Chore: Update readme in the docs

docs/readme.rst

@@ -1,17 +1,34 @@
 MiCoNE - Microbial Co-occurrence Network Explorer
 =================================================
 
-|Build Status| |CodeFactor| |Updates| |Code style: black|
+|Build Status| |Documentation Status| |codecov| |CodeFactor| |Updates|
+|Code style: black|
 
 ``MiCoNE``, is a flexible and modular pipeline for 16S data analysis. It
 incorporates various popular, publicly available tools as well as custom
 Python modules and scripts to facilitate inference of co-occurrence
 networks from 16S data.
 
+.. container::
+
+   ⚠️
+
+   .. raw:: html
+
+      <p>
+
+   The package is under active development and breaking changes are
+   possible
+
+   .. raw:: html
+
+      </p>
+
 -  Free software: MIT license
--  Documentation: https://segrelab.github.io/MiCoNE.
+-  Documentation: https://micone.readthedocs.io/
 
-Manuscript in preparation.
+Manuscript can be found on
+`bioRxiv <https://www.biorxiv.org/content/10.1101/2020.09.23.309781v2>`__
 
 Features
 --------
@@ -22,16 +39,41 @@ Features
    compute cluster or the cloud with minimal configuration change. Uses
    the `nextflow <www.nextflow.io>`__ under the hood
 -  Parallelization: automatic parallelization both within and across
-   samples
--  Ease of use: available as ``conda`` package as well as a ``docker``
-   container
+   samples (needs to be enabled in the ``config`` file)
+-  Ease of use: available as a minimal ``Python`` library (without the
+   pipeline) or the full ``conda`` package
+
+Installation
+------------
+
+Installing the minimal ``Python`` library:
+
+.. code:: sh
+
+   pip install micone
+
+Installing the ``conda`` package:
+
+.. code:: sh
+
+   git clone https://github.com/segrelab/MiCoNE.git
+   cd MiCoNE
+   conda env create -n micone -f env.yml
+   pip install .
+
+..
+
+   NOTE: The ``conda`` package is currently being updated and will be
+   available soon.
 
 Workflow
 --------
 
-.. figure:: images/pipeline.png
+.. figure:: assets/pipeline.png
    :alt: pipeline
 
+   pipeline
+
 It supports the conversion of raw 16S sequence data or counts matrices
 into co-occurrence networks through multiple methods. Each process in
 the pipeline supports alternate tools for performing the same task,
@@ -47,10 +89,13 @@ subcommands you can type:
 
    micone --help
 
-Supported subcommands: 1. ``init`` - Creates ``conda`` environments for
-various pipeline processes 2. ``run`` - The main subcommand that runs
-the pipeline 3. ``clean`` - Cleans temporary data, log files and other
-extraneous files
+Supported subcommands:
+
+1. ``init`` - Creates ``conda`` environments for various pipeline
+   processes
+2. ``run`` - The main subcommand that runs the pipeline
+3. ``clean`` - Cleans temporary data, log files and other extraneous
+   files
 
 To run the pipeline:
 
@@ -62,6 +107,68 @@ This runs the pipeline in the ``local`` machine using ``run.toml`` for
 the pipeline configuration and with a maximum of 4 processes in parallel
 at a time.
 
+Configuration
+-------------
+
+The configuration of the pipeline can be done using a ``.toml`` file.
+The details can be found in the relevant section in the docs. Here is an
+example ``config`` file that performs:
+
+1. grouping of OTUs by taxonomy level
+2. correlation of the taxa using ``fastspar``
+3. calculates p-values
+4. constructs the networks
+
+.. code:: toml
+
+   title = "A example pipeline for testing"
+
+   order = """
+     otu_processing.filter.group
+     otu_processing.export.biom2tsv
+     network_inference.bootstrap.resample
+     network_inference.correlation.sparcc
+     network_inference.bootstrap.pvalue
+     network_inference.network.make_network
+   """
+
+   output_location = "/home/dileep/Documents/results/sparcc_network"
+
+   [otu_processing.filter.group]
+     [[otu_processing.filter.group.input]]
+       datatype = "otu_table"
+       format = ["biom"]
+       location = "correlations/good/deblur/deblur.biom"
+     [[otu_processing.filter.group.parameters]]
+       process = "group"
+       tax_levels = "['Family', 'Genus', 'Species']"
+
+   [otu_processing.export.biom2tsv]
+
+   [network_inference.bootstrap.resample]
+     [[network_inference.bootstrap.resample.parameters]]
+       process = "resample"
+       bootstraps = 10
+
+   [network_inference.correlation.sparcc]
+     [[network_inference.correlation.sparcc.parameters]]
+       process = "sparcc"
+       iterations = 5
+
+   [network_inference.bootstrap.pvalue]
+
+   [network_inference.network.make_network]
+     [[network_inference.network.make_network.input]]
+       datatype = "metadata"
+       format = ["json"]
+       location = "correlations/good/deblur/deblur_metadata.json"
+     [[network_inference.network.make_network.input]]
+       datatype = "computational_metadata"
+       format = ["json"]
+       location = "correlations/good/deblur/deblur_cmetadata.json"
+
+Other example ``config`` files can be found at ``tests/data/pipelines``
+
 Credits
 -------
 
@@ -70,8 +177,11 @@ This package was created with
 `audreyr/cookiecutter-pypackage <https://github.com/audreyr/cookiecutter-pypackage>`__
 project template.
 
-.. |Build Status| image:: https://travis-ci.com/dileep-kishore/micone.svg?token=qCMKydrUTvcJ87J6czex&branch=master
-   :target: https://travis-ci.com/dileep-kishore/micone
+.. |Build Status| image:: https://github.com/segrelab/MiCoNE/workflows/build/badge.svg
+.. |Documentation Status| image:: https://readthedocs.org/projects/micone/badge/?version=latest
+   :target: https://micone.readthedocs.io/en/latest/?badge=latest
+.. |codecov| image:: https://codecov.io/gh/segrelab/MiCoNE/branch/master/graph/badge.svg?token=2tKiI0lUJb
+   :target: https://codecov.io/gh/segrelab/MiCoNE
 .. |CodeFactor| image:: https://www.codefactor.io/repository/github/segrelab/micone/badge
    :target: https://www.codefactor.io/repository/github/segrelab/micone
 .. |Updates| image:: https://pyup.io/repos/github/segrelab/MiCoNE/shield.svg