@@ -5,7 +5,7 @@ PAQR is a tool that allows the quantification of transcript 3' ends (or poly(A)
The method is actually a combination of scripts that depend on each other. To ease the execution of the scripts they have been assembled into a pipeline. The use PAQR, you simply have to run the pipeline.
## Requirements
Depending on how deep your original material was sequenced (i.e. how big your input files are in terms of the memory usage), your computer might require **50 GB of available memory (RAM)** to process these files. [Snakemake](https://snakemake.readthedocs.io/en/stable/) is used to facilitate the automated execution of all analysis steps. The easiest way to make use of the pipeline is to set up a python3 and a python2 virtual environment and run the pipeline in the python3 environment. Instructions on how to install the virtual environments for the analysis are given in the README of the main directory of this repository.
For normal samples (e.g. the samples that are used as test cases here) the pipeline will run on a laptop or stationary working machine with at least 10 GB of memory (RAM). Be aware though that the memory consumptions depends on the sequencing depth of your samples (i.e. how large your input files are). In rare cases, we found a peak memory usage of around 50 GB for very deeply sequenced samples (more than 400 million reads). [Snakemake](https://snakemake.readthedocs.io/en/stable/) is used to facilitate the automated execution of all analysis steps. The easiest way to make use of the pipeline is to set up a python3 and a python2 virtual environment and run the pipeline in the python3 environment. Instructions on how to install the virtual environments for the analysis are given in the README of the main directory of this repository.
### snakemake
[Snakemake](https://snakemake.readthedocs.io/en/stable/) is a workflow management system that helps to create and execute data processing pipelines. It is already part of the virtual environment which was introduced in the introduction README.
