mapping coverage
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@adrian-johnson-2728
Last seen 4.0 years ago
hi : has any one tried to graphically represent coverage data from capture sequencing on a chromosome. would you please share what packages you used to represent coverage graphically. thanks Adrian.
Coverage Coverage • 878 views
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@martin-morgan-1513
Last seen 3 days ago
United States
On 02/16/2011 03:08 PM, Adrian Johnson wrote: > hi : > > has any one tried to graphically represent coverage data from capture > sequencing on a chromosome. would you please share what packages you > used to represent coverage graphically. My own approach has been to write wrappers around lattice plotting functions, down-sampling using successiveIRanges for instance to capture the main features when getting a whole-genome view. In some respects it seems like the most insightful visualizations will not be of coverage per se but of derived statistics. Martin > > thanks > Adrian. > > _______________________________________________ > 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: M1-B861 Telephone: 206 667-2793
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what do you think of Martin Krzywinski's visualizations ("hive plots") at UBC? http://mkweb.bcgsc.ca/linnet/img/assembly-quality.png <http: mkweb.bcgsc.ca="" linnet="" img="" assembly-quality.png="">Are these orthogonal to the sorts of plots typically generated for read depth coverage along a chromosome/arm/region/locus, or useful as a replacement? Subjectively, that is. On Thu, Feb 17, 2011 at 5:07 PM, Martin Morgan <mtmorgan@fhcrc.org> wrote: > On 02/16/2011 03:08 PM, Adrian Johnson wrote: > > hi : > > > > has any one tried to graphically represent coverage data from capture > > sequencing on a chromosome. would you please share what packages you > > used to represent coverage graphically. > > My own approach has been to write wrappers around lattice plotting > functions, down-sampling using successiveIRanges for instance to capture > the main features when getting a whole-genome view. > > In some respects it seems like the most insightful visualizations will > not be of coverage per se but of derived statistics. > > Martin > > > > > thanks > > Adrian. > > > > _______________________________________________ > > 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 > > > -- > Computational Biology > Fred Hutchinson Cancer Research Center > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 > > Location: M1-B861 > Telephone: 206 667-2793 > > _______________________________________________ > 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 > -- If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is. John von Neumann<http: www-groups.dcs.st-="" and.ac.uk="" ~history="" biographies="" von_neumann.html=""> [[alternative HTML version deleted]]
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