Révision 5badc2fd
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Installation |
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------------ |
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The first installation step is to retrieve the source code of |
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MyLabStocks. You can do this by typing the following command in a |
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terminal. |
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git clone http://forge.cbp.ens-lyon.fr/git/nucleominer |
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Prerequisites |
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~~~~~~~~~~~~~ |
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* seqinr |
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* plotrix |
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* DESeq |
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* cachecache https://forge.cbp.ens- |
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lyon.fr/redmine/projects/cachecache |
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* nucleominer https://forge.cbp.ens- |
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lyon.fr/redmine/projects/nucleominer |
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The first packages could be installed by typing the following command |
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in an R console: |
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install.packages(c("fork", "rjson", "seqinr", "plotrix"))
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source("http://bioconductor.org/biocLite.R")
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biocLite("DESeq")
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The last packages are available in the git repository they could be |
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install by typing the following command in your terminal: |
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cd nucleominer |
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R CMD INSTALL doc/Chuffart_NM2_workdir/deps/bot_0.14.tar.gz\ |
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doc/Chuffart_NM2_workdir/deps/cachecache_0.1.tar.gz\ |
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build/nucleominer_2.3.46.tar.gz |
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text: |
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$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text |
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cp build/text/readme.txt ../../README
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cat build/text/index.txt build/text/readme.txt build/text/tuto.txt ../../README
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@echo |
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@echo "Build finished. The text files are in $(BUILDDIR)/text." |
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# built documents. |
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# |
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# The short X.Y version. |
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version = '2.3.45'
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version = '2.3.46'
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# The full version, including alpha/beta/rc tags. |
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release = '2.3.45'
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release = '2.3.46'
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# The language for content autogenerated by Sphinx. Refer to documentation |
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# for a list of supported languages. |
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- fork |
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- rjson |
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- seqinr |
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- plotrix |
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- DESeq |
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- cachecache https://forge.cbp.ens-lyon.fr/redmine/projects/cachecache |
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- bot https://forge.cbp.ens-lyon.fr/redmine/projects/bot |
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- nucleominer https://forge.cbp.ens-lyon.fr/redmine/projects/nucleominer |
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The first packages could be installed by typing the following command in an R console: |
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.. code:: bash |
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install.packages(c("fork", "rjson", "seqinr", "plotrix"))
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source("http://bioconductor.org/biocLite.R")
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biocLite("DESeq")
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The last packages are available in the git repository they could be install by typing the following command in your terminal: |
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.. code:: bash |
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cd nucleominer |
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R CMD INSTALL doc/Chuffart_NM2_workdir/deps/bot_0.14.tar.gz\
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R CMD INSTALL doc/Chuffart_NM2_workdir/deps/bot_0.14.tar.gz\ |
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doc/Chuffart_NM2_workdir/deps/cachecache_0.1.tar.gz\ |
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build/nucleominer_2.XXX.tar.gz
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build/nucleominer_2.3.46.tar.gz
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Comparison is based on a log likelihood ratio (LLR1). depending on the |
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LLR1 value nucleosomes are merged (low LLR) or separated (high LLR). |
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Finally the function returns a list of clusters and all computed |
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llr\_scores. Each cluster ows an attribute wp for “well positioned”.
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llr\_scores. Each cluster ows an attribute wp for "well positioned".
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This attribute is set to TRUE if the cluster is composed of exactly one |
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nucleosome of each sample. |
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|
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+---------------+---------------------------------------------------+ |
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| Author: | Florent Chuffart | |
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+---------------+---------------------------------------------------+ |
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| Version: | 2.3.45 |
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| Version: | 2.3.46 |
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+---------------+---------------------------------------------------+ |
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| License: | CeCILL | |
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+---------------+---------------------------------------------------+ |
| b/doc/sphinx_doc/tuto.rst | ||
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Working Directory Organisation |
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
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The working directory...
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After having install NucleoMiner2 environment (Previous section), go to the root working directory of the tutorial by typing the following command in a terminal:
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$$$ TODO: Explain how and where retrieve the workdir
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.. code:: bash
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cd doc/Chuffart_NM2_workdir/ |
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Retrieving Experimental Dataset |
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.. |
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.. |
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.. $$$ other python script to describe: |
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.. - libcoverage.py |
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.. - wf.py |
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.. |
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APPENDICE: Generate .c2c Files |
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------------------------------ |
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$$$ TODO make it works properly. |
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working directory. |
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The `.c2c` files is a simple table that describes how the genome sequence can be aligned. We generate it using NucleoMiner 1.0. |
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To install NucleoMiner 1.0 on your UNIX/LINUX computer you need first to install the Genetic Data analysis Library (GDL), which is a dynamic library of useful C functions derived from the GNU Scientific Library. |
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.. code:: bash |
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mkdir tmp_c2c_workdir |
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cd tmp_c2c_workdir |
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cp ../deps/gdl-1.0.tar.gz . |
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tar -xvzf gdl-1.0.tar.gz |
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cd gdl-1.0 |
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./configure |
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make |
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cd .. |
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Now you need to install the library on your system. This needs root priviledges: |
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sudo make install |
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Installing NucleoMiner |
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^^^^^^^^^^^^^^^^^^^^^^ |
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Installing NucleoMiner 1.0
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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Get the nucleominer-1.0.tar.gz archive on your computer. Copy it in a dedicated directory. Go into this directory using the cd command, and then unfold the archive by typing: |
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tar -xvzf nucleominer-1.0.tar.gz |
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This creates a directory called nucleominer-1.0. You now need to go into this directory and compile the library, by typing: |
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.. code:: bash |
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cp ../deps/nucleominer-1.0.tar.gz . |
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tar -xvzf nucleominer-1.0.tar.gz |
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cd nucleominer-1.0 |
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ln -s ../gdl-1.0/gdl |
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./configure |
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make |
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.. code:: bash |
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mkdir dir_4_c2c |
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NMgxcomp Data/BY_S288c/Sequence/Genome.fasta \
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Data/RM_11-1a/Sequence/Genome.fasta \
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NMgxcomp ../data/saccharomyces_cerevisiae_BY_S288c_chromosomes.fasta\
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../data/saccharomyces_cerevisiae_rm11-1a_1_supercontigs.fasta\
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dir_4_c2c/BY_RM 2>dir_4_c2c/BY_RM.log |
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After execution, the directory dir_4_c2c will hold the .c2c files. |
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After execution, the directory `dir_4_c2c` will hold the .c2c files. |
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| b/src/DESCRIPTION | ||
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Package: nucleominer |
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Maintainer: Florent Chuffart <florent.chuffart@ens-lyon.fr> |
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Author: Florent Chuffart |
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Version: 2.3.45
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Version: 2.3.46
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License: CeCILL |
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Title: nm |
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Depends: seqinr, plotrix, DESeq, cachecache |
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# get_content = function(# Get content from cached |
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# ### Acces to the cached content of a file via the global variable NM_CACHE. Load content in NM_CACHE if needed. |
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# obj, ##<< The object that we want its content. |
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# ... ##<< Parameters that will be passed to my_read |
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# ) {
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# UseMethod("get_content", obj)
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# ### Returns the cached content of the current object. |
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# } |
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# |
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# get_content.default = structure( function(# Get content from cached |
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# ### Acces to the cached content of a file via the global variable NM_CACHE. Load content in NM_CACHE if needed. |
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# obj, ##<< The object that we want its content. |
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# ... ##<< Parameters that will be passed to my_read |
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# ) {
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# if (inherits(try(NM_CACHE,TRUE), "try-error") || is.null(NM_CACHE)) {
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# NM_CACHE <<- list() |
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# } |
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# if (is.null(NM_CACHE[[obj$filename]])) {
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# print(paste("Loading file ",obj$filename, sep=""))
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# tmp_content = my_read(obj, ...) |
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# print("affect it...")
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# NM_CACHE[[obj$filename]] <<- tmp_content |
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# print("done.")
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# } |
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# return(NM_CACHE[[obj$filename]]) |
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# ### Returns the cached content of the current object. |
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# }, ex=function(){
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# # Create a dataframe |
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# df = NULL |
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# df = dfadd(df, list(key1 = "value1", key2 = "value2")) |
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# df = dfadd(df, list(key1 = "value1'", key2 = "value3'")) |
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# # Dump it into tmp file |
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# write.table(df, file="/tmp/tmp.dump.table.tmp") |
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# # Load it into cache using feature. |
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# cached_content_object = list(filename="/tmp/tmp.dump.table.tmp") |
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# class(cached_content_object) = "table" |
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# # First time it will be load into cache |
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# print(get_content(cached_content_object)) |
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# # Second time not |
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# print(get_content(cached_content_object)) |
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# |
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# }) |
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# |
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# my_read = function( |
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# ### Abstract my_read function. |
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# obj, ...) {
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# UseMethod("my_read", obj)
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# } |
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# |
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# my_read.default = function( |
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# ### Default my_read function. |
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# obj, ...){
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# stop(paste("ERROR, my_read is not defined for any Objects (file ", obj$filename," )", sep=""))
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# } |
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# |
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# my_read.fasta = function( |
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# ### my_read function for fasta files. |
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# obj, ...){
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# # require(seqinr) |
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# return(read.fasta(obj$filename, ...)) |
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# } |
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# |
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# my_read.table = function( |
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# ### my_read function for table files. |
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# obj, ...){
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# if (rev(unlist(strsplit(obj$filename, ".", fixed=TRUE)))[1] == "gz") {
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# return(read.table(file=gzfile(obj$filename), ...)) |
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# } else {
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# return(read.table(file=obj$filename, ...)) |
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# } |
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# } |
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# |
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# my_read.cvs = function( |
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# ### my_read function for cvs files. |
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# obj, ...){
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# return(read.csv(file=obj$filename, ...)) |
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# } |
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FDR = structure(function# False Discovery Rate |
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### From a vector x of independent p-values, extract the cutoff corresponding to the specified FDR. See Benjamini & Hochberg 1995 paper |
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##author<< Gael Yvert, |
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aggregate_intra_strain_nucs = structure(function(# Aggregate replicated sample's nucleosomes. |
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### This function aggregates nucleosomes from replicated samples. It uses TemplateFilter ouput of each sample as replicate. Each sample owns a set of nucleosomes computed using TemplateFilter and ordered by the position of their center (dyad). A chain of nucleosomes is builts across all replicates. Adjacent nucleosomes of the chain are compared two by two. Comparison is based on a log likelihood ratio (LLR1). depending on the LLR1 value nucleosomes are merged (low LLR) or separated (high LLR). Finally the function returns a list of clusters and all computed llr_scores. Each cluster ows an attribute wp for “well positioned”. This attribute is set to TRUE if the cluster is composed of exactly one nucleosome of each sample.
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### This function aggregates nucleosomes from replicated samples. It uses TemplateFilter ouput of each sample as replicate. Each sample owns a set of nucleosomes computed using TemplateFilter and ordered by the position of their center (dyad). A chain of nucleosomes is builts across all replicates. Adjacent nucleosomes of the chain are compared two by two. Comparison is based on a log likelihood ratio (LLR1). depending on the LLR1 value nucleosomes are merged (low LLR) or separated (high LLR). Finally the function returns a list of clusters and all computed llr_scores. Each cluster ows an attribute wp for "well positioned". This attribute is set to TRUE if the cluster is composed of exactly one nucleosome of each sample.
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samples, ##<< A list of samples. Each sample is a list like \emph{sample = list(id=..., marker=..., strain=..., roi=..., inputs=..., outputs=...)} with \emph{roi = list(name=..., begin=..., end=..., chr=..., genome=...)}.
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llr_thres=20, ##<< Log likelihood ratio threshold to decide between merging and separating |
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coord_max=20000000 ##<< A too big value to be a coord for a nucleosome lower bound. |
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# print(snep_design) |
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thres = FDR(reads$pvalsGLM, FDR) |
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reads$snep_index = reads$pvalsGLM < thres |
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print(paste(sum(reads$snep_index), " SNEPs found for ", length(reads[,1])," nucs and ", fdr*100,"% of FDR.", sep = ""))
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print(paste(sum(reads$snep_index), " SNEPs found for ", length(reads[,1])," nucs and ", FDR*100,"% of FDR.", sep = ""))
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return(reads) |
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}, ex=function(){
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marker = "H3K4me1" |
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if (length(nucs$center) > 0) {
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col = 1 |
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for (i in 1:length(nucs$center)) {
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foo<<-nucs |
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if (change_col) {
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col = col + 1 |
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} else {
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