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
## 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.
Check the input file format and then follow the instructions for the chosen analysis method.
## Input File
## Input File
### Preliminary steps
### 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
```bash
makeblastdb -in FILE.fasta -dbtype prot -title TITLE -parse_seqids-out DATABASE
makeblastdb -in FILE.fasta -dbtype prot -title TITLE -parse_seqids-out DATABASE
```
```
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@@ -20,12 +25,11 @@ The all-vs-all BLAST is performed with:
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@@ -20,12 +25,11 @@ The all-vs-all BLAST is performed with:
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
## Analysis Scripts
### AWK
### AWK
The AWK script is meant to be operated from a GNU/Linux system shell.
The AWK script is meant to be operated from a GNU/Linux system shell.
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@@ -36,7 +40,6 @@ It must be run as:
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@@ -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.
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
### 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.
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.
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@@ -48,7 +51,8 @@ The analysis can then be launched from any shell as:
...
@@ -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.
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)).
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.
