Update README.md (#203) (#206)

* Update README.md

Added text regarding the scANANSE publication & links

---------

Co-authored-by: Jos Smits <J.Smits@science.ru.nl>

README.md

@@ -10,7 +10,7 @@
 [![Maintainability](https://api.codeclimate.com/v1/badges/875df8c40fec66d68b1f/maintainability)](https://codeclimate.com/github/vanheeringen-lab/ANANSE/maintainability)
 [![Test Coverage](https://api.codeclimate.com/v1/badges/875df8c40fec66d68b1f/test_coverage)](https://codeclimate.com/github/vanheeringen-lab/ANANSE/test_coverage)
 
-### Prediction of key transcription factors in cell fate determination using enhancer networks
+## Prediction of key transcription factors in cell fate determination using enhancer networks
 ANANSE is a computational approach to infer enhancer-based gene regulatory networks (GRNs) and to identify key transcription factors between two GRNs. You can use it to study transcription regulation during development and differentiation, or to generate a shortlist of transcription factors for trans-differentiation experiments. 
 
 ANANSE is written in Python and comes with a command-line interface that includes 3 main commands: `ananse binding`, `ananse network`, and `ananse influence`. A graphical overview of the tools is shown below.
@@ -27,17 +27,30 @@ Check out the **[ANANSE documentation](https://anansepy.readthedocs.io/en/master
 
 For documentation on the **development version** see [here](https://anansepy.readthedocs.io/en/develop/).
 
+
 ## Citation
 
 > ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination 
 > Quan Xu, Georgios Georgiou, Siebren Frölich, Maarten van der Sande, Gert Jan C Veenstra, Huiqing Zhou, Simon J van Heeringen
 > Nucleic Acids Research, gkab598, https://doi.org/10.1093/nar/gkab598
 
 
+## scANANSE: Gene regulatory network and motif analysis of single-cell clusters
+
+scANANSE is a pipeline developed for single-cell RNA-sequencing data and single-cell ATAC-sequencing data. It can export single-cell cluster data from both Seurat or Scanpy objects, and runs the clusters through ANANSE using a snakemake workflow to significantly simplify the process. Afterwards, results can be imported back into your single-cell object.
+
+For more info on this implementation check out the
+* [scANANSE workflow](https://doi.org/10.12688/f1000research.130530.1)
+* [Python package for Scanpy objects](https://github.com/Arts-of-coding/AnanseScanpy)
+* [R package for Seurat objects](https://github.com/JGASmits/AnanseSeurat)
+* [anansnake package for automating multiple ANANSE analyses](https://github.com/vanheeringen-lab/anansnake)
+
+
 ## Help and Support
 
 * The preferred way to get support is through the [Github issues page](https://github.com/vanheeringen-lab/ANANSE/issues).
 
+
 ## License
 
   - **[MIT license](http://opensource.org/licenses/mit-license.php)** [![Anaconda-Server Badge](https://anaconda.org/qxuchn/ananse/badges/license.svg)](https://anaconda.org/qxuchn/ananse)