Added tutorial index to README.

README.md

@@ -1,5 +1,5 @@
 # Dynamic Network Analysis - Tutorial
-A tutorial for the application of Dynamical Network Analysis using Generalized Correlations.
+A series of tutorials for the application of Dynamical Network Analysis using Generalized Correlations.
 
 The python module repository can be found at https://github.com/melomcr/dynetan
 
@@ -9,14 +9,64 @@ This tutorial was prepared and released along with the publication:
 
 * [Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories. J. Chem. Phys. (2020). DOI: 10.1063/5.0018980](https://doi.org/10.1063/5.0018980)
 
+## Tutorial index
+
+**Long-form tutorial introducing the method and showcasing extensive analysis**
+
+This tutorial will introduce the researcher to the fundamentals of Dynamical Network
+Analysis and to the entire range of functionalities provided by this python module.
+The system used as an example is a single protein enzyme bound to its substrate, 
+and its simulations are used as a starting point for correlation calculation, 
+community analysis, optimal path determination, comparisons between cartesian and 
+network distances, and finally interactive visualization.
+The first file is a python notebook that explains the method as it processes the 
+trajectory. The second file is a jupyter notebook that exemplifies a pipeline for 
+interactive data analysis.
+The tutorial concludes with the creation of files for VMD visualization and 
+rendering of publication-quality images.
+
+* Tutorial-Step_1-ProcessTrajectory.ipynb
+* Tutorial-Step_2-AnalysisAndPlots.ipynb
+
+**Command-Line-Interface version of long-form tutorial**
+
+These files are adaptations of the notebooks in the tutorial above which were 
+prepared to facilitate the remote execution of trajectory processing and analysis.
+
+* Tutorial-Command-Line-Interface-Step_1.py
+* Tutorial-Command-Line-Interface-Step_2.py
+
+**Single-protein analysis**
+
+This tutorial will cover the common case of a single protein being simulated for
+community analysis and network visualization.
+The first file is a python script that can be adapted to run in a remote cluster
+through a command line interface (CLI). The second file is a jupyter notebook that
+serves as a starting point for interactive data analysis. The tutorial concludes
+with the creation of files for VMD visualization and rendering of publication-quality 
+images.
+
+* Tutorial-Single-Protein-CLI-Step_1.py
+* Tutorial-Single-Protein-Step_2.ipynb
+
+**Non-canonical residue and ligands**
+
+This tutorial shows how a researcher can apply the Dynamical Network Analysis 
+technique to a system containing non-canonical protein residues, lipids,
+carbohydrates, and ligands such as drugs. It shows how interactive visualizations 
+and specialized module functions can be used to prepare a network representation 
+of complex ligands. 
+
+* Tutorial-Non-Canonical-and-Non-Proteic-Residues.ipynb
+
 ## Installing Requirements
 
-There are two main ways to instll all dependencies to execute this tutorial: The first is by creating a Conda environment, the second is by manually installing system packages, and Python and R packages. We provide examples for both method below.
+There are two main ways to install all dependencies to execute this tutorial: The first is by creating a Conda environment, the second is by manually installing system packages, and Python and R packages. We provide examples for both method below.
 
 Building a Conda Environment
 ----------------
 
-The easyest way to install all requirements for the execution of this tutorial is to create a Conda environment. Inside the folder *CondaEnv* you will find a recipe for an environment with all required Python and R packages. 
+The easiest way to install all requirements for the execution of this tutorial is to create a Conda environment. Inside the folder *CondaEnv* you will find a recipe for an environment with all required Python and R packages. 
 To create the environment using a terminal (a command line interface), simply go to the *CondaEnv* folder and run the following command:
 
     conda env create -f environment.yml