https://github.com/biodataanalysisgroup/godel-numbering

Godel numbering

https://github.com/biodataanalysisgroup/godel-numbering

Repository

Godel numbering

https://github.com/BiodataAnalysisGroup/godel-numbering/blob/main/

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# Godel numbers and DNA

This material is made available under the [MIT license](https://opensource.org/licenses/MIT). Please see [LICENSE](LICENSE.md) for more details.

## Citation

- (_arXiv preprint_) Argyris Nicolaidis and Fotis Psomopoulos, "DNA coding and Gdel numbering", arXiv:1909.13574, 2019 (_[link](https://arxiv.org/abs/1909.13574)_)

## Structure
godel-numbering repository consists of four folders:
- `data`: This is where all the input files are stored.
- `R`: This is where all the R scripts are stored.
- `plots`: This is where all the output files are stored. This folder is created automatically while executing the project. There are also some examples of outputs in zip format.
- `lit`: some literature stuff


## Getting started
### Dependencies
Execute the following line to install the required packages:
- from CRAN:

```
install.packages(c("seqinr", "MASS", "ggplot2", "ggpubr", "stringr", "combinat" , "microseq"))
```

or run `install.R` script.

### Setting up
The project can be downloaded using git:
```
git clone https://github.com/BiodataAnalysisGroup/godel-numbering.git
```

### Extra files
- Download **S1_L001** and **S10_L001** samples from [E-MTAB-6962 - RNA-seq dataset](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6962/samples/), which correspond to ERR2681749_1.fastq.gz and ERR2681763_1.fastq.gz files respectively. 
- **Store both files in a single directoy.**.

### Running the project
The analysis is implemented in two scripts:

- ```Biological-Sequences-and-Godel-numbers.R```
  - **Specifying input**: Line 16. Specify `dataset_type` parameter depending on which dataset to run the analysis for. 
  - **Outputs**: Plots and statistical stuff array inside `plots` directoy.

- ```e-mtab-data.R```
  - **Specifying input**:
    1. Line 17. Specify the directory in your computer, in which [E-MTAB-6962 - RNA-seq dataset](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6962/samples/) files are stored.
    2. Line 26: Specify whether to run the analysis for S1_L001 or S10_L001 sample.
  - **Outputs**: Plots and statistical stuff array inside `plots` directoy.

In order to run the project:
1. Set the main project folder as your working directory.
2. Execute either `Biological-Sequences-and-Godel-numbers.R`or `e-mtab-data.R`, depending on which analysis you would like to run.

## Additional scripts
- `biom_data_150bp_to_fasta.R`: Converts `biom_data_150bp.rda` into .fasta file.

## Input
- Input files are stored inside `data` directory.

## Output
- Outputs are stored inside `plots` directory.

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