diff --git a/README.md b/README.md
index 890678483a4812d31e37f1ac1feab0ebd6cef65b..60a30b30560843b9da92a173f14da1022c1af911 100644
--- a/README.md
+++ b/README.md
@@ -1,18 +1,23 @@
 # SSN
 
+In this repository, you can find the scripts needed to generate a Sequence Similarity Network starting from a curated sequence alignment, as described in [this chapter](https://doi.org/10.5281/zenodo.15228784).
+
+Our recommendation is to use the [Google Colaboratory version](#colab-notebook) below, since it automates the setup and execution.
+
+Otherwise, follow these local setup instructions.
+
 
 ## Initial Setup
 
-Clone this repository either via git CLI `git clone https://gitlab.tugraz.at/D5B8E35025578B91/ssn.git` or by manual download.
+Clone this repository either via git CLI `git clone https://gitlab.tugraz.at/bioc/ssn.git` or by manual download.
 Check the input file format and then follow the instructions for the chosen analysis method.
 
 
 ## Input File
 
-
 ### Preliminary steps
 
-As described in the [chapter](https://www.youtube.com/watch?v=dQw4w9WgXcQ), the BLAST database can be built with:
+The BLAST database can be built with:
 ```bash
     makeblastdb -in FILE.fasta -dbtype prot -title TITLE -parse_seqids -out DATABASE
 ```
@@ -20,12 +25,11 @@ The all-vs-all BLAST is performed with:
 ```bash
     blastp -db DATABASE -query FILE.fasta -out FILE.tsv -outfmt "6 qseqid sseqid evalue bitscore"
 ```
-FILE.tsv is the output file used in the subsequent analysis to geenrate the SSN.
+FILE.tsv is the output file used in the subsequent analysis to generate the SSN.
 
 
 ## Analysis Scripts
 
-
 ### AWK
 
 The AWK script is meant to be operated from a GNU/Linux system shell.
@@ -36,7 +40,6 @@ It must be run as:
 ```
 where the FILE.tsv is the input file, formatted as indicated in the **Input File** section, and the BITSCORES.csv and EVALUES.csv files containing the respective scores.
 
-
 ### Python
 
 The python script requires a minimal python data analysis setup, with the pandas library to be installed via `pip install pandas -y` in your working environment.
@@ -48,7 +51,8 @@ The analysis can then be launched from any shell as:
 where the FILE.tsv is the input file, formatted as indicated in the **Input File** section, and the BITSCORES.csv and EVALUES.csv files containing the respective scores.
 
 
-### Colab Notebook
+## Colab Notebook
 
 The colab notebook version requires no local setup and can be run from any browser (an already set up version requiring no login can be found [here](https://colab.research.google.com/drive/1RQssmD8X7ZOGaxOYDUYA5kpxmDIGYmkx)).
-Upon visiting the [google colab website](https://colab.research.google.com/), log in, upload the IPYNB file and run it, following the instructions.
+
+If that version does not work, download the IPYNB file from this repository, log into the [google colab website](https://colab.research.google.com/), upload the file and follow the instructions.