LBMC » NucleoMiner

Tutorial

========

This tutorial describes steps allowing performing quantitative analysis of epigenetic marks on individual nucleosomes. We assume that files are organised according to a given hierarchy and that all command lines are launched from the project’s root directory.

This tutorial is divided into two main parts. The first part covers the python script `wf.py` that aligns and converts short sequence reads. The second part covers the R scripts that extracts information (nucleosome position and indicators) from the dataset.

Experimental Dataset, Working Directory and Configuration File 

--------------------------------------------------------------

Working Directory Organisation

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

After having install NucleoMiner2 environment (Previous section), go to the root working directory of the tutorial by typing the following command in a terminal:

.. code:: bash

  cd doc/Chuffart_NM2_workdir/

Retrieving Experimental Dataset

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The MNase-seq and MN-ChIP-seq raw data are available at ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) under accession number E-MTAB-2671.

$$$ TODO explain how organise Experimental Dataset into the `data` directory of the working directory.

We want to compare nucleosomes of 2 yeast strains: BY and RM. For each strain we performed Mnase-Seq and ChIP-Seq using an antibody recognizing the H3K14ac epigenetic mark.

The dataset is composed of 55 files organised as follows: 

  - 3 replicates for BY MNase Seq

    - sample 1 (5 fastq.gz files)

    - sample 2 (5 fastq.gz files)

    - sample 3 (4 fastq.gz files)

  - 3 replicates for RM MNase Seq

    - sample 4 (4 fastq.gz files)

    - sample 5 (4 fastq.gz files)

    - sample 6 (5 fastq.gz files)

  - 3 replicates for BY ChIP Seq H3K14ac

    - sample 36 (5 fastq.gz files)

    - sample 37 (5 fastq.gz files)

    - sample 53 (9 fastq.gz files)

  - 2 replicates for RM ChIP Seq H3K14ac

    - sample 38 (5 fastq.gz files)

    - sample 39 (4 fastq.gz files)

Python and R Common Configuration File

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

First of all we define in one place some configuration variables that will be launched by python and R scripts. These variables are contained in file `configurator.py`. The execution of this python script dumps variables into the `nucleominer_config.json` file that will then be used by both R and python scripts.

To do this, go to the root directory of your project and run the following command:

.. code:: bash

  python src/current/configurator.py

Preprocessing Illumina Fastq Reads for Each Sample

--------------------------------------------------

This preprocessing step consists of 4 main steps embedded in the `wf.py` script. They are described bellow. As a preamble, this script computes `samples`, `samples_mnase` and `strains` that will be used along the 4 steps.

.. autodata:: wf.samples

    :noindex: 

.. autodata:: wf.samples_mnase

    :noindex: 

.. autodata:: wf.strains

    :noindex: 

Creating Bowtie Index from each Reference Genome

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

For each strain, we need to create bowtie index. Bowtie index of a strain is a tree view of the genome of this strain. It will be used by bowtie to align reads. This step is performed by the following part of the `wf.py` script:

.. literalinclude:: ../../../snep/src/current/wf.py

   :start-after: # _STARTOF_ step_1

   :end-before: # _ENDOF_ step_1

   :language: python

The following table summarizes the file sizes and process durations concerning this step.

======  ======================  ======================  ================

strain  fasta genome file size  bowtie index file size  process duration

======  ======================  ======================  ================

BY      12 Mo                          25 Mo                    11 s.

RM      12 Mo                          24 Mo                    9 s.

======  ======================  ======================  ================

Aligning Reads to Reference Genome 

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Next, we launch bowtie to align reads to the reference genome. It produces a 

`.sam` file that we convert into a `.bed` file. Binaries for `bowtie`, `samtools` and `bedtools` are wrapped using python `subprocess` class. This step is performed by the following part of the `wf.py` script:

.. literalinclude:: ../../../snep/src/current/wf.py

   :start-after: # _STARTOF_ step_2

   :end-before: # _ENDOF_ step_2

   :language: python

Convert Aligned Reads into TemplateFilter Format

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

TemplateFilter uses particular input formats for reads, so it is necessary to convert the `.bed` files. TemplateFilter expect reads as follows: `chr`, `coord`, `strand` and `#read` where:

- `chr` is the number of the chromosome;

- `coord` is the coordinate of the reads;

- `strand` is `F` for forward and `R` for reverse;

- `#reads` the number of reads covering this position.

Each entry is *tab*-separated.

**WARNING** for reverse strands, bowtie returns the position of the first nucleotide on the left hand side, whereas TemplateFilter expects the first one on the right hand side.  This step takes this into account by adding the read length (in our case 50) to the reverse reads coordinates.

This step is performed by the following part of the `wf.py` script:

.. literalinclude:: ../../../snep/src/current/wf.py

   :start-after: # _STARTOF_ step_3

   :end-before: # _ENDOF_ step_3

   :language: python

The following table summarises the number of reads, the involved file sizes and process durations concerning the two last steps. In our case, alignment process have been multithreaded over 3 cores.

==  ==============  =========================  ======  ================  ==================  ================  

id  Illumina reads  aligned and filtred reads  ratio   `.bed` file size  TF input file size  process duration

==  ==============  =========================  ======  ================  ==================  ================

1   16436138        10199695                   62,06%  1064 Mo           60  Mo              383   s.

2   16911132        12512727                   73,99%  1298 Mo           64  Mo              437   s.

3   15946902        12340426                   77,38%  1280 Mo           65  Mo              423   s.

4   13765584        10381903                   75,42%  931  Mo           59  Mo              352   s.

5   15168268        11502855                   75,83%  1031 Mo           64  Mo              386   s.

6   18850820        14024905                   74,40%  1254 Mo           69  Mo              482   s.

36  17715118        14092985                   79,55%  1404 Mo           68  Mo              483   s.

37  17288466        7402082                    42,82%  741  Mo           48  Mo              339   s.

38  16116394        13178457                   81,77%  1101 Mo           63  Mo              420   s.

39  14241106        10537228                   73,99%  880  Mo           57  Mo              348   s.

53  40876476        33780065                   82,64%  3316 Mo           103 Mo              1165  s.

==  ==============  =========================  ======  ================  ==================  ================  

Run TemplateFilter on Mnase Samples

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Finally, for each sample we perform TemplateFilter analysis. 

**WARNING** TemplateFilter returns a list of nucleosomes. Each nucleosome is 

define by its center and its width. An odd width leads us to consider non-

integer lower and upper bound.

**WARNING** TemplateFilter is not designed to deal with replicates. So we recommend to keep a maximum of nucleosomes and filter the aberrant ones afterwards using the benefits of having replicates. To do this, we set a low correlation threshold parameter (0.5) and a particularly high value of overlap (300%).

This step is performed by the following part of the `wf.py` script:

.. literalinclude:: ../../../snep/src/current/wf.py

   :start-after: # _STARTOF_ step_4

   :end-before: # _ENDOF_ step_4

   :language: python

==  ======  ==========  =============  ================

id  strain  found nucs  nuc file size  process duration

==  ======  ==========  =============  ================

1    BY     96214       68 Mo          1022 s.                     

2    BY     91694       65 Mo          1038 s.                      

3    BY     91205       65 Mo          1036 s.                       

4    RM     88076       62 Mo          984 s.                      

5    RM     90141       64 Mo          967 s.                      

6    RM     87517       62 Mo          980 s.                      

==  ======  ==========  =============  ================

..

..

.. - libcoverage.py

.. - wf.py

..

..

..

..

..

..

.. In order to simplify the design of experiment, we consider Mnase as a marker.

.. For each couple `(strain, marker)` we perform 3 replicates. So, theoritically

.. we should have `3 * (1 + 5) * 3 = 54` samples. In practice we only obtain 2

.. replicates for `(YJM, H3K4me1)`. Each one of the 53 samples is indentify by a

.. uniq identifier. The file `CSV_SAMPLE_FILE` sums up this information.

..

.. .. autodata:: configurator.CSV_SAMPLE_FILE

..     :noindex:

..

.. We use a convention to link sample and Illumina fastq outputs. Illumina output

.. files of the sample `ID` will be stored in the directory

.. `ILLUMINA_OUTPUTFILE_PREFIX` + `ID`. For example, sample 41 outputs will be

.. stored in the directory `data/2012-09-05/FASTQ/Sample_Yvert_Bq41/`.

..

.. .. autodata:: configurator.ILLUMINA_OUTPUTFILE_PREFIX

..     :noindex:

..

.. For BY (resp. RM and YJM) we use following reference genome

.. `saccharomyces_cerevisiae_BY_S288c_chromosomes.fasta`

.. (resp. `saccharomyces_cerevisiae_rm11-1a_1_supercontigs.fasta` and

.. `saccharomyces_cerevisiae_YJM_789_screencontig.fasta`).

.. The index `FASTA_REFERENCE_GENOME_FILES` stores this information.

..

.. .. autodata:: configurator.FASTA_REFERENCE_GENOME_FILES

..     :noindex:

..

.. Each chromosome/contig is identify in the fasta file by an obscure identifier.

.. For example, BY chromosome I is identify by `gi|144228165|ref|NC_001133.7|` when

.. TemplateFilter is waiting for an integer. So, we translate it. The index

.. `FASTA_INDEXES` stores this translation.

..

.. .. autodata:: configurator.FASTA_INDEXES

..     :noindex:

..

.. From a pragamatical point of view we discard some part of the genome (repeated

.. sequence etc...). The list of the black listed area is explicitely detailled in

.. `AREA_BLACK_LIST`.

..

.. .. autodata:: configurator.AREA_BLACK_LIST

..     :noindex:

..

.. For BY-RM (resp. BY-YJM and RM-YJM) genome sequence alignment we use previously

.. compute .c2c file `data/2012-03_primarydata/BY_RM_gxcomp.c2c` (resp.

.. `BY_YJM_GComp_All.c2c` and `RM_YJM_gxcomp.c2c`). For more information about

.. .c2c files, please read section 5 of the manual of `NucleoMiner`, the old

.. version of `NucleoMiner2`

.. (http://www.ens-lyon.fr/LBMC/gisv/NucleoMiner_Manual/manual.pdf).

..

.. .. autodata:: configurator.C2C_FILES

..     :noindex:

..

.. `nucleominer` uses specific directory to work in, these are described in

.. `INDEX_DIR`, `ALIGN_DIR` and `LOG_DIR`.

..

.. Finally, `nucleominer` use external ressources, the path to these resspources

.. are describe in `BOWTIE_BUILD_BIN`, `BOWTIE2_BIN`, `SAMTOOLS_BIN`,

.. `BEDTOOLS_BIN` and `TF_BIN` and `TF_TEMPLATES_FILE`.

..

.. All paths, prefixes and indexes could be change in the

.. `src/current/nucleominer_config.json` file.

..

.. .. autodata:: wf.json_conf_file

..     :noindex:

..

Inferring Nucleosome Position and Extracting Read Counts

--------------------------------------------------------

The second part of the tutorial uses R (http://http://www.r-project.org). It consists of a set of R scripts that will be sourced in an R from a console launched at the root of your project. These scripts are:

  - headers.R

  - extract_maps.R

  - translate_common_wp.R

  - split_samples.R

  - count_reads.R

  - get_size_factors  

  - launch_deseq.R

The Script headers.R

^^^^^^^^^^^^^^^^^^^^

The script headers.R is included in each other scripts. It is in charge of: 

  - launching libraries used in the scripts

  - launching configuration (design, strain, marker...)

  - computing and caching CURs (caching means storing the information in the computer's memory)

Note that you can customize the function “translate”. This function allows you to use the alignments between genomes when performing various tasks. You may be using NucleoMiner2 to analyse data of a single strain, or of several strains. 

  - All the data corresponds to the same strain (e.g. treatment/control, or only few mutations): Then in step 1), the  regions to use are entire chromosomes. Instep 2) simply use the default translate function which is neutral.

  - The data come from two or more strains: In this case, edit a list of regions and customize the translate function which performs the correspondence between the different genomes. How we did it: a .c2c file is obtained with NucleoMiner 1.0 (refer to the Appendice "Generate .c2c Files"), then use it to produce the list of regions and customise “translate”.

In your R console, run the following command line:

.. code:: bash

  source("src/current/headers.R")

The Script extract_maps.R

^^^^^^^^^^^^^^^^^^^^^^^^^

This script is in charge of extracting Maps for well-positioned and fuzzy nucleosomes. First of all, this script computes intra and inter-strain nucleosome maps for each CUR. This step is executed in parallel on many cores using the BoT library. Next, it collects results and produces well-positioned, fuzzy and UNR maps.

The well-positioned map for BY is collected in the result directory and is called `BY_wp.tab`. It is composed of following columns:

 - chr, the number of the chromosome 

 - lower_bound, the lower bound of the nucleosome

 - upper_bound, the upper bound of the nucleosome 

 - cur_index, index of the CUR

 - index_nuc, the index of the nucleosome in the CUR

 - wp, 1 if it is a well positioned nucleosome, 0 otherwise

 - nb_reads, the number of reads that support this nucleosome

 - nb_nucs, the number of TemplateFilter nucleosome across replicates (= the number of replicates in which it is a well-positioned nucleosome)

 - llr_1, for a well-positioned nucleosome, it is the LLR1 (log-likelihood ratio) between the first and the second TemplateFilter nucleosome on the chain.

 - llr_2, for a well-positioned nucleosome, it is the LLR1 between the second and the third TemplateFilter nucleosome on the chain.

 - wp_llr, for a well-positioned nucleosome, it is the LLR2 that compares consistency of the positioning over all TemplateFilter nucleosomes.

 - wp_pval, for a well-positioned nucleosome, it is the p-value chi square test obtained with the LLR2 (`1-pchisq(2.LLR2, df=4)`)

 - dyad_shift, for a well-positioned nucleosome, it is the shift between the two extreme TemplateFilter nucleosome dyad positions. 

The fuzzy map for BY is collected in the result directory and is called `BY_fuzzy.tab`. It is composed of following columns:

 - chr, the number of the chromosome 

 - lower_bound, the lower bound of the nucleosome

 - upper_bound, the upper bound of the nucleosome 

 - cur_index, index of the CUR

The map of common well-positioned nucleosomes aligned between the BY and RM strains is collected in the result directory and is called `BY_RM_common_wp.tab`. It is composed of following columns:

 - cur_index, the index of the CUR

 - index_nuc_BY, the index of the BY nucleosome in the CUR

 - index_nuc_RM, the index of the RM nucleosome in the CUR

 - llr_score, , the LLR3 score that estimates conservation between the positions in BY and RM 

 - common_wp_pval,  the p-value chi square test obtained from LLR3 (`1-pchisq(2.LLR3, df=2)`)

 - diff, the dyads shift between the positions in the two strains

The common UNR map for BY and RM strains is collected in the result directory and is called `BY_RM_common_unr.tab`. It is composed of the following columns:

 - cur_index, the index of the CUR

 - index_nuc_BY, the index of the BY nucleosome in the CUR

 - index_nuc_RM,the index of the RM nucleosome in the CUR

To execute this script, run the following command in your R console:

.. code:: bash

  source("src/current/extract_maps.R")

The Script translate_common_wp.R

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This script is used to translate common well-positioned nucleosome maps from a strain to another strain and stores it into a table. 

For example, the file `results/2014-04/RM_wp_tr_2_BY.tab` contains RM well-positioned nucleosome translated into the BY genome coordinates. It is composed of following columns:

 - strain_ref, the reference genome (in which positioned are defined)

 - begin, the translated lower bound of the nucleosome

 - end, the translated upper bound of the nucleosome

 - chr, the number of chromosomes for the reference genome (in which positioned are defined)

 - length, the length of the nucleosome (could be negative)

 - cur_index, the index of the CUR

 - index_nuc, the index of the nucleosome in the CUR

To execute this script, run the following command in your R console:

.. code:: bash

  source("src/current/translate_common_wp.R")

The Script split_samples.R

^^^^^^^^^^^^^^^^^^^^^^^^^^

For memory space usage reasons, we split and compress TemplateFilter input files according to their corresponding  chromosome. for example, `sample_1_TF.tab` will be split into :

  - sample_1_chr_1_splited_sample.tab.gz

  - sample_1_chr_2_splited_sample.tab.gz

  - ...

  - sample_1_chr_17_splited_sample.tab.gz

To execute this script, run the following command in your R console:

.. code:: bash

  source("src/current/split_samples.R")

The Script count_reads.R

^^^^^^^^^^^^^^^^^^^^^^^^

To associate a number of observations (read) to each nucleosome we run the script `count_reads.R`. It produces the files `BY_RM_H3K14ac_wp_and_nbreads.tab`, `BY_RM_H3K14ac_unr_and_nbreads.tab` `BY_RM_Mnase_Seq_wp_and_nbreads.tab` and `BY_RM_Mnase_Seq_unr_and_nbreads.tab`  

for H3K14ac common well-positioned nucleosomes, H3K14ac UNRs, Mnase common well-positioned nucleosomes and Mnase UNRs respectively. 

For example, the file `BY_RM_H3K14ac_unr_and_nbreads.tab` contains counted reads for well-positioned nucleosomes with the experimental condition ChIP H3K14ac. It is composed of the following columns:

  - chr_BY, the number of the chromosome for BY

  - lower_bound_BY, the lower bound of the nucleosome for BY

  - upper_bound_BY, the upper bound of the nucleosome  for BY

  - index_nuc_BY, the index of the BY nucleosome in the CUR for BY

  - chr_RM, the number of the chromosome for RM

  - lower_bound_RM, the lower bound of the nucleosome for RM

  - upper_bound_RM, the upper bound of the nucleosome  for RM

  - index_nuc_RM,the index of the RM nucleosome in the CUR for RM

  - cur_index, index of the CUR

  - BY_H3K14ac_36, the number of reads for the current nucleosome for the sample 36

  - BY_H3K14ac_37, #reads for sample 37

  - BY_H3K14ac_53, #reads for sample 53

  - RM_H3K14ac_38, #reads for sample 38

  - RM_H3K14ac_39, #reads for sample 39

To execute this script, run the following command in your R console:

.. code:: bash

  source("src/current/count_reads.R")

The Script get_size_factors.R

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This script uses the DESeq function `estimateSizeFactors` to compute the size factor of each sample. It corresponds to normalisation of read counts from sample to sample, as determined by DESeq. When a sample has n reads for a nucleosome or a UNR,

the normalised count is n/f where f is the factor contained in this file.

The script dumps computed size factors into the file `size_factors.tab`. This file has the form:

========= ======= ======= =======

sample_id      wp     unr   wpunr

========= ======= ======= =======

        1 0.87396 0.88097 0.87584

        2 1.07890 1.07440 1.07760

        3 1.06400 1.05890 1.06250

        4 0.85782 0.87948 0.86305

        5 0.97577 0.96590 0.97307

        6 1.19630 1.18120 1.19190

       36 0.93318 0.92762 0.93166

       37 0.48315 0.48453 0.48350

       38 1.11240 1.11210 1.11230

       39 0.89897 0.89917 0.89903

       53 2.22650 2.22700 2.22660

========= ======= ======= =======

sample_id are given in file samples.csv

If you don't know which column to use, we recommend using wpunr.

If you want the very detailed factors produced by DESeq, here are the information:

  - unr: factor computed from data of UNR regions. These regions are defined for every pairs of aligned genomes (e.g. BY_RM)

  - wp: same, but for well-positioned nucleosomes.

  - wpunr: both types of regions.

To execute this script, run the following command in your R console:

.. code:: bash

  source("src/current/get_size_factors.R")

The Script launch_deseq.R

^^^^^^^^^^^^^^^^^^^^^^^^^

Finally, the script `launch_deseq.R` perform statistical analysis on each nucleosome using `DESeq`. It produces files:

  - results/current/BY_RM_H3K14ac_wp_snep.tab

  - results/current/BY_RM_H3K14ac_unr_snep.tab

  - results/current/BY_RM_H3K14ac_wpunr_snep.tab

  - results/current/BY_RM_H3K14ac_wp_mnase.tab

  - results/current/BY_RM_H3K14ac_unr_mnase.tab

  - results/current/BY_RM_H3K14ac_wpunr_mnase.tab

These files are organised with the following columns (see file `BY_RM_H3K14ac_wp_snep.tab` for an example):

  - chr_BY, the number of the chromosome for BY

  - lower_bound_BY, the lower bound of the nucleosome for BY

  - upper_bound_BY, the upper bound of the nucleosome  for BY

  - index_nuc_BY, the index of the BY nucleosome in the CUR for BY

  - chr_RM, the number of the chromosome for RM

  - lower_bound_RM, the lower bound of the nucleosome for RM

  - upper_bound_RM, the upper bound of the nucleosome  for RM

  - index_nuc_RM,the index of the RM nucleosome in the CUR for RM

  - cur_index, index of the CUR

  - form 

  - BY_Mnase_Seq_1, the number of reads for the current nucleosome for the sample 1  

Next columns concern indicators for each sample:

  - BY_Mnase_Seq_2, #reads for sample 2

  - BY_Mnase_Seq_3, #reads for sample 3  

  - RM_Mnase_Seq_4, #reads for sample 4  

  - RM_Mnase_Seq_5, #reads for sample 5

  - RM_Mnase_Seq_6, #reads for sample 6

  - BY_H3K14ac_36, #reads for sample 36

  - BY_H3K14ac_37, #reads for sample 37

  - BY_H3K14ac_53, #reads for sample 53

  - RM_H3K14ac_38, #reads for sample 38

  - RM_H3K14ac_39, #reads for sample 39

The 5 last columns concern DESeq analysis:

  - manip[a_manip] strain[a_strain] manip[a_strain]:strain[a_strain], the manip (marker) effect, the strain effect and the snep effect. These are the coefficients of the fitted generalized linear model.

  - pvalsGLM, the pvalue resulting of the comparison of the GLM model considering or not the interaction term marker:strain. This is the statsitcial significance of the interaction term and therefore the statistical significance of the SNEP.

  - snep_index, a boolean set to TRUE if the pvalueGLM value is under the threshold computed with FDR function with a rate set to 0.0001.

To execute this script, run the following command 

To execute this script, run the following command in your R console:

.. code:: bash

  source("src/current/launch_deseq.R")

Results: Number of SNEPs

------------------------

Here are the number of computed SNEPs for each forms.

===== ======= ===== =======

 form strains #nucs H3K14ac

===== ======= ===== =======

   wp   BY-RM 30464    3549

  unr   BY-RM  9497    1559

wpunr   BY-RM 39961    5240

===== ======= ===== =======

APPENDICE: Generate .c2c Files

------------------------------

$$$ TODO make it works properly.

working directory.

The `.c2c` files is a simple table that describes how the genome sequence can be aligned. We generate it using NucleoMiner 1.0.

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.

Installing the GDL library

^^^^^^^^^^^^^^^^^^^^^^^^^^

Get the gdl-1.0.tar.gz archive on your computer (in the directory deps of your working directory). Copy it in a dedicated directory. Go into this directory using the cd command, and then unfold the archive by typing:

tar -xvzf gdl-1.0.tar.gz

This creates a directory called gdl-1.0. You now need to go into this directory and compile the library, by typing:

.. code:: bash

  mkdir tmp_c2c_workdir

  cd tmp_c2c_workdir

  cp ../deps/gdl-1.0.tar.gz .

  tar -xvzf gdl-1.0.tar.gz

  cd gdl-1.0

  ./configure

  make

  cd ..

Now you need to install the library on your system. This needs root priviledges:

.. code:: bash

  sudo make install

Installing NucleoMiner 1.0

^^^^^^^^^^^^^^^^^^^^^^^^^^

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:

This creates a directory called nucleominer-1.0. You now need to go into this directory and compile the library, by typing:

.. code:: bash

  cp ../deps/nucleominer-1.0.tar.gz .

  tar -xvzf nucleominer-1.0.tar.gz

  cd nucleominer-1.0

  ln -s ../gdl-1.0/gdl

  ./configure

  make

You can then use the binaries dircetly from this folder (best then is to add the path to this folder in your PATH environment variable). If you want to install nucleominer at the system's level (useful if mutiple users will need it) then type, with root priviledges:

.. code:: bash

  sudo make install

Generate .c2c Files

^^^^^^^^^^^^^^^^^^^

To generate .c2c files you need to type the following command in a terminal:

.. code:: bash

  mkdir dir_4_c2c

  NMgxcomp ../data/saccharomyces_cerevisiae_BY_S288c_chromosomes.fasta\

           ../data/saccharomyces_cerevisiae_rm11-1a_1_supercontigs.fasta\

           dir_4_c2c/BY_RM 2>dir_4_c2c/BY_RM.log

After execution, the directory `dir_4_c2c` will hold the .c2c files.