Atac-seq is similar to ChIP-seq an epigenomics NGS technique to identify open chromatin. Since the sequencing is in a paired-end mode (instead of single-end like we had in the previous data set from the tutorial) other parameters are needed for the alignment with `bowtie2`.
*`-1` indicates the forward read
*`-1` indicates the forward read,
* while `-2` indicates the reverse read.
<br/>
`Genrich` has an advantage over `MACS2` since it is offering to do the necessary filtering steps during peak calling.
*`-e` allows to exclude chromosomes that are specified. In this case the mitochondrial chromosome (chrM).
*`-m` filters low quality reads.
*`-j` specifies that it has to run in the atac-seq mode.
*`-a` is the minimum AUC for a peak.
*`-r` removes PCR duplicates.
*`-k` creates a bedgraph-ish file for pileups.
<br/>
2. Write the script into a Snakefile.
* Create a new directory `atac-seq` in your home directory for this (see the code below)
```bash
$ cd
$ mkdir atac-seq
$ cd atac-seq
```
* The peak caller `Genrich` is available from the `bioconda` conda channel
3. How can you improve the workflow in the context of data management? Think about data structures and log files.
