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-.. _home-page-mopmod:
-
-*******************
-MOP_MOD
-*******************
-
-.. autosummary::
-   :toctree: generated
-
-This pipeline takes as input the output from MOP_PREPROCESS: basecalled fast5 reads, together with their respective fastq files and unspliced alignments to the transcriptome . It runs four different RNA detection algorithms (Epinano, Nanopolish, Tombo and Nanocompore) and it outputs the predictions generated by each one of them as individual tab-delimited files.
-
-.. image:: ../img/flow_mod.png
-  :width: 600
-  :alt: mop_mod graph
-
-Input Parameters
-======================
-
-The input parameters are stored in yaml files like the one represented here:
-
-.. literalinclude:: ../mop_mod/params.yaml
-   :language: yaml
-
-
-
-How to run the pipeline
-=============================
-
-Before launching the pipeline, user should:
-
-1. Decide which containers to use - either docker or singularity **[-with-docker / -with-singularity]**.
-2. Fill in both **params.yaml** and **tools_opt.tsv** files.
-3. Fill in **comparison.tsv** file - please see example below:
-
-.. code-block:: console
-
-   wt_1 ko_1
-   wt_2 ko_2
-
-
-To launch the pipeline, please use the following command:
-
-.. code-block:: console
-
-   nextflow run mop_mod.nf -params-file params.yaml -with-singularity > log.txt
-
-
-You can run the pipeline in the background adding the nextflow parameter **-bg**:
-
-.. code-block:: console
-
-   nextflow run mop_mod.nf -params-file params.yaml -with-singularity -bg > log.txt
-
-You can change the parameters either by changing **params.config** file or by feeding the parameters via command line:
-
-.. code-block:: console
-
-   nextflow run mop_mod.nf -params-file params.yaml -with-singularity -bg --output test2 > log.txt
-
-
-You can specify a different working directory with temporary files:
-
-.. code-block:: console
-
-   nextflow run mop_mod.nf -params-file params.yaml -with-singularity -bg -w /path/working_directory > log.txt
-
-
-Results
-====================
-
-Several folders are created by the pipeline within the output directory specified by the **output** parameter:
-
-1. **Epinano** results are stored in **epinano_flow** directory. It contains two files per sample: one containing data at position level and the other, at 5-mer level. Different features frequencies as well as quality data are included in the results. See example below:
-
-.. code-block:: console
-
-   #Ref,pos,base,cov,q_mean,q_median,q_std,mis,ins,del
-   gene_A,2515,C,45497.0,5.36995,4.00000,3.97797,0.0822032221904741,0.18715519704595907,0.2058377475437941
-   gene_A,2516,A,45504.0,5.38207,4.00000,4.71619,0.17128164556962025,0.20497099156118143,0.07733386075949367
-   gene_A,2517,C,45529.0,6.92130,5.00000,5.04250,0.06165301236574491,0.1505633771881658,0.13540820136616222
-   gene_A,2518,A,45545.0,6.49821,5.00000,5.47485,0.10802503018992206,0.10855198155670216,0.2082775277198375
-   gene_A,2519,T,45557.0,6.51247,5.00000,4.81853,0.09386043857145993,0.14792457800118533,0.2033057488421099
-
-Here an example of a plot from Epinano:
-
-.. image:: ../img/epinano.png
-  :width: 350
-
-
-Extracting modification encoded within BAM files (basecalling with modification)
-=====================================================================================
-
-Once the data has been basecalled with a modification-aware basecalling model, modification can be evaluated using modkit.
-
-To run this software, in the ``params.yaml`` file you should specify ``modkit: "YES"`` and run the code below:
-
-.. code-block:: console
-
-   cd mop_mod
-   nextflow run mop_mod.nf -params-file params.yaml -with-singularity -bg > yourlog.txt
-
-
-Modkit will generate a pileup file that can be filtered by keeping only the positions with a coverage higher than a value indicated in **tools_opt.tsv**.
-
-```
-modkit	filtering	">= 25"
-```
-
-The resulting bedgraphs are then merged in a single table called union.bed