Updated readme

## Introduction

**nf-core/cutandrun** is a best-practice bioinformatic analysis pipeline for CUT&Run and CUT&Tag experimental protocols that where developed to study protein-DNA interactions and epigenomic profiling.

**nf-core/cutandrun** is a best-practice bioinformatic analysis pipeline for CUT&RUN and CUT&Tag experimental protocols that were developed to study protein-DNA interactions and epigenomic profiling.

The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It is capable of using docker/singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules).

[CUT&RUN](https://elifesciences.org/articles/46314)

The pipeline has been developed with continuous integration (CI) in mind. nf-core code and module linting as well as a battery of over 100 unit and integration tests run on pull request to the main repository and on release of the pipeline. On official release, automated CI tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/cutandrun/results).

> Meers, M. P., Bryson, T. D., Henikoff, J. G., & Henikoff, S. (2019). Improved CUT&RUN chromatin profiling tools. *eLife*, *8*. https://doi.org/10.7554/eLife.46314

[CUT&Tag](https://www.nature.com/articles/s41467-019-09982-5)

> Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., Ahmad, K., & Henikoff, S. (2019). CUT&Tag for efficient epigenomic profiling of small samples and single cells. *Nature Communications*, *10*(1), 1930. https://doi.org/10.1038/s41467-019-09982-5]

The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a portable, reproducible manner. It is capable of using  containerisation and package management making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process, which makes it easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules).

The pipeline has been developed with continuous integration (CI) and test driven development (TDD) at its core. nf-core code and module linting as well as a battery of over 100 unit and integration tests run on pull request to the main repository and on release of the pipeline. On official release, automated CI tests run the pipeline on a full-sized dataset on AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/cutandrun/results).

![pipeline_diagram](docs/images/cutandrun-flow-diagram-v1-0_2.png)

7. Duplicate read marking ([`picard`](https://broadinstitute.github.io/picard/))

8. Create bedGraph files ([`bedtools`](https://github.com/arq5x/bedtools2/)

9. Create bigWig coverage files ([`bedGraphToBigWig`](http://hgdownload.soe.ucsc.edu/admin/exe/))

10. Peak calling specifically tailored for low background noise experiments ([`SEACR`](https://github.com/FredHutch/SEACR))

10. Peak calling ([`SEACR`](https://github.com/FredHutch/SEACR), [`MACS2`](https://github.com/macs3-project/MACS))

11. Consensus peak merging and reporting ([`bedtools`](https://github.com/arq5x/bedtools2/))

12. Quality control and analysis:

    1. Alignment, fragment length and peak analysis and replicate reproducibility ([`python`](https://www.python.org/))

The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the `process` name and override the Nextflow `container` definition for that process using the `withName` declaration. For example, in the [nf-core/viralrecon](https://nf-co.re/viralrecon) pipeline a tool called [Pangolin](https://github.com/cov-lineages/pangolin) has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn't make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via `-c custom.config`.

1. Check the default version used by the pipeline in the module file for [Pangolin](https://github.com/nf-core/viralrecon/blob/a85d5969f9025409e3618d6c280ef15ce417df65/modules/nf-core/software/pangolin/main.nf#L14-L19)

2. Find the latest version of the Biocontainer available on [Quay.io](https://quay.io/repository/biocontainers/pangolin?tag=latest&tab=tags)

3. Create the custom config accordingly:

   - For Docker: