Chore: Update README with more up-to-date information

README.md

@@ -1,24 +1,50 @@
 # MiCoNE - Microbial Co-occurrence Network Explorer
 
 ![Build Status](https://github.com/segrelab/MiCoNE/workflows/build/badge.svg)
+[![Documentation Status](https://readthedocs.org/projects/micone/badge/?version=latest)](https://micone.readthedocs.io/en/latest/?badge=latest)
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-`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.
+`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.
+
+<div align="center">
+⚠️ <p><strong>The package is under active development and breaking changes are possible</strong></p>
+</div>
 
 -   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
 
 -   Plug and play architecture: allows easy additions and removal of new tools
 -   Flexible and portable: allows running the pipeline on local machine, 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
+-   Parallelization: automatic parallelization both within and across 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:
+
+```sh
+pip install micone
+```
+
+Installing the `conda` package:
+
+```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
 
@@ -48,6 +74,67 @@ micone run -p local -c run.toml -m 4
 
 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
+
+```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
 
 This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.