Update nextflow-workflow.rst

docs/source/nextflow-workflow.rst

@@ -135,6 +135,163 @@ Example ``sample_sheet.csv``
     +-------------------------------------------------+------------------------------------------------------+---------------------------------------------------------------------+
 
 
+Quick start
+###########
+The following is a condensed summary of steps required to get Autometa installed, configured and running. There are links throughout to the appropriate documentation sections that can provide more detail if required.
+
+Installation
+************
+For full installation instructions, please see the :ref:`Installation` section
+
+Installation relies on Miniconda being installed on your system (`<https://docs.conda.io/en/latest/miniconda.html>`_).
+To install the conda environment run the following code:
+
+.. code-block:: bash
+
+    conda env create --file=https://raw.githubusercontent.com/KwanLab/Autometa/main/nextflow-env.yml
+
+Next, activate the environment:
+
+.. code-block:: bash
+
+    conda activate autometa-nf
+
+Configuring a scheduler
+***********************
+For full details on how to configure your scheduler, please see the :ref:`Configuring your process executor` section.
+
+If you are using a Slurm scheduler, you will need to create a configuration file. If you do not have a scheduler, skip ahead to "Running Autometa".
+
+First you will need to know the name of your slurm partition. Run :code:`sinfo` to find this. In the example below, the partition name is "queue".
+
+.. image:: ../img/slurm_partitions.png
+
+Next, generate a new file called slurm_nextflow.config via nano:
+
+.. code-block:: bash
+
+    nano slurm_nextflow.config
+
+Then copy the following code block into that new file:
+
+.. code-block:: bash
+
+    profiles {
+            slurm {
+                process.executor       = "slurm"
+                process.queue = "queue" // <<-- change this to whatever your partition is called
+                // queue is the slurm partition to use in our case
+                docker.enabled         = true
+                docker.userEmulation   = true
+                singularity.enabled    = false
+                podman.enabled         = false
+                shifter.enabled        = false
+                charliecloud.enabled   = false
+                executor {
+                    queueSize = 8
+                }
+            }
+        }
+
+Keep this file somewhere central to you. For the sake of this example I will be keeping it in a folder called "Useful scripts" in my home directory e.g. :code:`/home/me/Useful_scripts/slurm_nextflow.config`
+
+Save your new file with Ctrl+O and then exit nano with Ctrl+O. Installation and set up is now complete. 🎉 🥳 
+
+Running Autometa
+****************
+
+For a comprehensive list of features and options and how to use them please see :ref:`Running the pipeline`
+
+Autometa can bin one or several metagenomic datasets in one run. Regardless of the number of metagenomes you
+want to process, you will need to provide a sample sheet which specifies the name of your sample, the full path to 
+where that data is found. If the metagenome was assembled via SPAdes, Autometa can extract coverage and 
+contig length information from the sequence headers. If you used a different assembler you will need to provide either raw reads for coverage calculation or a table of contig/scaffold coverage. Full details for data preparation may be found under :ref:`sample-sheet-preparation` 
+
+First ensure that your Autometa conda environment is activated. You can activate your environment by running:
+
+.. code-block:: bash
+
+    conda activate autometa-nf
+
+Navigate to where your metagenome files are and run the following code to launch Autometa:
+
+.. code-block:: bash
+
+    nf-core launch KwanLab/Autometa
+
+You will now use the arrow keys to move up and down between your options and hit your "Enter" or "Return" key to make your choice.
+
+Menu 1:
+The double, right-handed arrows should already indicate "2.0.0". This is the latest version of Autometa, hit your "Enter" to chose this option.
+
+.. image:: ../img/Menu1.png
+
+Menu 2:
+Pick the commandline option.
+
+.. note::
+
+    Unless you've tunneled into your server, or are using Autometa locally, this is your best option.
+
+.. image:: ../img/Menu2.png
+
+Menu 3:
+If you are using a scheduler (Slurm in this example) the only thing you'll need to change is the profile option. If you are not using a scheduler, leave this blank.
+
+.. image:: ../img/Menu3.png
+
+Menu 4:
+Now we need to give Autometa the full path to where to find our sample sheet, where to put the output (it will generate a folder per metagenome in this folder) and where to store all the log files (aka the trace files)
+
+.. image:: ../img/Menu4.png
+
+Menu 5:
+This is where you can change your binning parameters. If you're not sure what you're doing I would recommend only changing the "length_cutoff". 
+The default cutoff is 3000bp, which means that any contigs/scaffolds smaller than 3000bp will not be considered for binning. This cutoff will depend on how good your assembly is: e.g. if your N50 is 1200 bp, I would choose a cutoff of 1000. If your N50 is more along the lines of 5000, I would leave the cutoff at the default 3000. I would strongly recommend against choosing a number below 900 here. In the example below, I have chosen a cutoff of 1000bp as my assembly was not particularly great
+
+.. image:: ../img/Menu5.png
+
+Menu 6:
+Here you have a choice to make:
+By enabling taxonomy aware mode, Autometa will attempt to use taxonomic data to make your bins more accurate. However, this is a more computationally expensive step and will make the process take longer. By leaving this option as the default "False" option, Autometa will bin according to coverage and kmer patterns.
+
+With either choice, you need to provide a path to where the necessary databases are stored in the "single_db_dir" 
+option. Here, I have enabled the taxonomy aware mode and provided the path to where the databases are stored. 
+See :ref:`Databases` for additional details on required databases.
+
+.. image:: ../img/Menu6.png
+
+Menu 7:
+This will depend on the computational resources you have available. You could start with the default values and see 
+how the binning goes. If you have particularly complex datasets you may want to bump this up a bit. For your 
+average metagenome, you won't need more than 150Gb of memory. I've opted to use 75 Gb as a 
+starting point for a few biocrust (somewhat diverse) metagenomes. 
+
+
+.. note::
+
+    For TB worth of assembled data you may want to try :ref:`autometa-bash-workflow` using the `autometa-large-data-mode.sh <https://github.com/KwanLab/Autometa/blob/main/workflows/autometa-large-data-mode.sh>`_ template
+
+.. image:: ../img/Menu7.png
+
+Last few options:
+You will now be presented with a choice. If you are NOT using a scheduler, you can go ahead and type "y" to launch the workflow. If you are using a scheduler, type "n" - we have one more step to go. In the example below, I am using a scheduler so I have typed "n" to prevent immediately performing the nextflow run command.
+
+.. image:: ../img/launch_choice.png
+
+If you recall, we created a file called :code:`slurm_nextflow.config` that contains the information Autometa will need to talk to the Slurm scheduler. We need to include that file using the :code:`-c` flag (or configuration flag). Therefore to launch the Autometa workflow I would run the following command (you would change the :code:`/home/sam/slurm_nextflow.config` file path to what is appropriate for your system):
+
+.. code-block:: bash
+
+    nextflow run KwanLab/Autometa -r 2.0.0 -profile "slurm" -params-file "nf-params.json" -c "/home/sam/slurm_nextflow.config"
+
+Once you have hit the "Enter" key to submit the command, a menu will pop up and let you know the progress of your binning run, such as the one below:
+
+.. image:: ../img/progress.png
+
+When its complete, the output will be stored in your designated output folder (See Menu 4).
+
+
 Basic
 #####