology Friedrich Miescher Institute Basel (Switzerland) Phone : +41 61 697 6492 Fax : +41 61 697 3976 Mail : michael.stadler at fmi.ch
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Hi Michael, That's the first time I saw QuasR -- looks like a very nicely done package. Good stuff! -steve On Tue, May 14, 2013 at 12:06 AM, Michael Stadler <michael.stadler at="" fmi.ch=""> wrote: > Dear Hari, > > Reading all alignments of a sample (either from a bed or a bam file) > into memory may not be sustainable. > > One way to get around that would be to work on streams, i.e. only > loading a chunk of the alignments at a time. The Rsamtools package > provides such functionality for bam files (see ?BamFile and "yield" > therein). > > Alternatively, I would like to point out the QuasR package to you > (apologies for the advertisement). We have avoided the memory issue by > traversing the bam files at the C level, and only extracting the > information that is required. The function qQCrepoprt() may fulfill your > requirement 3), and the functions qCount() and qProfile() may be what > you can use to do 4). > > QuasR was designed to begin the analysis with reads and also create the > bam files for you; you can however also start with pre-existing bam > files and still use a good part of its functionality (see vignette > section 5.1, "BAM" files). > > I hope this helps, > Michael > > > > Reading alignments > > On 13.05.2013 15:43, Hari Easwaran wrote: >> Dear Bioc gurus, >> >> I am a newbie with using R tools for ChIP-seq analyses and seek advice on >> the best way to go about a data set I have. Following are the file formats >> I have and what I would like to do with them: >> >> 1) Using Samtools, I created BED files (about 5 Gb) from the BAM files (3-4 >> Gb) >> >> 2) Want to read the BED files (or BAM files) into R. >> >> 3) Perform quality control plots (like the number of duplicated reads >> across the samples because the nature of ChIP-seq processing is different >> in some of the samples, and so I want to know what bias it introduces). >> >> 4) Be able to retrieve specific genomic regions for exploration and >> visualization of reads/peaks in the context of genomic annotations (I guess >> to have in a format so that I can play with GenomicRanges). >> >> >> I am doing all this in a cluster with fairly good memory capacities (about >> 18G; Or perhaps I think it is 'good' memory). I went through the mailing >> list and found some very useful discussion on reading BED/BAM files: >> >> https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-March/001900.html >> >> https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-September/002242.html >> >> I thought BED files will be easy to work with because it already has data >> in a format that I understand (chromosome, start, end, tags). I tried the >> 'import' function from rtracklayer, as suggested in the above link, to read >> the BED file. However, it didn't work as I run out of memory. >> >>>From the discussions, it seems an alternative is Rsamtools to read BAM >> files. Before I go about with trying Rsamtools, I would be happy to get >> some advice on whether I am on the right track by using Rsamtools, and if >> any other packages/tools might have in-built functions to achieve what I >> want with the data. >> >> Thanks for your time. >> >> >> Sincerely >> >> Hari Easwaran >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > -- > -------------------------------------------- > Michael Stadler, PhD > Head of Computational Biology > Friedrich Miescher Institute > Basel (Switzerland) > Phone : +41 61 697 6492 > Fax : +41 61 697 3976 > Mail : michael.stadler at fmi.ch > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Steve Lianoglou Computational Biologist Department of Bioinformatics and Computational Biology Genentech
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Hi Martin, Steve, and Michael, I have to do some homework reading the vignettes and working on a small subset. Michael, I came across QuasR in my search. Will try that. If I got it, Rsamtools, as well as QuasR, allows reading part of the data, say for a chromosome, thus helping in managing memory issues. I hope I can bother you again as I will have more questions. Thanks a lot for your prompt help. Sincerely, Hari On Tue, May 14, 2013 at 3:16 AM, Steve Lianoglou <lianoglou.steve@gene.com>wrote: > Hi Michael, > > That's the first time I saw QuasR -- looks like a very nicely done package. > > Good stuff! > -steve > > > On Tue, May 14, 2013 at 12:06 AM, Michael Stadler > <michael.stadler@fmi.ch> wrote: > > Dear Hari, > > > > Reading all alignments of a sample (either from a bed or a bam file) > > into memory may not be sustainable. > > > > One way to get around that would be to work on streams, i.e. only > > loading a chunk of the alignments at a time. The Rsamtools package > > provides such functionality for bam files (see ?BamFile and "yield" > > therein). > > > > Alternatively, I would like to point out the QuasR package to you > > (apologies for the advertisement). We have avoided the memory issue by > > traversing the bam files at the C level, and only extracting the > > information that is required. The function qQCrepoprt() may fulfill your > > requirement 3), and the functions qCount() and qProfile() may be what > > you can use to do 4). > > > > QuasR was designed to begin the analysis with reads and also create the > > bam files for you; you can however also start with pre-existing bam > > files and still use a good part of its functionality (see vignette > > section 5.1, "BAM" files). > > > > I hope this helps, > > Michael > > > > > > > > Reading alignments > > > > On 13.05.2013 15:43, Hari Easwaran wrote: > >> Dear Bioc gurus, > >> > >> I am a newbie with using R tools for ChIP-seq analyses and seek advice > on > >> the best way to go about a data set I have. Following are the file > formats > >> I have and what I would like to do with them: > >> > >> 1) Using Samtools, I created BED files (about 5 Gb) from the BAM files > (3-4 > >> Gb) > >> > >> 2) Want to read the BED files (or BAM files) into R. > >> > >> 3) Perform quality control plots (like the number of duplicated reads > >> across the samples because the nature of ChIP-seq processing is > different > >> in some of the samples, and so I want to know what bias it introduces). > >> > >> 4) Be able to retrieve specific genomic regions for exploration and > >> visualization of reads/peaks in the context of genomic annotations (I > guess > >> to have in a format so that I can play with GenomicRanges). > >> > >> > >> I am doing all this in a cluster with fairly good memory capacities > (about > >> 18G; Or perhaps I think it is 'good' memory). I went through the mailing > >> list and found some very useful discussion on reading BED/BAM files: > >> > >> > https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-March/001900.html > >> > >> > https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-September/002242.html > >> > >> I thought BED files will be easy to work with because it already has > data > >> in a format that I understand (chromosome, start, end, tags). I tried > the > >> 'import' function from rtracklayer, as suggested in the above link, to > read > >> the BED file. However, it didn't work as I run out of memory. > >> > >>>From the discussions, it seems an alternative is Rsamtools to read BAM > >> files. Before I go about with trying Rsamtools, I would be happy to get > >> some advice on whether I am on the right track by using Rsamtools, and > if > >> any other packages/tools might have in-built functions to achieve what I > >> want with the data. > >> > >> Thanks for your time. > >> > >> > >> Sincerely > >> > >> Hari Easwaran > >> > >> [[alternative HTML version deleted]] > >> > >> _______________________________________________ > >> Bioconductor mailing list > >> Bioconductor@r-project.org > >> https://stat.ethz.ch/mailman/listinfo/bioconductor > >> Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > >> > > > > -- > > -------------------------------------------- > > Michael Stadler, PhD > > Head of Computational Biology > > Friedrich Miescher Institute > > Basel (Switzerland) > > Phone : +41 61 697 6492 > > Fax : +41 61 697 3976 > > Mail : michael.stadler@fmi.ch > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@r-project.org > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- > Steve Lianoglou > Computational Biologist > Department of Bioinformatics and Computational Biology > Genentech > [[alternative HTML version deleted]]
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I agree with Steve; QuasaR is a very well done package. Thank you Michael! Ugo Quoting Steve Lianoglou <lianoglou.steve at="" gene.com="">: > Hi Michael, > > That's the first time I saw QuasR -- looks like a very nicely done package. > > Good stuff! > -steve > > > On Tue, May 14, 2013 at 12:06 AM, Michael Stadler > <michael.stadler at="" fmi.ch=""> wrote: >> Dear Hari, >> >> Reading all alignments of a sample (either from a bed or a bam file) >> into memory may not be sustainable. >> >> One way to get around that would be to work on streams, i.e. only >> loading a chunk of the alignments at a time. The Rsamtools package >> provides such functionality for bam files (see ?BamFile and "yield" >> therein). >> >> Alternatively, I would like to point out the QuasR package to you >> (apologies for the advertisement). We have avoided the memory issue by >> traversing the bam files at the C level, and only extracting the >> information that is required. The function qQCrepoprt() may fulfill your >> requirement 3), and the functions qCount() and qProfile() may be what >> you can use to do 4). >> >> QuasR was designed to begin the analysis with reads and also create the >> bam files for you; you can however also start with pre-existing bam >> files and still use a good part of its functionality (see vignette >> section 5.1, "BAM" files). >> >> I hope this helps, >> Michael >> >> >> >> Reading alignments >> >> On 13.05.2013 15:43, Hari Easwaran wrote: >>> Dear Bioc gurus, >>> >>> I am a newbie with using R tools for ChIP-seq analyses and seek advice on >>> the best way to go about a data set I have. Following are the file formats >>> I have and what I would like to do with them: >>> >>> 1) Using Samtools, I created BED files (about 5 Gb) from the BAM files (3-4 >>> Gb) >>> >>> 2) Want to read the BED files (or BAM files) into R. >>> >>> 3) Perform quality control plots (like the number of duplicated reads >>> across the samples because the nature of ChIP-seq processing is different >>> in some of the samples, and so I want to know what bias it introduces). >>> >>> 4) Be able to retrieve specific genomic regions for exploration and >>> visualization of reads/peaks in the context of genomic annotations (I guess >>> to have in a format so that I can play with GenomicRanges). >>> >>> >>> I am doing all this in a cluster with fairly good memory capacities (about >>> 18G; Or perhaps I think it is 'good' memory). I went through the mailing >>> list and found some very useful discussion on reading BED/BAM files: >>> >>> https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-March/001900.html >>> >>> https://stat.ethz.ch/pipermail/bioc-sig- sequencing/2011-September/002242.html >>> >>> I thought BED files will be easy to work with because it already has data >>> in a format that I understand (chromosome, start, end, tags). I tried the >>> 'import' function from rtracklayer, as suggested in the above link, to read >>> the BED file. However, it didn't work as I run out of memory. >>> >>>> From the discussions, it seems an alternative is Rsamtools to read BAM >>> files. Before I go about with trying Rsamtools, I would be happy to get >>> some advice on whether I am on the right track by using Rsamtools, and if >>> any other packages/tools might have in-built functions to achieve what I >>> want with the data. >>> >>> Thanks for your time. >>> >>> >>> Sincerely >>> >>> Hari Easwaran >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >> -- >> -------------------------------------------- >> Michael Stadler, PhD >> Head of Computational Biology >> Friedrich Miescher Institute >> Basel (Switzerland) >> Phone : +41 61 697 6492 >> Fax : +41 61 697 3976 >> Mail : michael.stadler at fmi.ch >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- > Steve Lianoglou > Computational Biologist > Department of Bioinformatics and Computational Biology > Genentech > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >
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